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Investigating the impact of extended-release tacrolimus on adherence and graft outcomes in pediatric… Lang, Samantha 2021

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  INVESTIGATING THE IMPACT OF EXTENDED-RELEASE TACROLIMUS ON ADHERENCE AND GRAFT OUTCOMES IN PEDIATRIC KIDNEY TRANSPLANT RECIPIENTS  by  Samantha Lang   BSc, King’s College London, 2012 MBBS, King’s College London, 2015      A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF     MASTER OF SCIENCE  in  The Faculty of Graduate and Postdoctoral Studies   (Experimental Medicine)   THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)   April 2021      ã Samantha Lang 2021      ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:  Investigating the impact of extended-release tacrolimus on adherence and graft outcomes in pediatric kidney transplant recipients  submitted by Samantha Lang  in partial fulfillment of the requirements for the degree of Master of Science  in  Experimental Medicine   Examining Committee: Dr. Tom Blydt-Hansen, Pediatric Nephrology, UBC  Supervisor  Dr. James Lan, Nephrology, UBC Supervisory Committee Member  Dr. John Gill, Nephrology, UBC Supervisory Committee Member       Dr. Joseph Kim, Nephrology, UofT Additional Examiner    Additional Supervisory Committee Members: Dr. Jagbir Gill, Nephrology, UBC Supervisory Committee Member           iii ABSTRACT  Improvements seen in short-term kidney transplant survival over the preceding three decades have not been reflected in long-term graft outcomes. This is particularly pertinent in the pediatric population who experience high rates of graft failure, a large proportion of which are attributed to non-adherence. Extended-release tacrolimus (ER-Tac), taken once daily, is associated with improved adherence in adults but this has not been extensively studied in pediatric kidney transplant recipients.   This study assessed 1)  the clinical factors that influence conversion to ER-Tac 2) whether conversion to ER-Tac is associated with improved adherence and 3) whether conversion to ER-Tac is associated with improved allograft function and rejection outcomes.   The first analysis showed that older age and female sex predicted conversion to ER-Tac.  Adherence measures (medication adherence measure (MAM-MM) and tacrolimus trough variability (Tac CV%)), individual barriers to adherence, renal function, and rejection were not significant predictors of conversion.  In the second analysis, we found that baseline adherence in this population was high and that ER-Tac was not subsequently associated with improved Tac CV% or self-reported adherence. Children were more likely to miss their morning medication and listed forgetfulness or schedule clashes as their most common reason to be non-adherent. Likewise, in the third analysis, ER-Tac was not superior to IR-Tac with regards to preventing rejection, decline in eGFR or graft loss.    iv Older age and female sex have been associated in other studies with poorer allograft outcomes and perhaps act as a high-level risk assessment for conversion to ER-Tac based on perception of risk. We did not find that age or sex were strongly associated with adherence or outcomes in multivariable analyses in this study. The lack of association between ER-Tac and adherence may be explained by a high baseline adherence in this population and because patients were not selected for conversion based on adherence behaviour. The finding of stable late graft outcomes between IR-Tac and ER-Tac remains important, especially given patient preference for ER-Tac regarding convenience and quality of life reported in other studies.    v LAY SUMMARY Kidney Transplant is a life-saving procedure for children with end stage kidney disease. After transplant, patients commence lifelong medication that must be taken on a strict schedule. This can be especially difficult for teenagers, children who take responsibility for their medication, and those with difficulties at home. Reducing a key medication (Tacrolimus) from twice (IR-Tac) to once daily (ER-Tac) has been shown to improve adults’ ability to take this medication reliably but has not been well studied in children. Taking medication properly is associated with better transplant outcomes.   In this study we investigated 1) which patient characteristics predicted whether someone would be switched to ER-Tac, and whether switching was more common in those who missed medications 2) whether switching from IR-Tac to ER-Tac improved how well children take this medication 3) whether switching improved the outcomes after transplant such as kidney function and transplant survival. vi PREFACE  The work presented herein was part of an ongoing national multi-centre cohort study. This study was designed by principal investigator Dr. Tom Blydt-Hansen and approved by the research ethics board of The University of Manitoba (REB# H2012:302) as well as participating research sites. The University of British Columbia research ethics board provided approval for the local participating site (REB# H14-02223).  Dr. Atul Sharma, Dr. Beth Foster, Dr. Ian Gibson, Dr. Julie Ho, Dr. Peter Nickerson and Dr. David Wishart were collaborators and assisted in the conception and early design of this multi-centre study.   A version of the material presented in chapter 3 was published as:  Lang S, Sharma A, Foster B, et al. Age and sex determine conversion from immediate‐release to extended‐release tacrolimus in a multi‐center cohort of Canadian pediatric renal transplant recipients. Pediatric Transplantation. 2020:e13959.   Additionally, the material presented in chapter 3 was also presented orally at the Canadian Society of Transplantation National Meeting.   Statistical analyses were planned and performed by Samantha Lang under the supervision of Dr. Blydt-Hansen and statistician Dr. Atul Sharma. Samantha Lang and Mike Guron (research assistant within the Blydt-Hansen lab) were responsible for data cleaning. The manuscript was drafted by Samantha Lang, and all authors (collaborators as listed above) provided edits and approved the final draft of the manuscript.    vii For material presented in chapters 4 and 5, the relative contributions are as stated above for chapter 3, with the exception of edits by collaborators as these chapters have not yet been reviewed by collaborators nor submitted for publication.            viii TABLE OF CONTENTS ABSTRACT ................................................................................................................................... iii LAY SUMMARY ............................................................................................................................. v PREFACE ...................................................................................................................................... vi TABLE OF CONTENTS ............................................................................................................. viii LIST OF TABLES .......................................................................................................................... x LIST OF FIGURES ...................................................................................................................... xi LIST OF ABBREVIATIONS ...................................................................................................... xii ACKNOWLEDGEMENTS ......................................................................................................... xvi Chapter 1. Introduction ............................................................................................................ 1 1.1 The Role of the Kidney .............................................................................................................. 1 1.2 Chronic Kidney Disease ............................................................................................................. 1 1.2.1 Definition ............................................................................................................................................... 1 1.2.2 Epidemiology ......................................................................................................................................... 2 1.2.3 Diagnosis and Clinical Features ............................................................................................................. 4 1.2.4 Renal Replacement Therapy .................................................................................................................. 5 1.3 Kidney Transplantation ............................................................................................................. 6 1.3.1 History .................................................................................................................................................... 6 1.3.2 Organ Donation and Allocation ............................................................................................................. 7 1.3.3 Factors that influence Transplant Outcome ........................................................................................... 8 1.3.4 Challenges Specific to Pediatric Kidney Transplantation .................................................................... 14 1.3.5 Sequelae of Kidney Transplantation .................................................................................................... 16 1.4 Tacrolimus ................................................................................................................................ 22 1.4.1 Pharmacodynamics ............................................................................................................................... 22 1.4.2 Pharmacokinetics ................................................................................................................................. 22 1.4.3 Tolerability and Toxicity ...................................................................................................................... 23 1.4.4 Extended-Release Tacrolimus .............................................................................................................. 24 1.5 Non-Adherence Following Kidney Transplant ..................................................................... 25 1.5.1 Measuring Non-Adherence .................................................................................................................. 25 1.5.2 Determinants of Non-Adherence ......................................................................................................... 27 1.5.3 Consequences of Non-Adherence ........................................................................................................ 29 1.5.4 Adherence Interventions ...................................................................................................................... 30 1.6 Thesis Rationale ....................................................................................................................... 31 1.7 Thesis Aims ............................................................................................................................... 32 Chapter 2. Overview of Methods and Materials .................................................................... 34 2.1 Study Design and Patient Population ..................................................................................... 34 2.2 Data Collection and Participant Follow-up ........................................................................... 34 2.3 Adherence Measures ................................................................................................................ 35 2.3.1 MAM-MM ........................................................................................................................................... 35  ix 2.3.2 Tacrolimus trough CV% ...................................................................................................................... 36 2.4 Barriers to Adherence .............................................................................................................. 36 2.5 Rejection .................................................................................................................................... 37 2.6 eGFR slope ................................................................................................................................ 37 Chapter 3. Clinical Factors that Influence Conversion to Extended -Release Tacrolimus 38 3.1 Introduction .............................................................................................................................. 38 3.2 Methods ..................................................................................................................................... 39 3.3 Results ....................................................................................................................................... 41 3.4 Discussion .................................................................................................................................. 44 3.5 Figures and Tables ................................................................................................................... 50 Chapter 4. The Impact of Extended-Release Tacrolimus on Adherence Outcomes in Pediatric Kidney Transplantation ................................................................................................ 55 4.1 Introduction .............................................................................................................................. 55 4.2 Methods ..................................................................................................................................... 56 4.3 Results ....................................................................................................................................... 60 4.4 Discussion .................................................................................................................................. 65 4.5 Figures and Tables ................................................................................................................... 72 Chapter 5. The Impact of Extended-Release Tacrolimus on Graft Outcomes in Pediatric Kidney Transplantation ................................................................................................................ 81 5.1 Introduction .............................................................................................................................. 81 5.2 Methods ..................................................................................................................................... 81 5.3 Results ....................................................................................................................................... 84 5.4 Discussion .................................................................................................................................. 87 5.5 Figures and Tables ................................................................................................................... 92 Chapter 6. Conclusions and Future Directions ..................................................................... 97 Bibliography ................................................................................................................................ 100 Appendices .................................................................................................................................. 127 Appendix A. Medication Adherence Measure (Medication Module) ............................................. 127 Appendix B. Adolescent/Parental Medication Barrier Scale .......................................................... 130   x LIST OF TABLES  Table  3.1. Demographic and Baseline Characteristics in Total Study Population and Potential Converters to ER-Tac……………………………………………………………………………52  Table 3.2. Patient and Clinical Characteristics according to Conversion Subgroup……………53  Table 3.3. Cox Proportional Hazards Regression Assessing the Influence of Co-Variates on Conversion to ER-Tacrolimus, Exploratory and Multivariable Analyses……………………….54  Table  4.1. Cohort Characteristics………………………………………………………………..74  Table 4.2. Linear (Tac CV%) and Logistic (MAM-MM SR 100% adherence) Unadjusted and Adjusted Regression Models Assessing the Influence of Co-Variates on Early Tacrolimus Adherence within the First Year after Transplant………………………………………………..75  Table 4.3. Linear (Tac CV%) and Logistic (MAM-MM SR 100% adherence) Unadjusted and Adjusted Regression Models Assessing the Influence of Co-Variates on Late Tacrolimus Adherence beyond the First Year after Transplant……………………………………………….77  Table 4.4.  Linear (Tac CV%) and ordered logistic (MAM-MM self-reported 100% adherence) unadjusted and adjusted regression models assessing the influence of co-variates on tacrolimus adherence before and after start date including ER-Tac………………………………………….79  Table 5.1.  Unadjusted and adjusted linear regression models assessing the influence of co-variates on eGFR slope in the first year after transplant……………………………………………..……93  Table 5.2. Unadjusted and adjusted linear regression models assessing the influence of co-variates on eGFR slope in the year pre- and post-start date………………………………………………94  Table 5.3.  Cox Proportional Hazard Regression models assessing the influence of co-variates on time to long-term outcomes following start date† (biopsy proven rejection, persistent decline of 50% eGFR and a composite endpoint of rejection, persistent 50% eGFR decline and graft  loss)………………………………………………………………………………………………95  Table 5.4. Cox Proportional Hazard Regression models assessing the influence of co-variates on time to the composite endpoint of biopsy proven rejection, persistent 50% eGFR decline and graft loss following start date…………………………………………………………………………..96 xi LIST OF FIGURES Figure 3.1.   Study Flow Diagram……………………………………………………………….50  Figure 3.2.  Kaplan Meier Survival Curve comparing conversion to ER-Tacrolimus over time (in months) by sex, and age at transplant during childhood (<13 years of age) or adolescence (13 years and above)………………………………………………………………………………..………51  Figure 4.1. Study Flow Diagram………………………………………………………………...72  Figure 4.2. Mean tacrolimus CV% ± SD and percentage self-reporting 100% adherence across the post-transplant period………………………………………………………………………….....73  Figure 5.1. Change in eGFR slope for converters and non-converters in the year pre- and post- start date………………………………………………………………………………………….92   xii LIST OF ABBREVIATIONS ACR – Albumin creatinine ratio   ADPKD – Autosomal dominant polycystic kidney disease   AFT – Accelerated failure time model   AMBS – Adolescent Medication Barrier Scale   AMR – Antibody mediated rejection   AUC24 – Area under the concentration time curve within 24 hours of dosing   β: Beta coefficient  BKV – BK virus   BKVN – BK virus nephropathy   BMI – Body mass index   BSA – Body surface area   caAMR – Chronic active AMR  CAKUT – Congenital abnormalities of the kidney and urinary tract   caTCMR – Chronic active T cell mediated rejection   CI – Confidence interval   CKD – Chronic kidney disease   CKD-MBD -  Chronic kidney disease – bone mineral disorder  CMV – Cytomegalovirus  Cmin – Minimum blood concentration  CNI – Calcineurin inhibitor   CYP3A4/A5 – Cytochrome P450 3A4/3A5 enzymes   xiii DD – Deceased donor   DGF – Delayed graft function   DSA – Donor specific antibody   dnDSA – de novo donor specific antibody   EBV – Epstein Barr virus   EDTA - Ethylenediamine tetra-acetic acid  ECD – Expanded criteria donor   eGFR – Estimated glomerular filtration rate   EM – Electronic monitoring   ER-Tac -  Extended-release tacrolimus   ESKD – End stage kidney disease  FDA – Food and Drug Administration   FSGS – Focal glomerulosclerosis   HD -  Hemodialysis   HHD – Home hemodialysis   HLA  - Human leukocyte antigen   HPV – Human papillomavirus   HR – Hazard Ratio  ICC – Intraclass correlation   IFTA – Interstitial fibrosis and tubular atrophy   i-IFTA – Inflammation within areas of interstitial fibrosis and tubular atrophy  IL-2 – Interleukin-2  IL-6 – Interleukin-6   xiv IQR – Interquartile Range   IR-Tac    Immediate release tacrolimus   IVIG – Intravenous immunoglobulin   KDIGO  - Kidney Disease Improving Global Outcomes  LCO – Latest Conversion Opportunity   LD – Living donor   MAM-MM - Medication adherence measure – medication module  MiHA – Minor histocompatibility  antigens   MICA – MHC class I polypeptide-related sequence A   NFAT – Nuclear factor of activated T cells   OR – Odds ratio  PD  - Peritoneal dialysis   PH – Proportional hazards  PJP – Pneumocystis jiroveci pneumonia   PMBS – Parental Medication Barrier Scale  PO – Per os   PTLD – Post lymphoproliferative disorder   rATG – Rabbit anti-thymoglobulin   RRT – Renal replacement therapy    SD – Standard deviation   SR: Self-reported   Tac CV% - Tacrolimus trough coefficient  of variation   TCMR – Acute T cell mediated rejection    xv t-IFTA - Tubulitis within areas of interstitial fibrosis and tubular atrophy  UNOS – United Network for Organ Sharing   VIF – Variance inflation factor   xvi ACKNOWLEDGEMENTS I owe particular thanks, first and foremost, to my supervisor Dr. Tom Blydt-Hansen who has provided me with encouragement, mentorship and many learning opportunities over the preceding two years. Without his prevailing wisdom and support this thesis would not have been possible. Furthermore, the critical and unwavering statistical expertise and guidance that I have received from Dr. Atul Sharma has been invaluable to my learning and for this I am sincerely grateful. Special thanks are also owed to the Blydt-Hansen lab team whose friendship and help has made my MSc a truly enjoyable experience.   I would also like to extend my gratitude to Dr. James Lan, Dr. Jagbir Gill and Dr. John Gill for their donating their time to help me and guide me through this process as well as their helpful, specific and constructive feedback along the way that has improved my work and taught me a great deal.   I would also like to acknowledge that part of this work was supported by a grant funded by the Canadian Institutes of Health Research (CIHR) and an investigator-initiated grant funded by Astellas Pharma Canada, Inc. 1 Chapter 1.  Introduction 1.1 The Role of the Kidney The kidney plays a multitude of critical roles that span several organ systems.1 Broadly, the roles of the kidney can be delineated into three major functions: glomerular filtration, regulation of fluid and electrolyte balance, and endocrine/regulatory roles. Glomerular filtration describes the crossing of plasma filtrate across the glomerular barrier into the urinary space. Kidney function is therefore determined by glomerular filtration rate (GFR), which is the volume filtered by all functioning nephrons in one minute (ml/min). This highly effective filtration leads to the elimination of water soluble toxins and bi-products of metabolism. GFR is challenging to measure directly and clinically an estimated glomerular filtration rate (eGFR) is used. In adults, eGFR is calculated accounting for age, sex and race.2-5 In children, eGFR is most commonly calculated using the bedside Schwartz formula, which factors in height, and therefore growth.6   Fluid and electrolyte homeostasis is maintained principally by the renal tubule and includes acid-base regulation through the selective resorption of bicarbonate and buffering of hydrogen ions. Endocrine and extra-renal regulatory functions of the kidney include the production of renin as part of the renin-angiotensin-aldosterone system, transforming vitamin D into its active metabolite 1,25-dihydroxycholecalciferol and the production of erythropoietin, which is made in the renal interstitium and stimulates erythrocyte development.  1.2 Chronic Kidney Disease 1.2.1 Definition As with all vital organ systems, the kidneys possess remarkable reserve, with the ability to upregulate filtration in response to physiological stressors. As a result, up to 50% of nephrons  2 can be lost prior to any detectable change in creatinine.7 Other signs of kidney damage such as micro-albuminuria (reflecting increased glomerular permeability to protein) may appear prior to any discernible dysfunction.8 Chronic Kidney Disease (CKD) is a clinical syndrome that, irrespective of the underlying etiology, describes the gradual irreversible decline of kidney function. International criteria published by Kidney Disease Improving Global Outcomes (KDIGO) classify CKD into five stages of severity.9 Beyond the age of two years old, eGFR in children and adults is roughly comparable, therefore CKD staging is also broadly applicable to children.10 CKD is diagnosed when 1) there is evidence of structural kidney damage despite a normal or even elevated eGFR 2) eGFR falls below 60 ml/min/1.73 m2 for 3 months or 3) albumin creatinine ratio (ACR) rises above 3mg/mmol. CKD is classified stage 5 once eGFR falls below 15mls/min/1.73m2. This milestone indicates the development of end stage kidney disease (ESKD) and often signifies the requirement for renal replacement therapy (RRT) in the form of dialysis, or transplantation.   1.2.2  Epidemiology Adult CKD is a global public health crisis with prevalence higher than 10% reported in several countries.11,12 More concerningly, due to the asymptomatic nature of early kidney disease, the true prevalence of CKD is thought to be significantly higher than reported cases. Diabetes and hypertension are the leading causes of CKD in adults; 30-50% of CKD cases are attributed to diabetes alone.13 Rising rates of these conditions in tandem with an increasingly aged population has led to the dramatic global rise of CKD. CKD affects 78 per million population of under 40s compared to 5900 per million population of those over 80 years of age.14 Alternative causes of  3 adult CKD include autosomal dominant polycystic kidney disease (ADPKD), infections, and drug toxicity.13   The pediatric prevalence of CKD is 55-60 per million age restricted population (pmarp).15 CKD in childhood, unlike adults, is not driven by high rates of diabetes and hypertension. Instead, congenital abnormalities of the kidney and urinary tract (CAKUT) predominate (accounting for 50% of pediatric CKD cases) followed by steroid resistant nephrotic syndrome, and glomerulonephritis.15 Males have a higher preponderance to congenital abnormalities and are therefore over-represented in the pediatric CKD population.16,17 Glomerulonephritis is the leading cause of CKD in adolescents and is the leading overall cause of ESKD as it progresses more rapidly to ESKD than CAKUT.16-18   However, pediatric nephrology may be on the cusp of a new era of leading CKD etiologies. Childhood obesity, an independent risk factor for CKD, has increased to epidemic proportions in recent years, and is contributing to an ever larger proportion of pediatric nephrology referrals.19-22 Over the same period, higher numbers of low-birth weight neonates with reduced nephron counts and exposure to nephrotoxic medication at birth are surviving into later childhood with an associated risk of chronic kidney dysfunction.23-25   Between eight and eleven pmarp children progress to RRT each year.16 Reliable ESKD data is limited to high income nations that can afford costly RRT programs: 80% of children receiving either dialysis or transplantation live in North America, Europe or Japan. The prevalence of ESKD in low and middle income countries remains unclear.16   4  The prevalence of both CKD and ESKD are lower in children than adults and less than 2% of all ESKD cases occur in those younger than 20 years of age.11 Nonetheless, CKD is an important disease in the pediatric population by virtue of its severity and impact on life expectancy; the mortality rate in children diagnosed with ESKD is 30-150 times that of healthy children.26,27   1.2.3 Diagnosis and Clinical Features  Due to its indolent nature, CKD is often diagnosed following routine blood or urine screening or is detected once progression is adequately far to trigger symptoms. In very young children with structural abnormalities, the diagnosis of CKD is made on this feature alone irrespective of kidney function as per the KDIGO guidelines and is usually diagnosed antenatally on prenatal ultrasound.9  As CKD progresses to ESKD, individuals may experience a myriad of health issues related to the cumulative decline in functional renal mass including Chronic Kidney Disease-Mineral and Bone Disorder (CKD-MBD), hypertension, cardiovascular disease and anemia. Children with CKD face additional issues including stunted growth and potential neuro-cognitive delay. The wide-reaching ramifications of kidney dysfunction are managed with complex medication regimens, supplements and a strict diet. However, as patients approach ESKD, the issue of filtration, with the need to tightly regulate electrolyte and bicarbonate reabsorption as well as manage the elimination of toxins requires a more sophisticated solution, which comes in the form of RRT.    5 1.2.4 Renal Replacement Therapy Renal replacement therapy is an umbrella term that encompasses peritoneal dialysis (PD), hemodialysis (HD) and kidney transplant. RRT is self-explanatory in its aim to replace kidney function. With peritoneal and hemodialysis, successful elimination of waste and balancing of electrolytes is achieved through the respective substitution of peritoneum or synthetic material as the semi-permeable membrane required for filtration. However, the best dialysis only achieves kidney function less than 15 ml/min/1.73 m2 and fails to replace the endocrine or other regulatory functions of the kidney. In transplantation, the donor kidney replaces all functions of the failed native kidneys.   Peritoneal dialysis allows relative freedom as it can be performed overnight at home and is excellent in very young children where vascular access may be more challenging; 92.5% of those under one year of age are treated with PD.11 Hemodialysis has a survival advantage over PD, is associated with lower rates of infection but is limited by its impact on quality of life and schooling, requiring significant time spent at the hospital.17,28-30 Home hemodialysis (HHD) allows longer and more frequent dialysis, however, the infrastructure to achieve this is complicated and more commonly HD occurs in a hospital setting or at satellite dialysis centres.31,32   Untreated ESKD portends an unfavorable prognosis and RRT is undoubtedly life-saving. However, average life expectancy for children on dialysis is only 20 years and dialysis is associated with significant cardiovascular and infectious morbidity, reduced quality of life and a substantial healthcare cost.29,33,34 The high rate of cardiovascular complications pertain to the persistent kidney failure more than the dialysis treatment. Kidney transplantation confers a clear  6 survival and morbidity advantage over dialysis given that it more completely recovers GFR and it replaces other functions of the kidney. Kidney transplantation has become the treatment of choice for ESKD in children.34-36   1.3 Kidney Transplantation  1.3.1 History  Kidney transplantation has experienced steady growth as a clinical field since the 1950s. In 1954, Hume and Murray performed the first kidney transplant to have long-term success between two identical twins.37 The first pediatric kidney transplantations were performed from 1963-1966 in Colorado.38   In non-identical twin transplants, allograft survival relied on overcoming rejection, which without intervention resulted in rapid kidney failure and death. Initially, total body radiation was used to induce immune tolerance. The unacceptable high mortality of irradiation coupled with a burgeoning understanding of the immunology behind rejection heralded the era of chemical immunosuppression. 6-Mercaptopurine and azathioprine were used with moderate success; recipients who received solely AZA or 6-MP had a life expectancy post-transplant of months as opposed to days or weeks. One of the biggest breakthroughs in the field of transplantation was Thomas Starlz’ addition of prednisone as both a rejection prophylactic and as a rescue therapy, which improved survival to over 70% at one year. The presentation of his findings at a conference in 1963 saved transplant medicine as a viable therapeutic option and his azathioprine/prednisone regimen became the treatment standard for the next 20 years. Cyclosporine, the first calcineurin inhibitor (CNI) changed the face of transplant medicine again when it was approved by the FDA in 1983, augmenting one-year kidney graft survival rates to  7 approximately 80%. Tacrolimus, a more effective CNI was then developed and approved in 1994. Tacrolimus remains a cornerstone of transplant immunosuppression today.    Many other important landmarks have occurred over the last five decades including national donor card schemes, improved technologies resulting in better donor-recipient matching, national and international transplant registries and multidisciplinary care across pre- and post-transplant services.39 These combined surgical, pharmacological and logistic strides have elevated kidney transplantation from a non-viable treatment modality to the gold standard treatment for ESKD in less than 50 years.17  1.3.2 Organ Donation and Allocation   Given the survival benefit for kidney transplantation over dialysis in children with ESKD, pre-emptive transplantation is preferred.40 Compared to post-dialysis transplantation, pre-emptive cases have 70% improved patient and 30% improved graft survival whilst eliminating the detrimental impact of dialysis on pediatric quality of life.35,36 Earlier transplantation also optimizes time-sensitive growth and cognition outcomes. Pediatric patients can miss out on pre-emptive transplantation secondary to late referrals to specialist nephrology services, leaving inadequate time to workup living donors before dialysis is required.41,42 Although, prompt referral can be improved with clinician education and robust referral processes this has limited impact in the face of the larger barrier to early transplantation - a demand for kidney donors that outstrips supply.  Pediatric priority within the organ allocation system attempts to mitigate against this but nonetheless, over 80% of pediatric kidney transplant recipients continue to require treatment with dialysis before a donor kidney is available.43   8  1.3.3 Factors that influence Transplant Outcome Donor A variety of aspects pertaining to the donor’s health have been shown to affect kidney transplantation success including donor age, comorbidities such as hypertension and hypercholesterolemia, and living (LD) vs deceased donation (DD).44-46  Of these younger donor age and living donation have the greatest positive impact on transplant longevity.47-49  Although a critical donor source, DD is consistently associated with inferior pediatric outcomes compared to LD.50 LD are more commonly related to the recipient with inherent better HLA matching and shorter cold ischemia time, reducing early damage secondary to ischemia re-perfusion injury.51   Increased donor age is associated with higher peri-transplant serum creatinine, delayed graft function (DGF) and increased mortality secondary to cardiovascular events.52 Older donors also associate with higher rates graft failure, and death with a functioning graft, particularly in young recipients.53 In donor-recipient pairs with no HLA mismatch, five year graft survival is as low as 39% when donors are over 60 years old but as high as 81% for donors aged 21-30 years old.54   Donors that do not meet standard organ donation criteria due to features associated with poorer outcome may be considered as expanded criteria donors (ECD). In the USA, ECD are formally defined as deceased donors aged 60 and over, or aged 50-59 who died secondary to cerebrovascular accident, had hypertension, or elevated creatinine at the time of death.55 Given the survival benefit of kidney transplantation over remaining on the transplant wait list in age  9 matched recipients, ECD provide a survival benefit to those over 40 but in younger recipients and particularly pediatrics, ECD are not recommended. As a result, and to mitigate against the negative impact of donor age on pediatric transplant outcomes, the USA implemented the share 35 program that ensures pediatric priority of deceased donors younger than 35.56 This contributes to good quality deceased donation in children and shortens transplant wait times in this population. In Canada, and specifically British Columbia, children are included in category 1 priority listing, only behind highly sensitized patients, combined kidney and pancreas transplants and medically urgent transplantation.57 Recipient The environment into which the kidney is transplanted is as critical to transplant survival as the health of the donor kidney. Recipient factors such as dialysis history, nutritional status and demographic features such as age, sex and race all influence alloreactivity and graft longevity.  Adolescent age has long been associated with poor allograft outcomes and is associated with the worst graft survival rates of any age group.58-65 Rejection rates are also higher in adolescence.47,66 Effector cell populations rise in children, peak during adolescence and early adulthood before innate and adaptive immune responsiveness becomes increasingly senescent with age.67-71 Although graft loss in adolescents is often equated with non-adherence, such immunobiological developments place this cohort at increased risk of alloreactivity.    Female sex is a recognized risk factor for graft failure, girls exhibit higher T cell alloreactivity and experience higher rates of rejection and graft loss than boys.72 Estrogen has been shown to upregulate antibody and cytokine signaling and drives lymphocyte development, acting as a  10 potential mechanism.72 Additionally, in the case of male donors to female recipients, the presence of H-Y antigen contributes to rejection.73-75 Finally, although more relevant for adult female recipients, pregnancy exposes women to fetal alloantigens and is a significant sensitizing event that limits the suitable donor pool and impacts transplant outcomes.   Recipient race affects post-transplant outcomes particularly for African American individuals in the USA. Genetic differences between races that influence graft outcome are confounded by complex interactions with social determinants of health. Black recipients are both at higher risk of recurrent focal and segmental glomerulosclerosis (FSGS), and of alloreactivity and rejection due to a higher number HLA polymorphisms.76-80 In addition, long term transplant survival in black recipients is hampered by longer times spent on the transplant waitlist, higher rates of deceased donor transplants, an increased chance of HLA mismatch by virtue of being matched against a predominantly white cohort, and socioeconomic factors such as restrictions in accessing affordable medication or medical care.77,78,81,82   Metabolic factors including nutrition and uremia are recognized to play an important but as yet not fully understood role in graft longevity. A U-shaped relationship exists between BMI and transplant outcomes, with underweight and obese individuals experiencing reduced patient and graft survival, increased rates of infection, and for obese individuals higher rates of cardiovascular disease.83-91 Markers of malnutrition including low serum albumin and vitamin D deficiency at transplant were also associated with higher rates of graft failure.92-95    11 Prior dialysis particularly those longer with dialysis vintage have poorer graft outcomes and more cardiovascular complications compared to those transplanted pre-emptively.40,96-98 Uremia is a recognized cardiovascular risk factor and is thought to be one of the mechanisms by which time with ESKD on dialysis negatively impacts graft survival.97 Donor recipient interface  HLA Mismatch Human leukocyte antigens (HLA) are highly polymorphic proteins that denote self. Donors and recipients are screened at a minimum for whole antigen matching at HLA-A, -B, and DR loci. Broad antigen matching has been outperformed by the introduction of molecular HLA compatibility, which matches at the more sensitive epitope/eplet level using technologies such as HLAMatchmaker.99-102 However, an incomplete understanding of eplet immunogenicity and feasibility/cost limit its current use in clinical transplant allocation. Circumvention of these barriers will likely herald the introduction of high-resolution HLA matching into the clinic.103   HLA mismatch is an independent risk factor for the development of de novo DSA (dnDSA) and graft loss in adult and pediatric kidney transplant populations.104-107  A pediatric study from the United Network for Organ Sharing (UNOS) registry reported that a single HLA mismatch increased the risk of graft failure by 30%, with six mismatches increasing the risk by 92%.108 Furthermore, pediatric recipients will require multiple transplants in their life, ameliorating donor-recipient compatibility reduces sensitization for subsequent transplants in addition to prolonging current graft survival.109    12 Class II mismatches in particular associate strongly with outcomes. If only partial allocation is feasible such as with deceased donor transplantation, minimizing HLA-DR mismatch is considered most important.102,110-113 HLA-DQ mismatches have garnered increasing recognition due to the high prevalence of anti-HLA DQ dnDSA relative to non-DQ DSA and the detrimental impact that HLA-DQ mismatches and HLA-DQ antibodies have on rejection and graft loss.114-117   Minor histocompatibility antigens (MiHA) are non-HLA peptides that are also immunogenic. Their role in alloimmunity is not fully elucidated and many have been shown to be benign to the allograft. However, certain MiHA have been identified as alloreactive having caused rejection in transplants performed across HLA identical siblings. MiHA of importance include H-Y antigens,73,75,104,118  MHC class I polypeptide-related sequence A (MICA),119 and antibodies related to vascular function such as anti-endothelial receptor antibodies and angiotensin II type 1 receptor antibodies, which are an important cause (66% cases) of DSA negative AMR.120   Blood group antigens  Incompatibility of the blood group antigens leads to hyperacute rejection. The KDIGO guidelines list ABO incompatibility as an absolute contraindication to pediatric kidney transplant.9   Pre-sensitization Pre-sensitization refers to donor-specific immunological memory that has developed prior to transplantation and results in an anamnestic response following transplantation. Pre-sensitization and exposure to potential donor antigens is caused by blood transfusions,121 prior  13 transplantation,122 pregnancy, as well as cross-reactive antibodies that can develop following infection.123-126 Peri/Post-transplant factors Immunosuppression All patients require post-transplant immunosuppression in order to mitigate rejection. The most common immunosuppressive regimen includes a calcineurin inhibitor (CNI), an anti-proliferative agent, and a glucocorticoid.   Calcineurin inhibitors (CNI) form the backbone of rejection prophylaxis. Tacrolimus in particular has superseded cyclosporine as a the CNI of choice due to fewer acute rejection events and improved five year graft survival rates (84% vs 70%).127  Mycophenolate mofetil was added as an adjunctive therapy after three large randomized trials demonstrated that its addition to a CNI and prednisone regimen significantly reduced rejection rates within the first twelve months after transplant.39,128,129 The long term use of corticosteroids in children is limited by their wide reaching adverse effects including but not limited to issues with growth, and the precipitation of hypertension and diabetes. Steroid free regimens have therefore been introduced and are associated with improvements in height and acceptable rejection rates.130 However, compensatory increased use of other immunosuppressants in place of corticosteroids has been shown to increase rates of viral infections and PTLD, which has created reticence in the widespread removal of corticosteroids from transplant protocols and in the majority of centres glucocorticoids remain an essential part of the regimen.131   14 Both under and over immunosuppression risk adverse outcomes. Reduction in immunosuppression necessitated by viral infection or inadvertently secondary to patient non-adherence (section 1.5. Adherence) risks B and T cell escape, subsequent rejection and graft failure.132-140 Over immunosuppression, on the other hand, is associated with increased rates of infection and malignancy. As a result, the immunosuppressive regimen is tightly controlled, especially for tacrolimus where regular drug level monitoring aims to maintain optimal therapeutic exposure.   Delayed Graft Function Delayed graft function (DGF) is defined by the commencement of dialysis in the first week following transplant and is independently associated with higher risk of later rejection and graft failure.135,141-144 Prolonged dialysis vintage, longer cold ischemia times and grafts from older donors are all risk factors for DGF through their ability to increase ischemia reperfusion injury.145-147 Ischemia reperfusion injury upregulates inflammatory cytokines within the graft, attracting immune cells and promoting the development of an early adaptive response.148   1.3.4 Challenges Specific to Pediatric Kidney Transplantation Recipient Size The small stature of pediatric recipients complicates kidney transplantation outcomes for multiple reasons. Although kidney transplantation is the optimal intervention for ESKD regarding growth and cognition, the pediatric transplant population do not catch up to their healthy counterparts and these remain important challenges that carry over from CKD into the pediatric post-transplant course.149,150   15  Furthermore, operative considerations for pediatric anatomy must be considered, donor-recipient size mismatch poses a particular problem. Pediatric donors are scarce and anastomoses between pediatric vessels are prone to thromboses.151 More commonly adult donors are used but the significant increased mass of an adult kidney puts strain on the pediatric cardiovascular system, which can lead to hypoperfusion, ischemia and delayed graft function.152,153 Having said that, on the most part, adequate perfusion is achieved with equivalent long-term graft outcomes to size matched grafts.154 Following transplantation, size mismatch may create an additional functional reserve that can mask damage secondary to rejection. In the absence of surveillance biopsies, delayed detection of alloimmune injury can negatively impact graft outcomes.152,155   In very small children (<10kg), the kidney is too large to be placed extra-peritoneally in the iliac fossa. Intraperitoneal transplant placement is associated with increased risk of bowel injury, migration of the kidney within the abdomen and more challenging biopsy sampling.151   Etiology of ESKD  High rates of CAKUT in the pediatric population increase the rate of infective complications and the need for later additional operations, with an associated risk of operative complications.151,156 Dependent on the structural abnormality, procedures commonly performed include corrective bladder surgery and bilateral native nephrectomy.      16 Transition to Adult Care  Graduating from pediatric to adult services is a rite of passage that is often disruptive to patient care with the expectations of taking on increasing responsibility for one’s own health, developing a relationship with a new clinical team and navigating unfamiliar healthcare environments.157 Patient related behaviour such as adherence is particularly vulnerable to decline during this period, which particularly in transplantation may have dire consequences.158 Specialist programs and transition coordinators now exist to monitor and acclimatize children to adult nephrology services.158  1.3.5 Sequelae of Kidney Transplantation Despite great strides in improving early graft outcomes,159 allografts do not function indefinitely and disappointingly there has been only minimal improvement in long-term outcomes over the last 30 years.160,161 Many transplant recipients, particularly younger individuals will therefore require more than one transplant in their lifetime. Chronic rejection remains the leading cause of graft loss in the pediatric population, responsible for approximately 36-38% of all graft failures.17 Prolonging graft longevity through the prevention and treatment of rejection is therefore an ongoing area of research and clinical interest. However, although rejection avoidance is critical, malignancy, infection and recurrent disease also pose significant threats to allograft survival and the opposing effects of immunosuppression must be considered in therapeutic recommendations. Rejection Rejection describes anti-graft T and B cell responses driven by the immune recognition of foreign donor antigen and represents immunological escape from maintenance  17 immunosuppression. As immunosuppressive regimens have become increasingly effective, the classical features of fever and graft pain that characterized rejection historically have attenuated and are rarely seen. Now, the decision to perform a biopsy is more commonly in response to a rise in serum creatinine or according to surveillance protocols with the aim of detecting subclinical rejection.   Rejection encompasses several clinical syndromes that correspond to differing morphology seen on histology and reflect a varying progression of the underlying alloimmune processes. Hyperacute rejection is the most rapid, often occurring within minutes, which necessitates re-excision of the graft on the operating table.162 It represents a rapid, overwhelming antibody response in the context of a positive crossmatch and is indicative of prior sensitization.162,163 Given that the majority of transplants are performed across a negative crossmatch with cautious screening for current and historical DSA, it is rare in the modern transplant era, accounting for approximately 0.2% of pediatric graft failures in North America.17   In the first year following transplant, the most common form of rejection is acute T cell mediated rejection (aTCMR). Acute TCMR is characterized by infiltration of T cells and macrophages within the tubule and interstitium and at higher grades of severity by the development of intimal arteritis.164,165 Primary treatment is with IV methylprednisolone and/or augmentation in baseline immunosuppression.166-169 In severe or steroid refractory cases polyclonal T-lymphocyte antibodies such as rabbit anti-thymoglobulin (rATG) are used as second line therapies.168-173   18 Late acute TCMR defines TCMR that occurs beyond the first year, and generally speaks to a more entrenched immunological process that is harder to resolve. More commonly than in early acute TCMR, T cell infiltration will be accompanied by B cell or plasma cell infiltrates, or mixed with antibody mediated rejection (AMR).174-177 Mixed, B cell rich and plasma cell acute rejection are all associated with significantly poorer prognoses and are more commonly corticosteroid refractory.177-182   AMR that occurs early in the post-transplant period is likely secondary to prior sensitization, either historical DSA or silent cellular sensitization that is not screened for pre-transplant.183 This allows for rapid rebound antibody production in the presence of donor antigen and is also known as type 1 AMR. Type 2 AMR refers to AMR that has developed secondary to dnDSA and commonly occurs later in the post-transplant course.183,184 dnDSA are more commonly class II anti-HLA antibodies and are associated with a poorer prognosis.184 Antibodies cause endothelial injury through a myriad of  complement mediated and complement independent mechanisms.185 AMR is diagnosed based on the presence of the following histological signs: glomerulitis or peri tubular capillaritis, evidence of antibody interaction with the graft tissue (most commonly C4d deposition), and the finding of serum DSA or equivalent classifier.164,165 No medications are recommended by the FDA in the treatment for AMR.186  The standard of treatment clinically has been established as IVIG and plasmapheresis despite poor quality evidence from heterogeneous trials and studies.187,188 Rituximab, an anti-CD20 antibody is also commonly used.188-190   Chronic rejection phenotypes reference the presence of active inflammation in areas that are scarred from prior alloimmune injury. Unlike in acute forms of rejection where creatinine may  19 spike, chronic rejection is often insidious with a gradual downtrend in kidney function that is also referred to as the “creatinine creep”.   In chronic active TCMR, tubulitis and interstitial inflammation are seen in areas of interstitial fibrosis and tubular atrophy (IFTA) referred to as t-IFTA and i-IFTA respectively.164 The development of transplant glomerulopathy alongside features of active AMR describe chronic active AMR, reflecting repeated and ongoing antibody mediated injury.164,165 Both are representative of terminal processes in the alloimmune pathway and portend a poor prognosis.191-193 This pertains particularly to caAMR, which is the leading cause of late allograft loss.193-195   There is no agreed upon treatment for either caTCMR or caAMR and recommendations for both are to optimize maintenance immunosuppression, closely monitor tacrolimus levels and the addition of a corticosteroid if the patient was previously on a corticosteroid free regimen.196,197 Despite multiple therapeutic options trialed for the treatment of caAMR, an expert international consensus from The Transplant Society reported insufficient evidence to recommend any therapy or combined therapeutic regimen.187 Novel treatments that block IL-6 signaling have shown potential promise in stabilizing histology and function in caAMR and randomized trials are currently underway to further establish their utility in this difficult to treat condition.198-201 Non-immunologic   Death with a functioning graft is most commonly secondary to infection, cancer or cardiovascular disease.202-204 As a result of chronic immunosuppression, transplant recipients are at six times the risk of developing a malignancy as the general population.205 PTLD is  20 overwhelmingly the most common, with other infection related cancers such as Kaposi’s sarcoma and HPV related cancers also posing a particularly high risk in this population.206 In the pediatric kidney transplant population, cancer after transplant accounts for approximately 10% of deaths.59 Cardiovascular disease is highest for those on the transplant waitlist and dramatically improves post-transplant.27,207 Despite this, pediatric kidney transplant recipients continue to have 100 times the risk of a cardiovascular event compared to their healthy peers and CVD accounts for over 30% of deaths with a functioning graft.11  The introduction of more effective immunosuppressants to curb rejection have driven up rates of opportunistic infections in recent transplant eras.208-213 The intensification of immunosuppression at times of rejection poses an additional risk.214,215 For viral infections, recipients who have not acquired immunity to the virus are at particularly high risk of severe infection when receiving a transplant from a sero-positive donor.216-218 Opportunistic infections that threaten patient and graft survival include cytomegalovirus (CMV), Epstein Barr virus (EBV), BK virus (BKV) and fungal infection pneumocystis jiroveci pneumonia (PJP). BKV nephropathy (BKVN) is associated with up to 8% of pediatric kidney graft losses219-223 and uncontrolled EBV replication may lead to post-transplant lymphoproliferative disorder (PTLD), the commonest malignancy seen in pediatric transplant recipients.206 Prior to widespread prophylaxis with co-trimoxazole, PJP affected between 5-15% of transplant recipients with a mortality rate as high as 40-50%.224-227  Although, anti-viral therapy is used in the treatment of CMV, treatment of BKV and EBV rely on reduction of immunosuppression in order to re-activate T cell responses and suppress viral  21 load.228,229 Both reduced immunosuppressive cover and heterologous immunity through antibodies that cross-react between viral antigens and HLA antigens risk precipitating rejection in the wake of a viral infection.230 The risk of infection is mitigated by careful monitoring of viral titers and prophylaxis (for PJP and CMV) during high-risk windows such as in the early transplant window and following rejection.231,232  Disease recurrence following transplant accounts for 6% of all pediatric kidney transplant losses across the USA and Canada.17 Recurrence is most commonly seen for FSGS, recurring up to 60% of children following transplant.230,233 IgA nephropathy and systemic lupus erythematosus are also prone to recur in the transplanted kidney as are certain rarer metabolic conditions such as oxalosis.230,234  Progressive CKD in patients with auto-immune conditions and recipients of non-kidney solid organ transplants raised the issue of chronic CNI nephrotoxicity and its importance post-kidney transplant. However, the true nature and extent of CNI impact on renal allografts is a contentious issue. CNI avoidance or minimization has shown benefit in some studies with reduced rates of fibrosis and arteriolar hyalinosis, but in others almost a third still develop classical CNI lesions despite never having taken cyclosporine or tacrolimus.235-237 Given the importance of CNIs in preventing rejection and the likely multifactorial nature of chronic allograft lesions, decisions around modifying transplant protocols must be carefully considered.   22 1.4 Tacrolimus  1.4.1 Pharmacodynamics Tacrolimus is a macrolide lactone isolated from the fermentation of streptomyces tsukubaensis that exerts its immunosuppressive actions through calcineurin blockade. Inhibition of calcineurin blocks the transcription of genes critical to T cell activation.238-240 Calcineurin’s role is to dephosphorylate the nuclear factor of activated T cells (NFAT). This enables NFAT entry into the nucleus where it induces transcription of cytokine genes, most notably IL-2, which is a potent driver of T cell proliferation. Once within the cytoplasm, tacrolimus forms protein complexes with FK506 immunophilin binding protein 12. This complex subsequently binds and inhibits calcineurin preventing the critical dephosphorylation of NFAT and inhibiting IL-2. Tacrolimus has been shown to be between 10 and 100 times more potent than cyclosporine, which acts similarly but binds to cyclophilin A.241 1.4.2 Pharmacokinetics  Tacrolimus is poorly absorbed in both adults and children.242 Its bioavailability is approximately 20-25% but this is highly variable between individuals.243 Tacrolimus’ lipophilic properties mean that its absorption is also influenced by the fat content of meals.  Once in the bloodstream, tacrolimus undergoes extensive sequestration by erythrocytes (95%) and the majority of the remainder binds to plasma proteins.244 Only the limited remaining unbound fraction is available to exert its immunosuppressive effects. In solid organ transplant recipients, the mean terminal elimination half-life of immediate release tacrolimus is between 12-19 hours and as such requires a strict 12 hourly dosing regimen. Tacrolimus is excreted in feces via the biliary tract following metabolism by hepatic cytochrome enzymes CYP3A4/A5. Less than 1% of tacrolimus is excreted unchanged in the urine.244   23 The pharmacokinetics of tacrolimus are affected by a wide range of factors including weight, age, race, concomitant medications (including corticosteroids), hematocrit, and the pharmacogenetics of P-glycoprotein transporters (involved in tacrolimus intestinal absorption) and CYP3A4/A5.242,243,245 Children require higher doses of tacrolimus than adults secondary to differences in bioavailability and total body clearance.246 Interindividual polymorphisms particularly of CYP3A5 influence tacrolimus metabolism with rapid metabolizers requiring higher doses.247 Oral dosing of tacrolimus in children usually starts at 0.1-0.2 mg/kg/dose. The variability of tacrolimus pharmacokinetics coupled with its narrow therapeutic range necessitates regular therapeutic drug monitoring to guide ongoing dosage.248   1.4.3 Tolerability and Toxicity  The increased risk of infection and malignancy secondary to tacrolimus has been previously discussed. Other adverse effects range from diarrhea to neuro- and nephrotoxicity.249 Neurological side-effects can be major including seizures, encephalopathy and coma, which occur in less than 10% of patients and is more common with IV administration. Tremors, headaches and sleep disorders constitute minor neurotoxicity and occur in up to 20% even with oral administration.249 Acute and chronic nephrotoxicity have both been observed secondary to tacrolimus, the impact of CNI nephrotoxicity in kidney transplant has been elucidated above.  Tacrolimus is also diabetogenic as it interferes with glucose metabolism and rates are higher than for cyclosporine.250 However, rates of hypertension and hypercholesterolemia are significantly lower for tacrolimus than for cyclosporine.250,251     24 1.4.4 Extended-Release Tacrolimus     Prolonged-release formulations of tacrolimus that only require once daily dosing have been developed including AdvagrafTM (Astellas Pharma Canada, Inc) and Envarsus PATM  (Paladin Labs, Inc). The safety and efficacy of Envarsus PATM has not been well established in children and only AdvagrafTM is licensed for pediatric practice within Canada. Therefore, hereafter when discussing once daily tacrolimus, it will be referred to as extended release tacrolimus (ER-Tac) and is referencing the use of AdvagrafTM. Twice daily tacrolimus will be referred to as immediate release tacrolimus (IR-Tac).   Bioequivalence of the two formulations has been based on a 1 mg: 1 mg conversion. Systemic concentration measured by the area under the concentration time curve within 24 hours of dosing (AUC24) and minimum blood concentration (Cmin) met the bioequivalence thresholds of 80-125% recommended by the FDA.252 In one randomised controlled trial of almost 700 adult stable kidney transplant recipients ER-Tac failed the tighter pre-specified 10% non-inferiority margin by less than one percent (10.9%).253 It is common for Cmin/trough levels to be lower with ER-Tac.254-258 This potential reduction in exposure following conversion is the largest concern with using ER-Tac, however the majority of individuals do not require dosage change and ER-Tac has not been shown to be associated with a higher rate of adverse clinical events.255,259,260   With regards to clinical efficacy of both medications, the OSAKA trial demonstrated equivalent efficacy for ER-Tac vs IR-Tac for a composite endpoint of graft loss, biopsy-confirmed acute rejection, or graft dysfunction at 24 weeks post-transplant.261 Also, equivalent efficacy , safety and graft function between the two medications has been demonstrated up to 4 years post-transplant.254,262-264 ER-Tac conversion has been associated with improvements in adherence in  25 adult solid organ transplant recipients, which have the potential to translate to an improvement in late rejection events and long term allograft outcomes.265-270 Some studies have reported an improvement in eGFR following conversion.271,272  1.5 Non-Adherence Following Kidney Transplant Adherence is defined as the extent to which a person’s actions conform to agreed upon medical advice.273,274 This is different from compliance, which is implicitly paternalistic and describes behaviour that simply conforms to medical advice. Non-adherence may manifest through missed clinic attendance or tests, deviating from diet or exercise advice or more classically not following medication protocols as prescribed. Although all of these pertain to transplant medicine, strict adherence to the immunosuppression schedule is absolutely critical in order to effectively prevent rejection and arguably should be prioritized.  Adhering perfectly to complex life-long medication regimens where medication must be taken at the same times each day and often conflicts with activities of daily living is very difficult. Unsurprisingly, medication non-adherence in transplant recipients is high, averaging 32% in the pediatric population.275 Non-adherence does vary widely across the literature however (5-80%), which likely reflects differences in population, adherence definition, and adherence measurement methodology.276-288  1.5.1 Measuring Non-Adherence  Methods for estimating medication non-adherence include clinician assessment, self-reporting, and pill based methods such as pharmacy refill records and electronic pill box monitoring (EM).  26 For medications such as tacrolimus, variation in therapeutic drug levels can also provide a serological estimate of adherence.   Clinician and self-reporting frequently over-estimate adherence rates.289-291 Social desirability and memory biases hinder accurate self-reporting, but this remains a cost-effective and convenient method, especially when patients are asked to remember a short time period. Across a range of conditions, including transplant, clinician gestalt regarding adherence on the whole correlates poorly with self-assessment and pill-based methods of adherence.289,291-297 However, there are exceptions; in an adult study by Schafer-Keller et al. 2008 rates of clinician assessed non-adherence (24.9%) was surprisingly higher than electronic monitoring (17.3%) and the two methods were significantly correlated.298 Furthermore, Fedderson et al, 2020 demonstrated that although pediatrician assessment of adherence did not correlate significantly with later rejection, pediatric psychologist assessment did, highlighting the importance of the multidisciplinary team in pediatric post-transplant care.299  Objective methods include EM, tacrolimus drug level monitoring and pharmacy refill counts. Electronic pill monitoring is often considered the gold-standard due to its ability to quantify late as well as missed doses and its lack of subjection to social desirability bias. However, its utility in the pediatric cohort may be limited: EM often does not accommodate liquid preparations; 41% of adolescents who used it found it difficult; and 22% said that it changed their medication taking routine.300 Moreover, 10% were explicitly worried about the impact of EM on their adherence.300   27 Disagreement between methods of assessing adherence may also vary because they are detecting distinct but related aspects of adherence behaviour. Combining adherence measures holds promise and has been shown to have higher sensitivity and specificity than individual measures used alone.292,301   1.5.2 Determinants of Non-Adherence Medication non-adherence is a complex behaviour with multifactorial determinants. Reasons for non-adherence in the pediatric cohort may be centered around not wanting to take their medication or wishing to be like their peers302 but is also commonly unintentional.  People often simply forget or have a busy schedule that interferes with taking their medication.291,303-305   There are several factors that have been shown to increase the risk of medication non-adherence in the pediatric cohort that include adolescence, independence with medication and challenges at home.   Adolescence is a difficult time for many individuals even without a chronic condition. It is a period of transition, social pressure and increasing independence. Although there are exceptions,284 adolescence has been reported as the riskiest time period for non-adherence with rates higher than adults and younger children.275,278,306 Transition to adult services during this age can cause further disruption, with an additional negative impact on adherence.158  Children who take on sole responsibility for their medication are more likely to struggle with adherence.279,281,307,308 Parental supervision is key to correct medication taking and the transition  28 of responsibility over medication needs to be cautiously managed. Medication knowledge and better health literacy in general has also been associated with improved adherence, a finding that is consistent across a range of illness and different population ages.309-313 Being well-informed likely reflects concern on behalf of the patient regarding the consequences of their condition, which translates into conscientiousness regarding medication taking.  A range of psychosocial issues have been associated with missed medications. Psychological factors including lower self-confidence, issues with anger and comorbid psychological conditions all increase the risk of non-adherence.277,278,308,314,315 Furthermore, challenging relationships with parents and complicated family dynamics have been shown to negatively impact adherence.278,280,302,303,316 Socioeconomic status for the most part correlates positively with adherence, which may be a factor of medication affordability and access, especially in countries without free health care.317 Interestingly, a recent study by Silva et al 2020, showed that in a population with free health care, high rather than low parental income was associated with non-adherence.306   Finally, the medication regimen itself is associated with adherence, the more complex a medication regime both in terms of medication quantity and dosing frequency, the higher the rates of medication non-adherence. Across a range of conditions in adults and children, once daily dosing and a lower medication burden are associated with superior adherence compared to more frequent dosing regimens.318-322  29 1.5.3 Consequences of Non-Adherence  As previously highlighted, non-adherence to tacrolimus has a deleterious impact on graft survival,53,213,282,323-325 and concerningly, only slight deviations from perfect tacrolimus adherence (5-8%) are required to increase the risk of graft loss.284,323 Compared to adherent patients, non-adherent individuals are at more than five-fold increased odds of graft failure.326,327 Medication non-adherence has been shown to be responsible for 16% of adult and 44% of pediatric graft losses.275,328. In one study, as many as 71% of pediatric graft failures were reported to be caused by non-adherence.329  Graft loss in non-adherent patients is mediated predominantly by late acute and chronic rejection.283,330,331 Both poorer self-reported adherence and higher tac CV% associate with late acute rejection.140,275,328,330-338 Patients classified as non-adherent were more likely to require an indication biopsy after one year and had increased rates of C4d deposition, tubulitis and fibrotic change on histology.339,340 Finally, non-adherence is an independent predictor for dnDSA and chronic rejection, and is reported in 47-58% of those diagnosed with chronic rejection.110,140,178,193,279,338,340-342   Non-adherence is costly; by three years post-transplant, low adherence has been calculated to cost $21,600 (USD) more per person, excluding the cost of immunosuppressive medication.323 Thus, from a patient outcome and health economics perspective, improving adherence is an important priority in transplant medicine.   30 1.5.4 Adherence Interventions  Ameliorating adherence is a multifaceted endeavour; a range of interventions have been developed that attempt to make medication taking more convenient, improve education regarding medication or address psychological factors associated with medication non-adherence.   Consistent, longitudinal adherence support in the form of education and counselling from a trained professional has been shown to improve adherence but is a resource intensive strategy for staff and time intensive for patients.343  Models that include telemedicine counselling, prompts and in-person coaching such as in the MAESTRO-Tx trial in adults and the pediatric TAKE-IT trial improved adherence whilst being more cost-effective and represent promising feasible strategies.344,345 Behavioural interventions that patients can use independently such as telehealth reminders or electronic pill management systems have been shown to improve adherence but studies often have short follow-up periods making it hard to assess the long term impact of such interventions and the effect on transplant outcomes.343  Simplifying medication regimens by reducing the number of daily doses is an effective way to improve adherence across a range of conditions.320,321,346,347 Specific to solid organ transplantation, ER-Tac has been associated with an overall improvement in adherence in adults compared to IR-Tac. Compared to pre-conversion adherence, studies have reported a 34-36% improvement in adherence at 4-12 months post-conversion in liver, heart and kidney transplant recipients.265,266,269,270,348 One study reported an improvement in adherence that did not persist beyond one month, 267 and two studies did not find an improvement in adherence.268,349 ER-Tac has also been associated with fewer adherence barriers, is more convenient and preferred by patients.348,350,351   31  Limited studies exist in children. A study of 11 highly stable pediatric kidney transplant recipients demonstrated no change in adherence following conversion to ER-Tac but sample size was small and baseline adherence was reported as excellent.352 Quintero et al. 2018 reported an improvement in non-adherence rates from 58% to 38% following conversion in children post liver-transplantation.353 One further study in pediatric kidney transplant recipients analyzed adherence barriers associated with conversion, and showed substantial improvement regarding the impact to daily life and medication burden.260     1.6 Thesis Rationale  In summary, children, and adolescents in particular, experience higher rates of rejection, allograft dysfunction and loss compared to adult kidney transplant recipients.47,58,62,64,65,159 Although, there is an immuno-developmental foundation that underpins increased alloreactivity in this age group,67,68,70 non-adherence remains a significant and concerning contributing factor to inferior graft outcomes.275,354  In line with evidence that once daily dosing improves adherence over more frequent dosing regimens, ER-Tac was developed, which is taken every 24 hours and replaces the strict 12 hourly dosing of IR-Tac. ER-Tac has been associated with improved adherence in adults following kidney transplant,267,350,355,356 but this has not been extensively studied nor conclusively demonstrated in the pediatric kidney transplant cohort.260,352    32 Adult studies on the whole have reported stable eGFR following conversion to ER-Tac,254,262,263 but importantly there are studies that report an improvement in kidney function.271,272 No studies in children post kidney transplant have analyzed the efficacy of ER-Tac conversion in the context of both adherence, and long term rejection or functional outcomes.   This study is, to our knowledge, the first multi-centre study to comprehensively assess 1) which factors influence conversion to ER-Tac in routine practice and subsequently, 2) the impact of ER-Tac on adherence and graft outcomes in pediatric kidney transplant recipients. We hypothesize that ER-Tac will be associated with superior adherence and graft outcomes compared to IR-Tac.   1.7 Thesis Aims 1. The first aim is to determine the patient characteristics and clinical factors that influence physician decisions to convert to ER-Tac.  2. The second aim is to determine the association of self-reported and serological tacrolimus adherence measures with conversion to ER-Tac  a. To understand the determinants of adherence in this population, we will compare factors that influence adherence in the first year and beyond the first year after transplant.  b. To measure the impact of ER-Tac on adherence in this population, we will compare change in adherence over the year pre- and post-adherence between non-converters and converters.    33 3. The third aim is to determine whether allograft outcomes are superior in those converted to ER-Tac compared to those remaining on IR-Tac a. To measure the impact of ER-Tac short-term kidney function we will compare change in eGFR slope over the year pre- and post-adherence between non-converters and converters.   b. To measure the impact of ER-Tac on long-term outcomes we will compare time to 50% permanent decline in eGFR, time to first rejection episode and to graft failure following conversion between those taking once daily and those continuing twice daily tacrolimus.    34 Chapter 2.  Overview of Methods and Materials  2.1 Study Design and Patient Population This is a prospective, observational multi-centre cohort study conducted across 11 Canadian pediatric transplant centres between 2012 – 2018, with the objectives to evaluate factors that influence conversion to ER-tacrolimus (ER-Tac) (Advagraf ®, Astellas Pharma Canada, Inc) and the subsequent impact of ER-Tac on adherence and graft outcomes.   The study protocol adheres to the Declaration of Istanbul, and was approved by the research ethics board of the University of Manitoba (REB# H2012:302) and participating sites. Participants were eligible for enrolment if 21 years old or younger at the time of transplant and excluded if they were unable to attend scheduled study follow-up visits. Participants and families provided informed consent/assent in line with site REB/IRB recommendations. This is an incident cohort where all included participants were commenced on IR-Tac at baseline. Conversion to ER-tacrolimus was at the discretion of the participant’s nephrologist in line with their routine practice.   2.2 Data Collection and Participant Follow-up  Participants were only followed whilst they remained under the care of a pediatric transplant program. Scheduled visits occurred at baseline until a maximum of five years, initially every two weeks for two months, then monthly until six months, three-monthly from six to twelve months and six-monthly thereafter.  Additional targeted visits occurred in the context of kidney biopsies, rejection, acute kidney injury or infection. Clinical information was collected at each visit including height and weight, creatinine and therapeutic drug levels, current immunosuppression,  35 the presence of rejection.  Basic imputation was performed for missing height data. Based on height recorded at prior and subsequent visits, the average height was calculated for the interim, enabling calculation of eGFR from creatinine values where height data was unavailable. For aims 2 and 3, additional data in a subset of patients (n =31) including tacrolimus trough levels, creatinine, and biopsy reports were available and collected retrospectively beyond the patient’s last recorded visit.   2.3 Adherence Measures Adherence to Tacrolimus was assessed using the Medication Adherence Measure- Medication Module (MAM-MM)357 and the Coefficient of Variation (CV%) calculated for tacrolimus trough levels (Tac CV%).332,334   2.3.1 MAM-MM The MAM-MM was completed at each scheduled visit. It is a validated self-reported measure of pediatric/parental medication adherence, conducted as a semi-structured interview. Participants were asked to state how many tacrolimus doses were missed or taken late over the previous seven days.  To preserve statistical power, missed and late doses were collapsed into a single metric: the percentage of doses taken on time (i.e., neither missed nor late), which was calculated as follows:  %	#$%&%	'()&*	$*	'+,& = (/$'(0		12&%32+4&#	#$%&% −		(,+%%&#	#$%&% + 0('&	#$%&%)Total		prescribed	doses	 	) 	× 	100  Perfect or 100% adherence was defined as 100% of tacrolimus doses taken on time.    36 2.3.2 Tacrolimus trough CV%  Tacrolimus requires clinical monitoring due to its narrow therapeutic window.  Dosing is adjusted in order that serum tacrolimus trough levels conform to optimal target levels. Erratic trough levels indicate poor medication adherence.332 The coefficient of variation percentage (CV%) represents tacrolimus trough level variability over time: /(32$0+,H%	IJ%	 = ('2$HKℎ	0&M&0	%'(*#(2#	#&M+('+$*'2$HKℎ	0&M&0	,&(* ) × 	100  The CV% was calculated by dividing tacrolimus trough level standard deviation (SD) by the trough level mean, which is then multiplied by 100 to derive a percentage. Tacrolimus trough levels were collected at each visit. In addition, tacrolimus trough levels that had been collected since a participant’s prior visit were also documented.   Ethylenediamine tetra-acetic acid (EDTA) venous or capillary blood collection tubes were used for trough level collection. Tacrolimus trough levels were collected, and analyzed locally based on individual centre protocol.   2.4 Barriers to Adherence The adolescent medication barrier scale (AMBS) (17 questions) and parent medication barrier scale (PMBS) (16 questions) assess barriers that adolescents and parents face in correctly following their prescribed medication regimens across three areas: 1) disease frustration/adolescent issues 2) regimen adaption/cognitive 3) ingestion issues.311 Participants report their agreement with statements using a Likert Scale: 1 (strongly agree) – 5 (strongly  37 disagree). AMBS questionnaires were collected only from participants who had completed Grade 6 (11-12 years and above).   PMBS/AMBS were completed at baseline, 1 year, 18 months and 2 years post-transplant then yearly thereafter until 5 years.  The total PMBS and AMBS scores from fully completed questionnaires were used in analyses.   2.5 Rejection  Rejection in this study was defined as a biopsy proven episode of rejection that had required treatment. Rejection episodes were not included or excluded based on specific treatments.   2.6 eGFR slope  Estimated glomerular filtration rate (eGFR) was calculated using the revised Schwartz equation.6 eGFR data were converted into an individual regression line for each patient for the first year after transplant and the pre- and post- conversion period, provided they had at least 6 months of eGFR data in all time windows. Following calculation of the regression coefficient (ml/min/1.73 m2/year), slopes were divided by an individual’s baseline eGFR (nadir eGFR between 31 and 62 days post-transplant) to provide the percentage change in eGFR per year. The difference in magnitude between percentage eGFR change pre- to post- start date could then be calculated by the difference in regression coefficients.   38 Chapter 3.  Clinical Factors that Influence Conversion to Extended -Release Tacrolimus  3.1 Introduction  Tacrolimus is the cornerstone of immunosuppression regimens following kidney transplant. In its conventional immediate-release formulation (IR-Tac), tacrolimus is taken on a strict 12-hour schedule. An extended-release, once-daily preparation of tacrolimus (ER-Tac), has been developed, in line with evidence that lowering medication dosing frequency is an effective strategy to reduce non-adherence.320,321   In adults with solid organ transplants, ER-Tac has been shown to positively affect adherence and has been demonstrated to be equally efficacious in terms of graft survival and function.260,263,265,266,272,352,355,358-360 The pharmacokinetics, safety and efficacy of ER-Tac have been well demonstrated in pediatric kidney transplant recipients,258,260,361 and yet there is a dearth of studies assessing ER-Tac and adherence in children post kidney transplant.  In Canadian pediatric clinical practice, IR-Tac is generally commenced immediately post-transplant with conversion to ER-Tac determined by the transplant physician. Conversion to ER-tac in non-interventional adult studies is highly variable, ranging from 8-75%.269,349 Whether ER-Tac is currently being prescribed to its desired target demographic (those struggling with adherence) is unclear. Fellström et al. 2018 demonstrated lower adherence in participants that were subsequently converted to ER-Tac, suggesting that this may be the case.349 However, Cassuto et al. 2016 reported that adherent participants were converted to ER-Tac earlier than their non-adherent counterparts.355  39  The aim of this study is to understand the clinical factors that influence physician decision to convert pediatric kidney transplant recipients to ER-Tac. To our knowledge, this is the first study to assess this in the kidney transplant literature. We hypothesize that as age increases, predisposition to conversion to ER-Tac will increase. We anticipate that this reflects a need for convenience in adolescents who are more likely to be occupied at the time of their evening medication, rather than a reflection of adherence.   3.2 Methods  Data for this analysis was closed prior to completion of all study visits and also does not include long-term follow up data. This analysis included data collected until 2019-10-25.   Outcome Variable  Our primary endpoint was defined as conversion to ER-Tacrolimus, participant data in this study was censored at the latest opportunity to convert (LCO).  The LCO was defined as the last recorded visit prior to conversion, or if remained on IR-Tacrolimus and never converted, the latest recorded study-visit.   Co-Variates For factors that were dynamic over the post-transplant course (self-reported adherence using the MAM-MM, tacrolimus trough level variability, kidney function), contemporaneous averages were established for each participant over the two years prior to LCO. A patient was recorded as having recent rejection if they experienced an episode of rejection that required treatment in the  40 prior two years. Tacrolimus levels were standardized against their respective target levels prior to calculating the coefficient of variation (CV%). If the post-transplantation period was less than two years, all data was included with the exception of the first 31 days post-transplantation. PMBS/AMBS completed at baseline were included.   Statistical Analysis Statistical analyses were performed using R (Version 4.0.0).362 Categorical data were presented as frequency and percentage. Continuous data were presented as mean and standard deviation, or median and interquartile range (IQR). Basic overall comparisons of the groups ‘Remained on IR-Tac’ and ‘Converted to ER-Tac’ were performed using student’s t-test or Mann-Whitney U for continuous variables and chi squared or fisher’s exact test for categorical variables. All statistical tests were two-sided. For all analyses, statistical significance was set at p<0.05.  Covariate influence on conversion to ER-Tacrolimus was assessed using Cox Proportional Hazards Regression. The proportional hazards (PH) assumption was tested using Schoenfeld residuals and Harrel’s Rho. As a result of the violation of the PH assumption by age at transplant, parametric accelerated failure time models (AFT) that specify the distribution of the survival/hazard function and do not rely on the PH assumption were considered. However, the plotted hazard function appeared multi-modal, and would have required at least four parameters to fit. Given the limited number of conversion events (n=30) and the known increasing bias observed as events per variable decrease below ten,363 a four parameter AFT model was deemed inappropriate for this analysis. Models were therefore performed using Cox Proportional Hazards  41 Regression with the inclusion of a single additional interaction term: age at transplant (years):time post-transplant (months).  Given our hypothesis regarding age, age at transplant was included as an obligate predictor. Exploratory analyses, adjusted for age and the age:time interaction, identified additional candidate predictors (p≤0.1) for inclusion into the final multi-variable model. Co-linearity in the multi-variable model was formally assessed using variance inflation factors (VIF). Maximum VIF in the final model was 1.53 (excluding the expected correlation between age at transplant and the age:time interaction); VIFs greater than five are usually deemed problematic. Kaplan-Meier plots visualized the probability of remaining on IR-Tac for sex and age at transplant.   3.3 Results  Patient Characteristics  A total of 101 participants were enrolled and subsequently transplanted. Ninety-seven patients were included in the total study population: one participant was immediately lost to follow-up and three experienced early graft failure due to thrombosis (n =1) or disease recurrence (n=2). Participants able to convert to ER-Tac were included in the analysis (n=66). This excluded participants without access to ER-Tac (from Ontario sites, n =23), and those unable to take ER-Tac capsules (received IR-Tac via gastric or nasogastric tube and had never taken IR-Tac capsules orally (PO), n = 6, or participants were < 5 years at latest visit and had never taken PO IR-Tac capsules, n = 1) (Figure 3.1). One further participant was excluded for never having taken IR-Tac of any formulation.    42 Baseline and demographic characteristics of the total study population and the ‘can convert’ population are detailed in Table 3.1. Potential converters were older (13.1 ± 4.7 vs 11.35 ± 5.5 years) and were more likely to take tacrolimus in capsule form (93.9% vs 78.4%). Overall, there were more males (60.6%), and the majority of participants were concomitantly prescribed mycophenolate mofetil (97%) and prednisone (100%).   For those participants able to convert, one participant was prescribed ER-Tac as early as the first post-transplant visit, lacking any contemporaneous data; a second participant converted having attended targeted visits but no scheduled visits, and thereby had not completed any MAM-MM interviews. Group comparisons and subsequent analyses were performed including these participants where data was available. Compared to those who remained on IR-Tac, those who converted to ER-Tac were more likely to be female (53.3% vs 27.8%, p = 0.06), had higher HLA A, B mismatch (2.43 (0.86) vs 1.83 (1.18), p = 0.02), and had experienced at least one episode of rejection (66.7% vs 50%, p = 0.047) in the two years prior to latest conversion opportunity (LCO) (Table 3.2). Months to LCO was shorter in the converted group (15.9  ± 11.7 vs 35.3 ± 11.7, p <0.01), but there was a similarity in total follow-up between the groups (40.5 ± 13.3 vs 35.3 ± 11.7, p = 0.094). Converted and unconverted groups did not differ grossly by transplant centre (p = 0.11). Pre-conversion mean Tac CV% was similar in the ER-Tac group compared to the remain on IR-Tac group (30.3 ± 10.2 vs.  29.9 ± 9.8, p = 0.90). The median percentage of medication taken on time (neither missed nor late) as assessed by the MAM-MM was 100% in both groups (p = 0.9).   Thirty-eight percent of those who remained on IR-Tac reported less than 100% adherence (all tacrolimus doses taken on time) over the two years prior to LCO, compared to 41% of those who converted to ER-Tac.   43 Time-to-Event Analyses Influence of Age on Conversion to ER-Tacrolimus  Age at transplant was a strong and independent predictor of ER-Tac conversion (Table 3.3). Each additional year of age more than doubled the chance of conversion, in both the exploratory analyses (HR 2.06, 95% CI 1.62, 2.63, p <0.001), and in the final model (HR 2.54, 95% CI 1.83, 2.54, p <0.001). The relationship between age and conversion varied as a function of time and is strongest soon after transplant. In our final model, the impact of age on conversion declines by three percent with each month after transplantation (HR 0.97, 95% CI 0.95, 0.98, p <0.001). The association between age and conversion was very stable, no change to the strength of the association was seen following the addition of any of the candidate predictors.   Demographic and Baseline Factors   Over the follow-up period, females were approximately four times more likely to convert than males (HR 4.40, 95% CI 1.69, 11.48, p = 0.003), an effect which persisted in the multivariable analysis (HR 3.78, 95% CI 1.35, 10.6, p = 0.01). The combined effects of age and sex on conversion to ER-Tac are visualized in Figure 3.2.  There were no differences between any transplant centres in exploratory analyses (data not shown). Race and other clinical variables assessed at baseline were not associated with conversion (Table 3.3). Significant differences in HLA A, B mismatch and recent history of rejection between the converted and unconverted groups in unadjusted analyses (Table 3.2) were no longer significant once adjusted for age at transplant in the exploratory model and thus were not included in the final model.     44 Contemporaneous Factors   Of the contemporaneous clinical co-variates, Tac CV%, and mean eGFR were both identified as candidate predictors in our exploratory model (Table 3.3). A higher eGFR (ml/min/1.73 m2) showed a trend to increased conversion by a factor of 1.03 (HR 1.03, 95% CI 0.99, 1.07, p = 0.073). The effect for eGFR disappeared in the final model (HR 1.01, 95% CI 0.97, 1.06, p =0.47). The relationship between conversion and Tac CV% also attenuated after adjustment in the final model but to a lesser degree than eGFR (HR 1.04, 95% CI 0.99, 1.10, p =0.13). The self-reported percentage of medications taken on time (neither missed nor late) was not associated with conversion.   3.4 Discussion  This study evaluated the influences that are associated with subsequent conversion to extended-release tacrolimus. Our exploratory model identified age, sex, Tac CV%, and higher eGFR as candidate predictors. The final model demonstrated a strong link for both age and sex with conversion to ER-Tac; the chance of conversion roughly doubled with every increasing year of age, and approximately quadrupled for females. The influence of age on conversion was not constant and reduced as time increased post-transplant. Tac CV% and eGFR were not significant predictors in the final model. We observed similar total follow-up times in the converted and non-converted groups, demonstrating that those who did not convert had ample opportunity to do so; in other words, follow-up time did not bias conversion.   Our primary hypothesis was that older age would predict conversion, but we felt a priori that this would be a marker of convenience rather than a reflection of adherence. This study clearly  45 demonstrated a bias toward conversion with older age. However, the independent association between conversion and female sex suggests that convenience is not the sole driver in the decision to convert patients.   Importantly, female sex and adolescence share in common an association with non-adherence and graft loss.118 Receiving a transplant during adolescence is associated with the highest rates of graft loss compared to younger children and adults.60,62,63 Females have higher graft failure rates than males, including during childhood, and especially when the donor is male.118,364,365 Regarding adherence, the adolescent cohort has non-adherence rates in excess of all other ages.275,278,366 One pediatric study reported non-adherence of only 17% in children younger than 12 years old compared to 54% in adolescents.367  Poorer adherence in females has been demonstrated in adult studies.368-370 In pediatric and adolescent populations adherence appears to be equivocal between the sexes,371-373 including in a recent study of North American pediatric renal transplant recipients.374  Yet, clinicians may still perceive a greater risk of non-adherence in females as a result of more prevalent adult data.   Interestingly, transplant centre was not predictive of conversion in this study. This differs from Dharnidarka et al 2017 who, in a study on post-transplant prescribing, demonstrated that centre specific practices explained the most variance regarding induction therapy.375 Induction therapies may have garnered more interest regarding protocol development from transplant programs (which will direct prescribing at a centre level) due to a mixed evidence base and because induction prescribing occurs during the peri-transplant period for everyone.  In contrast, tacrolimus is essentially universally prescribed, conversion to ER-tac may not be seen as a large  46 prescribing shift, and conversion cases occur sporadically across a broad time frame post-transplant. Thus, ER-Tac prescribing may have not been incorporated into protocols that drive behaviour at a centre level.  Specific to adherence, tacrolimus trough level variability was not an independent predictor of conversion in our final model. Self-reported adherence using the MAM-MM was not associated with conversion and nor were the PMBS or AMBS questionnaires, which detail barriers to adherence, including in exploratory analyses. As expected, given the inherent social desirability bias associated with self-report metrics, Tac CV% of approximately 30% suggested higher non-adherence rates than the median 100% adherence reported in the MAM-MM. The tacrolimus trough CV% rates in our population are consistent with those reported in children elsewhere, although slightly higher than adults.140,332,342 The absence of association between adherence markers and conversion may reflect the challenges that healthcare professionals face in identifying non-adherents; clinician gestalt on this is poor with a gross underestimation of non-adherence.289,292,295,376,377 This difficulty in differentiating based on adherence may also partially explain the dependence on demographic features when deciding who to convert to ER-Tac, especially as MAM-MM and detailed Tac CV% data do not form part of routine clinical practice. Furthermore, other clinical indicators that mark an individual patient as being at higher risk of graft loss were not associated with conversion, including presence of rejection, decline in renal function, and HLA mismatch.113,191,378-380   These findings suggest that ER-Tac conversion decisions were based on an assessment of risk. However, rather than an in-depth, case-by-case analysis using individualized markers of risk,  47 nephrologists appeared to use high-level decision making and rely on older age and female sex as coarse markers to distinguish patients at higher susceptibility of allograft related complications and non-adherence. The findings of our study are supported by Lutfey et al. 2005, who evaluated adherence in patients with diabetes. Lutfey et al. demonstrated that clinicians inferred non-adherence based on age and race, despite these markers not being particularly associated with adherence.381 In addition, clinicians failed to link adherence to less observable but potentially more reliable markers.   This use of readily available information to rapidly intuit conclusions is known as heuristics. Heuristics and automated cognitive processing are powerful tools for decision making in the information dense clinical environment.382,383  Clinicians rely on ingrained pattern recognition to differentiate information into manageable categories. However, as is seen here, this reliance on pre-conceived notions based on broad demographic categories can result in less sophisticated decisions, with room for erroneous conclusions.   The reduction of the impact of age as time post-transplant progresses was an interesting finding that may speak more to routines than allograft risk. An adolescent participant may be converted soon after transplant on account of being perceived as high risk. However, clinicians may not consider switching younger participants until they are older. By this time, clinicians and participants may have been reluctant to disrupt well-established medication routines, due to potential negative consequences for adherence.300,384    48 Strengths and Limitations  Given the potential benefits of ER-Tac on adherence and patient preference, it is important to understand whether clinicians are converting the patients that could reap the most benefit. This is the first study to assess factors that influence clinical decision making around ER-Tac prescribing in the renal transplant literature. A major limitation of this study is the lack of parental socio-economic data. Evidence shows that parental income and education can both unconsciously bias clinical decision making.385,386 Socioeconomic status and disruption at home are also key influencers of pediatric adherence.316,325 Thus, the home environment could factor in the decision to convert to once daily tacrolimus. Secondly, despite being a national study with broad inclusion criteria, a relatively low number of people were converted, which limited variable inclusion in our multivariable analysis. Finally, there was no center-specific effect observed in the exploratory analyses, however it would be an important consideration for future studies to assess individual clinician practices, to evaluate the physician’s reasons for conversion on a case-by-case basis, and investigate the level of awareness surrounding biases related to age and sex.    Conclusions   Extended-release tacrolimus in pediatric kidney transplant recipients is focused according to perceived risk of poor allograft outcomes rather than individualized risk stratification. Clinical markers of risk including rejection history, renal function and adherence were not associated with conversion. Perceived risk was shown to be inferred by clinicians using the broad and easily identifiable demographic features of older age and female sex, which we found to be independent predictors of conversion and both associate with poorer graft outcomes. The impact  49 of age reduced as time progressed after transplant, potentially highlighting a reluctance to alter entrenched medication regimens, even as patients tip into a high-risk age bracket.  50 3.5 Figures and Tables            51  52 Table 3.1. Demographic and Baseline Characteristics in Total Study Population and Potential Converters to ER-Tac†          .                       Values are expressed as n (%) or mean ± SD. †Potential Converters to ER-Tac: Excludes participants without opportunity to convert to ER-Tac (Centres without access to ER-Tac; IR-Tac previously taken via gastric tube/nasogastric tube and never as PO IR-Tac tablets; IR-Tac PO liquid and participant age < 5 at most recent visit) ‡All: seven participants received both basilixmab and anti-thymoglobulin, one participant received neither and one participant had missing data. Can Convert: 5 participants received both basilixmab and anti-thymoglobulin, one participant received neither Abbreviations: ESKD – end stage kidney disease, HLA – human leucocyte antigen, IR-Tac – immediate release tacrolimus, ER-Tac – extended-release tacrolimus.    All (n=97) Can Convert† (n=66) Age at transplant (years) 11.35 ± 5.5 13.1 ± 4.7 Male sex 60 (61.9) 40 (60.6) Race    White 59 (60.8) 37 (56.1)  First-Nations 6 (6.2) 4 (6.1)  Other 32 (33.0) 25 (37.8) ESKD etiology    Glomerular 37 (38.1) 29 (43.9)  Non-Glomerular 51 (52.6) 31 (47.0)     Unknown 9 (9.3) 6 (9.1) First transplant 92 (94.9) 61 (92.4) Living donor kidney 52 (53.6) 36 (54.5) Transplant date    2012-2014 35 (36.1) 24 (36.4)  2015-2017 62 (63.9) 42 (63.6) Cold ischaemia time in hours 5.48 ± 4.45 5.15 ± 4.4 HLA A, B mismatch  2.29 ± 1.13 2.11 ± 1.08 HLA DR, DQ mismatch 2.61 ± 1.75 2.2 ± 1.53 Rejection 0-6 months 28 (28.9) 23 (34.8) Tacrolimus as capsule 76 (78.4) 61 (93.9) Induction therapy‡     Basiliximab 75 (78.1) 62 (93.9)  Anti-thymoglobulin  27 (28.1) 8 (12.1) Initial immunosuppression   Tacrolimus (IR) (vs. cyclosporine) 95 (97.9) 66 (100)  Mycophenolate Mofetil  93 (95.9) 64 (97.0)  Prednisone 96 (98.9) 66 (100)  Other 3 (3.1) 2 (3.0)  Unknown 1 (1.2) 0 (0) Converted to ER-Tac 30 (30.9) 30 (44.8)  53  Table 3.2. Patient and Clinical Characteristics according to Conversion Subgroup  Remained on IR-Tac Converted to ER-Tac p-value Baseline Characteristics n = 36 n = 30  Age in years at transplant 12.5 ± 5.2 13.86 ± 3.9 0.22 Male sex 26 (72.2) 14 (46.7) 0.06 White (vs other) 23 (63.9) 14 (46.7) 0.25 ESRD diagnosis    0.36  Glomerular 18 (50) 11 (36.7)   Non-glomerular 14 (38.9) 17 (56.7)   Unknown  4 (11.1) 2 (6.6)  Transplant date   0.41  2012-2014 11 (30.6) 13 (43.3)   2015-2017 25 (69.4) 17 (56.7)  Living donor kidney  22 (61.1) 14 (46.7) 0.35 HLA A, B mismatch 1.83 (1.18) 2.43 (0.86) 0.02 HLA DR, DQ mismatch (n = 22, 13) 2.05 (1.65) 2.46 (1.33) 0.42 Induction therapy     Basiliximab 34 (94.4) 28 (93.3) 1  Anti-thymoglobulin  3 (8.3) 5 (16.7) 0.45 Baseline PMBS score (n= 29, 23) 39.8 ± 9.3 38.3 ± 10.1 0.60 Baseline AMBS score (n= 24, 21) 39.3 ± 9.2 38 ± 9.9 0.67 Contemporaneous Characteristics†  n = 36 n = 29  Transplant to latest follow up visit in months 35.3 ± 11.7 40.5 ± 13.3 0.094 Transplant to LCO in months  35.3 ± 11.7 15.9 ± 11.7 <0.01 Recent rejection 10 (27.8) 16 (55.2) 0.05 Tacrolimus as Capsule (n = 36, 28) 34 (94.4) 26 (92.8) 1.0 Tacrolimus trough CV% (n = 36, 28) 29.9 ± 9.8 30.3 ± 10.2 0.90 MAM-MM % ‡ (n = 36, 27) 100 (98.6, 100) 100 (99.4, 100) 0.90 eGFR (ml/min/1.73 m2)    65.5 ± 17.8 66.0 ± 14.0 0.91 Values are expressed as n (%), mean ± SD or median (IQR). Where data for the full analysis set is unavailable, covariate specific n is documented in the row header. †Contemporaneous Characteristics are calculated at latest conversion opportunity (LCO) (Tacrolimus as capsule, months transplant to LCO) or represent the mean data of (max) two years prior to LCO (rejection, CV%, MAM-MM, eGFR). LCO is defined as latest study visit prior to ER-Tac conversion or, if not converted, latest study visit. If less than two years of follow-up at censorship, all data >31 days post-transplantation is included. ‡MAM-MM %: self-reported adherence- percentage of tacrolimus doses reported taken on time (neither missed nor late). Abbreviations: ESKD – end stage kidney disease, PMBS - parent medication barrier scale, AMBS - adolescent medication barrier scale, eGFR - estimated glomerular filtration rate, CV% - percentage coefficient of variance - represents tacrolimus trough level variability, HLA – human leucocyte antigen, MAM-MM - medication adherence measure medication module.    54 Table 3.3. Cox Proportional Hazards Regression Assessing the Influence of Co-Variates on Conversion to ER-Tacrolimus, Exploratory and Multivariable Analyses Exploratory Model: adjusted for age and age:time interaction only. Final Model: Adjusted for candidate predictors (p<0.1) identified in exploratory model.  *p<0.05 ** p<0.01 ***p<0.001. ‡MAM-MM %: self-reported adherence- percentage of tacrolimus doses reported taken on time (neither missed nor late). Abbreviations: LCO – latest conversion opportunity, HR – hazard ratio, CI – confidence interval, ESRD – end-stage renal disease, DD - deceased donor, LD – living donor, Percentage, PMBS -  parent medication barrier scale, AMBS - adolescent medication barrier scale, eGFR: estimated glomerular filtration rate, CV%: percentage coefficient of variance - represents tacrolimus trough level variability, HLA – human leucocyte antigen, MAM-MM - medication adherence measure medication module Exploratory Model Final Model HR (95% CI) p-value HR (95% CI) p-value Age at transplant  2.06 (1.62, 2.63) <0.001*** 2.54 (1.83, 3.54) <0.001*** Age at transplant (years):Time from transplant (months) interaction  0.97 (0.96, 0.98) <0.001*** 0.97 (0.95, 0.98) <0.001*** Age Adjusted Model     Female sex (reference male)  4.40 (1.69, 11.48) 0.0025** 3.78 (1.35, 10.6) 0.011** Non-white race (reference white)  1.16 (0.49, 2.75) 0.74 - - Non-glomerular disease (reference glomerular) 1.72 (0.70, 4.25) 0.23 - - Transplant date (reference 2012-2014) 0.45 (0.16, 1.30) 0.14 - - DD Kidney (reference LD) 0.54 (0.20, 1.44) 0.22 - - HLA A, B mismatch  1.03 (0.73, 1.46) 0.86 - - HLA DR, DQ mismatch  1.07 (0.59, 1.96) 0.82 - - Baseline PMBS 1.04 (0.98, 1.11) 0.18 - - Baseline AMBS   0.03 (0.94, 1.12) 0.55 - - Induction therapy      Basiliximab 0.51 (0.10, 2.68) 0.43 - -  Anti-thymoglobulin  1.64 (0.56, 4.82) 0.37 - - Tacrolimus Preparation (reference capsule) 2.18 (0.28, 17.11) 0.46 - - Recent rejection (reference no rejection) 0.64 (0.28, 1.46) 0.29 - - Tacrolimus trough CV%  1.05 (0.99, 1.10) 0.10 1.04 (0.99, 1.10) 0.13 MAM-MM % ‡ 0.95 (0.75, 1.20) 0.65 - - eGFR 1.03 (0.99, 1.07) 0.073 1.01 (0.97, 1.06) 0.47  55 Chapter 4.  The Impact of Extended-Release Tacrolimus on Adherence Outcomes in Pediatric Kidney Transplantation  4.1 Introduction  The pediatric population is more vulnerable to non-adherence than adults with higher rates observed particularly in adolescents.275,326 In one systematic review 44% of adolescents were non-adherent to medication compared to 22% in younger children.275 Non-adherence is also a major cause of graft failure in children and has been associated with both late and chronic rejection.110,140,193,275,331,332,341,387 Therefore, a comprehensive understanding of adherence in the pediatric cohort is critical.  Beyond adolescent age, additional determinants of non-adherence include psychological comorbidities, challenging relationships with parents and the assumption of responsibility over complex medication regimens.279,280,302,307,308,314,316 Although girls have a higher risk of transplant failure,118,364,365 on the whole prior pediatric studies show no difference in adherence between males and females, including in kidney transplant recipients.371-374   Forgetfulness and scheduling issues have also been identified as major reasons why children and adolescents do not take their medication correctly.291,303,316 Reduced dosing frequency is associated with better adherence, potentially through minimizing the number of events at risk of being forgotten.320,321 ER-Tac, taken once rather than twice daily, has been associated with a 36% improvement in tacrolimus adherence at 6 months post-conversion in adult kidney  56 transplant recipients, but the impact of ER-Tac on adherence in children following kidney transplant remains unclear.270  This is the first multi-centre analysis of medication adherence and ER-Tac in pediatric kidney transplant recipients. The aim of this study is to assess factors associated with tacrolimus adherence in the Canadian pediatric kidney transplant population, and to evaluate the impact of ER-Tac conversion on adherence. We hypothesize that adherence will be greater in those who converted to ER-Tac compared to those who remained on the immediate release formulation.   4.2 Methods  Routine follow-up of scheduled visits closed for all participants on 2020-06-01. Anniversary data from the cohort with long-term follow up was closed for this analysis on 2021-01-20.    Definition of Start Date  The impact of tacrolimus formulation was assessed by comparing the difference in outcome in the year before start date to the year after start date between converters and non-converters. For those who were converted to ER-Tac, start date was the first visit when ER-Tac was prescribed. For those who did not convert, there is no equivalent time post-transplant that can act as a meaningful pseudo-conversion date around which to compare a change in outcome. Transplant date cannot act as an equivalent given the lack of a before time-interval and bias regarding unexposed survival time.    57 Balancing the distribution of time 0 in non-converters to converters through random matching has been shown to reduce potential survival bias.388  Converters were excluded from this analysis  and unable to donate a start date if they had less than three tacrolimus trough levels (converted almost immediately after transplant) or if they had inadequate data post-conversion to calculate Tac CV% (converted at their penultimate or final visit). Non-converters were randomly allocated a start date from a pool of conversion dates that permitted at minimum three tacrolimus trough levels in the year before and after.   Outcome Variables The primary outcome for adherence was the difference in Tac CV% during the post-start compared to the pre-start window.332,334 Secondary endpoints included difference in self-reported tacrolimus adherence, also calculated for the year before and after start date, and serological and self-reported adherence in the early (first year after transplant) and late (beyond the first year) post-transplant periods.  Tac CV% was analyzed as a continuous variable. Self-reported adherence was assessed using MAM-MM,311,357 and was analyzed as a binary variable – 100% vs. less than 100% adherence. Self-reported adherence between pre- and post- start date could remain the same, improve from <100% adherent to 100% adherent, or deteriorate from 100% adherent to <100% adherent. Therefore, difference in self-reported adherence from the pre- to post-start period was an ordinal variable with three levels – deteriorated, unchanged, improved.   Co-Variates  Tacrolimus formulation and its impact on adherence was the main exposure of interest, defined as having taken ER-Tac at any time point post-transplant. Participants were included in the ER- 58 Tac group even if they were subsequently re-converted to IR-Tac. Demographic (age, race, sex), peri-transplant (ESKD diagnosis, transplant history, cold ischemia time, donor type) and post-transplant (conversion to ER-Tac, AMBS and PMBS scores, adherence measures, medication knowledge, eGFR and rejection) confounders that may influence adherence were included in this analysis. Children were defined as knowledgeable regarding their medication if they were able to recall the correct medication independently and without parental support more than 50% of the time when asked as part of the MAM-MM.  Tac CV%, medication knowledge, MAM-MM adherence and eGFR slope were calculated for early, late post-transplant periods as well as for the year before start date. Rejection episodes included any history of rejection prior to start date. Total PMBS and AMBS scores from fully completed baseline questionnaires were used in analyses on early adherence and the mean scores of subsequent fully completed questionnaires (1 year onwards) were used to assess association of barriers with late adherence. For the analysis comparing adherence outcomes before and after conversion, the most recent completed AMBS or PMBS prior to start date was used.   Statistical Analysis  Statistical analyses were performed using R (Version 4.0.3).362 Categorical data were presented as frequency and percentage. Continuous data were presented as mean and standard deviation, or median and interquartile range (IQR). t-test and chi-squared were used for simple between group comparisons. All statistical tests were two-sided. For all analyses, statistical significance was set at p<0.05.   59 Linear regression assessed the influence of covariates (excluding tacrolimus formulation) on Tac CV% across the early and late post-transplant periods; logistic regression was used for assessing the binary outcome of self-reported  MAM-MM adherence (100% or <100% medications taken and taken on time). Candidate predictors were selected for inclusion into the multivariable model based on univariable associations (p<0.2). Secondary to non-normally distributed residuals, the y variable (Tac CV%) was log-transformed, and exponentiated betas are reported.  Transplant centre was included as a random effect in all multivariable models. Intra-class correlation was reported for each multivariable model and describes the proportion of residual variance explained by the effect of centre.   Linear and ordered logistic regression models were then employed to analyze covariate influence on change in Tac CV% and change in self-reported adherence from the year prior to the year after start-date. No log-transformation of the y variable was required for the change in Tac CV% in this model. For all multivariable models assessing the impact of ER-Tac pre- and post- start date, tacrolimus formulation was included as an obligate predictor.   Co-linearity was assessed for each multivariable model using variance inflation factors (VIF). For all models included in this analysis, no VIF exceeded 1.5.   Power  The pre- and post-conversion  difference in Tac CV% between converters and non-converters was the primary outcome of all three aims of this thesis, and upon which the overall study was  60 powered. Based on preliminary data from Winnipeg where overall Tac CV% was 30% ± 13% (mean ± SD), Tac CV% was shown to be approximately 10% higher in a cohort with late TCMR (36%) versus those who were TCMR free (25%). Based on this data, it was decided that an 8-10% change in Tac CV% would be clinically meaningful. Using an anticipated total study population of 120 and a conversion rate of approximately 50%, it was calculated that to detect an 8% difference in Tac CV% between converters and non-converters (student’s t-test) with 90% power, 56 participants would be required to convert. In actuality, a total study population of 101 were recruited and transplanted with thirty-two patients converted to ER-Tac, which enabled us to detect an 8% difference in Tac CV% with 80% power. However, more complex analyses in this study may be underpowered due to the limited number of converters.   4.3 Results  Of 101 patients who were enrolled and transplanted, 95 patients had more than one year of follow-up post-transplantation and were included in this analysis. Three participants experienced early graft failure due to disease recurrence (n=2) and thrombosis (n=1), and three were lost to follow-up within the first year (Figure 4.1).   Baseline characteristics are detailed in Table 4.1. This cohort, which is fairly typical of a pediatric kidney transplant population, were more likely to be male (62.1%), have received a living donor transplant (54.7%) and have non-glomerular disease (51.6%). The mean age at transplant was 11.4 ± 5.5 years. Overall, participants were followed for a mean of 3.8 ± 1.3 years. Following random start date matching, mean follow up between transplant and start date  61 for converters (1.39 ± 0.77 years) and non-converters (1.58 ± 1.15 years) were equivocal (p = 0.38) demonstrating successful balancing of the groups with regards to time after transplant.   Per year of follow-up, a mean of 17.9 ± 9.7 tacrolimus trough levels were captured per participant. Mean Tac CV% calculated across the entirety of the follow-up period was 35.8% ±10.2%. Participants completed on average 13.2 MAM-MMs each during their post-transplant period. The majority of individuals (64%) self-reported 100% adherence for all MAM-MMs that they completed post-transplant. The lowest self-reported adherence recorded on a single MAM-MM was 88.4%.  Adherence Over Time  Figure 4.2 depicts changes in adherence over follow-up time post-transplant.  Annual Tac-CV% was 30.4% ± 10.6% in the first year following transplant and 32.9% ± 13.8% for beyond the 5th year post-transplant. The percentage of the cohort reporting 100% adherence is 75% during the first year compared to >85% for all later years before falling to 75% after 5 years. Neither Tac CV% nor the percentage of the cohort reporting 100% adherence differed significantly between the early and late post-transplant windows (p = 0.21 and 0.83 respectively).    Factors Influencing Early Adherence For early post-transplant Tac-CV%, being knowledgeable regrading tacrolimus medication was associated with an 18% reduction in Tac CV% (Exp β 0.82; 95% CI 0.77, 0.93; p= 0.004) (Table 4.2). This persisted in the adjusted model (Exp β 0.84; 95% CI 0.72, 0.98; p = 0.04). No other  62 covariates were associated with early Tac CV% in unadjusted or adjusted analyses including age, sex, barriers to medication and self-reported perfect adherence in the first year after transplant.   When considering self-reported 100% adherence in the first year, every increasing year of age at transplant was associated with a 2% fall in self-reported 100% adherence (OR 0.98; 95% CI 0.97, 1.00; p = 0.05) (Table 4.2). Proficient knowledge regarding medication was also associated with a fall in self-reported perfect adherence by a factor of 0.24 (OR 0.76; 95% CI 0.64, 0.90; p = 0.002). Neither persisted as significant effects in the multivariable analyses. Transplant centre had no discernable impact on Tac CV% or self-reported adherence in the first year following transplant.   Factors Influencing Late Adherence  No factors were associated with late Tac CV% (Table 4.3). Female sex, prior Tac CV% and self-reported 100% adherence within the first year were identified as candidate predictors in the unadjusted analyses (p<0.2) but did not reach significance and were also not associated with Tac CV% in the adjusted model.   For self-reported 100% adherence during the late post-transplant period, only prior self-reported 100% adherence was predictive (Unadjusted: OR 1.45; 95% CI 1.19, 1.76; p <0.001; Adjusted: OR 6.13; 95% CI 2.00, 20.09; p = 0.002) (Table 4.3). No other factors were associated with self-reported adherence in unadjusted or adjusted analyses.    63 As with early adherence, transplant centre did not associate with late Tac CV% or late self-reported adherence (Table 4.3).   The Impact of ER-Tac Conversion on Adherence  The impact of ER-Tac conversion on adherence was assessed by comparing adherence between converters and non-converters one year before and after start date. Eighty-six participants had a calculable tacrolimus trough CV% in both pre and post-start date periods. Mean tacrolimus CV% in converters fell 1.1% (31.3% to 30.2%) compared to a fall of 0.5% in non-converters (28.9% to 28.4%) (p = 0.90).   Seventy-one participants completed at least one MAM-MM in the year prior and the year following start date. Eight (11%) people deteriorated from perfect to imperfect adherence and seven (10%) improved from imperfect to perfect self-reported adherence. Fifty-six people reported stable adherence, 51 (71%) reported 100% adherence during both intervals and five (7%) reported less than 100% adherence during both pre- and post- start date windows. Of the converters, four reported improved adherence, 21 remained stable, and only one individual reported a deterioration in adherence. This was not significantly different from those who remained on IR-Tac, three reported an improvement, 35 remained stable and seven reported a deterioration (p = 0.20).   ER-Tac did not associate with an improvement in pre- to post- Tac CV% in either unadjusted or adjusted regression models (Table 4.4). However, conversion to ER-Tac was associated with an improvement in self-reported MAM-MM 100% adherence that trended toward but did not reach  64 significance in the unadjusted analysis (OR 3.12; 95% CI 0.93, 12.95; p = 0.08) and was not significant in adjusted analyses (OR 2.89; 95% CI 0.60, 13.89, p = 0.18).   Perfect MAM-MM self-reported adherence in the year prior to start date was associated with a large reduction in Tac CV% in the year after start date (β -15.69; CI -27.87, -3.51 p = 0.01), which persisted in adjusted analyses (β – 12.67, 95% CI -24.51, -0.84; p = 0.05) (Table 4.4). Every increasing year of age at start date was associated with an increase in Tac CV% of less 1% (Unadjusted: β 0.72 95% CI 0.01, 1.43 p = 0.05; Adjusted: β 0.79, CI -0.04, 1.63, p = 0.08). No factors including transplant centre were associated with a pre to post change in self-reported adherence.    Patient Reported Reasons for Self-Reported Non-Adherence  Thirty-four individuals reported less than 100% tacrolimus adherence across a total of 65 MAM-MMs. When asked to select when they are most at risk of missing their medications, in 34 (52%) of MAM-MMs participants reported that they do not miss their medication. Of the remainder, 19 (29%) reported missing morning medication and 9 (14%) reported missing evening medication (χ2 = 3.57, p = 0.06). Three (5%) MAM-MMs reported multiple times per day as their most likely time to miss and were not included.    When asked about the factors that make it hard to take medications, 9 (14%) reported either no reason or reported multiple reasons. Thirty-two (49%) reported that they don’t miss, followed by 20 (31%) reporting unintentional non-adherence (forget, interferes with activity, wasn’t home). Three (5%) people reported issues with tablets (hard to swallow, dislike taste) and one person  65 (2%) reported refusing to take secondary to defiance. No MAM-MMs reported missed medications due to not being able to afford prescriptions, adverse effects or because they didn’t feel their medications were required. Not including questionnaires that reported “don’t miss medication”, individuals were significantly more likely to report unintentional non-adherence as the reason for missing medication (χ2 = 27.25, p = <0.001).   4.4 Discussion  In this study, we evaluated the influence of clinical factors, and of conversion to ER-Tac, on adherence to tacrolimus in children following kidney transplant. Tac CV% and rates of self-reported 100% adherence were stable over time, not differing significantly between early and late post-transplant periods. Being knowledgeable regarding medication  was associated with lower Tac CV% but trended toward an association with poorer self-reported adherence. No factors were associated with Tac CV% beyond the first year and only prior self-reported 100% adherence predicted ongoing adherence. Although not associated with Tac CV% in early or late adherence analyses, perfect self-reported adherence during the pre-start window did associate with a large fall in Tac CV% in the year post-start. No co-variates were associated with a change in MAM-MM adherence across the pre- and post-start intervals. Of particular note, converting to ER-Tac did not improve subsequent tacrolimus CV% or MAM-MM self-reported perfect adherence compared to non-converters.  A closer examination of behaviour in self-disclosed non-adherents demonstrated a trend for children to miss their morning medication. The dominant reason for missing medication or taking it late was forgetting or scheduling issues.    66 A high proportion of participants reported themselves as perfectly adherent in this study, 64% of participants reported 100% taking and timing adherence for every MAM-MM completed during their follow-up period. This may be a reflection of the MAM-MM where participants are only asked to report on the prior seven days adherence.357 It is plausible that participants have taken all of their medication correctly in the last week, and that the MAM is failing to capture a more longitudinal picture of adherence or that this population is very adherent in general. Moreover, the seven days that are assessed are, by definition, immediately before study visits, which may result in a white-coat effect and falsely augment adherence during this time period. Additionally, social desirability bias will impact any self-reporting measure. Mean tacrolimus trough level CV% of 35.8% ± 10.2% in this study was in line with reports from other pediatric kidney transplant studies, which range from 22-44%.287,332,333,342,352,389 This is higher than the reported mean range in adults of 16-21%,334,335,342 which may reflect a differences in tacrolimus pharmacokinetics between children and adults as opposed to purely being a factor of adherence.333,390     ER-Tac was designed with the purpose to augment adherence, and indirectly to improve clinical outcomes through reduction of non-adherence related sequelae such as late rejection and graft loss. This study did not see an improvement in self-reported adherence in the year following conversion to ER-Tac, which may be as a result of the excellent adherence we observed at baseline. Baseline self-reported non-adherence in the year prior to start date was low in both converter (12%) and non-converter (13%) groups. The only other study of ER-Tac and adherence in pediatric kidney transplant recipients by Pape et al 2011 also reported low rates of baseline non-adherence (15%) and observed no difference following conversion to ER-Tac.352 In  67 contrast, Quintero et al who studied ER-Tac conversion in children following liver transplant reported baseline non-adherence in 58% of the cohort, and reported a 30% improvement following conversion.353 Likewise, in adult studies baseline rates of non-adherence were high ranging from 24% - 75% and ER-Tac was associated with improved adherence.265-267,269,270   Cassuto et al. demonstrated that the utility of ER-Tac may depend on the severity of non-adherence.355 For those with minor adherence issues, adherence remained unchanged in 74%, whereas 67% of those classified as non-adherent experienced had improved adherence after conversion. Therefore, ER-Tac may not improve adherence in everyone but have particular efficacy in a subset who are already struggling to take their medication. Based on results from Aim 1 of this thesis, converters were not selected based on non-adherence,391 which may explain the lack of association seen between ER-Tac and adherence outcomes in this study. Future studies assessing the efficacy of ER-Tac should aim to identify and convert non-adherent patients.   In this study there was also no association between conversion to ER-Tac and reduction in Tac CV%, a finding similarly observed by Pape et al., 2011.352 In adults, once daily tacrolimus resulted in reduction in Tac CV% in some studies,356,392,393 but not in others.394-396 Inherent differences have been demonstrated in the pharmacokinetics of ER-Tac vs IR-Tac and their response to different CYP3A5 genotypes that may partially explain the discrepant change in CV% seen across studies rather than purely changes in adherence.356,392,393 With the exception of Pape et al,352 no studies examined ER-Tac influence on both tacrolimus CV% and self-reported adherence within the same analysis.   68  Adult studies report that the evening dose is more likely to be missed and is the preferred dose to be dropped.269,350,397 However, in pediatric cohorts including this study, findings suggest a trend toward being more adherent with evening medication compared to morning.297,303 Despite this, when reducing medications to once daily, it may be natural to continue the first dose of the day and discontinue the evening medication. If children are more likely to be non-adherent in the morning, a frontloading of medication to this time may not improve adherence despite the reduction in tablet burden.   Overall, non-adherence is challenging to measure and to predict, only medication knowledge was associated with early adherence and only previous good adherence predicted continued adherence. The impact of previous adherence on future behaviour is very well-established, prior non-adherence predicts non-adherence with immunosuppressant medication and acts as a relative contra-indication to transplant.398  As with other studies, forgetfulness/issues with planning was a major reported cause of non-adherence.291,303,305 Predicting unintentional lapses in perfect behaviour that may be idiosyncratic and affected by a wide range of situation specific factors may be why adherence is difficult to predict.   Patients who have a better understanding of both their condition and medication regimen tend to have better adherence than those who have limited knowledge for their medication or why it is required.309-313 In line with this, we found that medication knowledge was associated with an improvement in Tac CV%  during the first year, which persisted in the multi-variable model. However, over the same period children who were knowledgeable regarding their medication  69 reported lower self-reported adherence scores compared to those who relied on their parents or their coach to remember the medication. This association with self-reported adherence, however, did attenuate in the multivariable model. These findings suggest that children who are more cognizant of their medication may pay more attention if a medication is taken late or missed, whereas children who lack knowledge regarding their medication may continue to perceive their adherence as good because they don’t heed missed or late doses as significant and subsequently do not commit these non-adherent events to memory.   Adolescent age has long been purported to be a key factor associated with medication non-adherence.275,281,323,354 However, there are exceptions where studies find no effect of age on adherence or adherence barriers303,399-401 and one study found improved adherence in the adolescent cohort compared to younger children.284 This study found no association between age at transplant and early or late adherence in multivariable analyses. Each increasing year of age at start date trended toward increasing tacrolimus CV% in the year following start date by 0.72% but this did not quite reach significance in the multivariable model. It may be that this is a genuine effect and a lack of power is precluding us from seeing the effect of age in the multivariable model. However, considering the absence of an association between age and adherence in the early and late adherence model and the lack of significance here, it cannot be stated that there is a relationship between age and adherence in this study.   Self-reported adherence and tacrolimus CV% were not co-associated in early or late adherence models but reporting perfect adherence during the year pre-start was associated with a fall in Tac CV% in the post-start year. This suggests that tacrolimus CV% may be affected by self-reported  70 adherence in the recent past, which are not seen when self-reported adherence and Tac CV% are averaged over the longer period of the late adherence window. Other studies also report a lack of contemporaneous agreement between reported self-adherence and Tac CV%.334 This study suggests there may be a lag effect between self-reported adherence and improvements in serological adherence.   Strengths and Limitations  This study is the first multi-centre study to assess adherence following conversion to ER-Tac in the pediatric kidney transplant population. It is also one of the only studies to compare converters to a non-converter control group. This study is limited by the lack of inclusion of psycho-social predictors. A range of variables from socioeconomic status, parent-child relationships, the home environment and recipient mental health have all been implicated in increasing the risk of non-adherence.277,317 278,280,316 402 It would  also have been important to assess patient preference alongside patient adherence to determine if, as in other cohorts, ER-Tac is preferred over IR-Tac.  Furthermore, there were several instances where factors trended toward significance and given the low number of converters, this study may have been underpowered to detect smaller effect sizes that may still remain clinically relevant.   Conclusions  In brief, this study did not find ER-Tac to be superior to IR-Tac in terms of Tac CV% or self-reported adherence.  A potential explanation lies in the high rates of adherence observed in this  cohort at baseline.  Additionally,  we did not find a clear relationship between age and  71 adherence, which differs from the prevailing literature.   In agreement with other studies, patients were most likely to report  non-adherence secondary to forgetfulness.  72 4.5 Figures and Tables     73  74 Table 4.1. Cohort Characteristics  All (n=95) Demographic/Baseline  Age at transplant (years) 11.4 ± 5.5 Male sex 59 (62.1) Race   White 58 (61.0)  First-Nations 5 (5.3)  Other 32 (33.7) ESKD etiology   Glomerular 37 (38.9)  Non-Glomerular 49 (51.6)     Unknown 9 (9.5) First transplant 90 (94.7) Living donor kidney 52 (54.7) Transplant date   2012-2014 34 (35.8)  2015-2017 61 (64.2) Cold ischaemia time in hours 5.42 ± 4.49 HLA A, B mismatch  2.3 ± 1.1 HLA DR, DQ mismatch 2.6 ± 1.7 Induction therapy†   Basiliximab 74 (77.9)  Anti-thymoglobulin  27 (28.4) Initial immunosuppression  Tacrolimus (IR) (vs. cyclosporine) 94 (98.9)  Mycophenolate Mofetil  92 (96.8)  Prednisone 95 (100)  Other 3 (3.2) Follow-up   Follow-up time (y) 3.8 ± 1.3 Converted to ER-Tac 32 (33.7) Rejection      45 (47.4)  Knowledgeable re. Medication 57 (60) Tacrolimus CV%  35.8 ± 10.2 MAM % adherence  100 (99.4, 100) MAM % reported 100% adherent  61 (64) AMBS total score  37.8 ± 9.8 PMBS total score  36.8 ± 9.0  Values are expressed as n (%), mean ± SD or median (IQR). †Seven participants received both basilixmab and anti-thymoglobulin, one participant received neither. Contemporaneous co-variates are calculated across the entire follow-up period.  Abbreviations: ESKD – end stage kidney disease, HLA – human leucocyte antigen, IR-Tac – immediate release tacrolimus, ER-Tac – extended-release tacrolimus.  75 Table 4.2. Linear (Tac CV%) and Logistic (MAM-MM SR 100% adherence) Unadjusted and Adjusted Regression Models Assessing the Influence of Co-Variates on Early Tacrolimus Adherence within the First Year after Transplant.     Tac CV%   Unadjusted Adjusted  Exp β (95% CI) Std Error p-value Exp β (95% CI) Std Error p-value Age at transplant in years 0.99 (0.98, 1.00) 1.00 0.11 1.00 (0.98, 1.01) 1.01 0.53 Non-white race (ref white) 0.96 (0.83, 1.10) 1.07 0.55 - - - Female sex (ref male) 1.06 (0.92, 1.22) 1.07 0.44 - - - Non-glomerular disease (ref glomerular) 1.08 (0.93-1.24) 1.08 0.31 - - - Had previous transplant  1.23 (0.91,1.67) 1.17 0.18 1.25 (0.93, 1.68) 1.16 0.14 DD kidney (ref LD) 1.02 (0.89, 1.17) 1.07 0.77 - - - AMBS (baseline)  1.00 (1.00, 1.01) 1.00 0.25 - - - PMBS (baseline) 1.12 (0.93, 1.36) 1.10 0.24 - - - Knowledgeable re. medication (year 1)  0.82 (0.77, 0.93) 1.07 0.004** 0.84 (0.72, 0.98) 1.08 0.04* MAM-MM SR 100% adherence (year 1) 1.05 (0.90, 1.23) 1.08 0.51 - - - Tac CV% (year 1) - - - - - - Transplant Centre    ICC: 0.01 MAM-MM SR 100% Adherence  Unadjusted Adjusted  OR Std Error p-value OR Std error p-value Age at transplant in years 0.98 (0.97, 1.00) 1.00 0.05* 0.93 (0.80, 1.06) 1.07 0.32 Non-white race (ref white)  0.97 (0.82, 1.16) 1.09 0.76 - - - Female sex (ref male) 1.05 (0.88, 1.26) 1.09 0.60 - - - Non-glomerular disease (ref glomerular) 1.03 (0.85, 1.24) 1.10 0.79 - - - Had previous transplant 1.06 (0.71, 1.57) 1.22 0.78 - - - DD kidney (ref LD) 0.99 (0.83, 1.19) 1.09 0.95 - - - AMBS (baseline)  1.00 (0.99, 1.01) 1.00 0.94 - - - PMBS (baseline) 1.00 (0.99, 1.01) 1.00 0.59 - - - Knowledgeable re. medication (year 1) 0.76 (0.64, 0.90) 1.09 0.002** 0.27 (0.04, 1.10) 2.25 0.10 MAM-MM SR 100% adherence (year 1) - - - - - - Tac CV% (year 1) 1.00 (0.99, 1.01) 1.00 0.88 - - - Transplant Centre     ICC: 0.09 Adjusted models included transplant centre as a random effect and candidate predictors (p<0.2) identified in unadjusted analyses. For the linear regression model (Tac CV%), exponentiated betas are reported on their original response scale, where their effect is multiplicative.  The response variable was log transformed in order to normalise regression residuals.  *p<0.05 ** p<0.01 ***p<0.001. Abbreviations: Tac CV%: percentage coefficient of variation - represents tacrolimus trough level variability, Exp β: exponentiated beta - anti-log of beta coefficient, CI:  76 confidence interval, DD: deceased donor, LD: living donor, PMBS:  parent medication barrier scale, AMBS: adolescent medication barrier scale, MAM-MM: medication adherence measure medication module, OR: odds-ratio, SR: self-reported.                                      77 Table 4.3. Linear (Tac CV%) and Logistic (MAM-MM SR 100% adherence) Unadjusted and Adjusted Regression Models Assessing the Influence of Co-Variates on Late Tacrolimus Adherence beyond the First Year after Transplant.  Tacrolimus trough CV% Year 1  Unadjusted Adjusted  Exp β (95% CI) Std Error p-value Exp β (95% CI) Std Error p-value Age at transplant in years 1.01 (0.99, 1.03) 1.01 0.39 - - - Non-white race (ref white)  1.03 (0.84, 1.26) 1.11 0.78 - - - Female sex (ref male) 1.17 (0.96, 1.42) 1.10 0.13 1.16 (0.96, 1.41) 1.10 0.13 Non-glomerular disease (ref glomerular) 1.06 (0.87, 1.31) 1.11 0.56 - - - Had previous transplant 0.90 (0.58, 1.38) 1.24 0.62 - - - DD kidney (ref LD) 1.00 (0.82, 1.21) 1.10 0.98 - - - AMBS (year 1+)  1.01 (0.99, 1.02) 1.01 0.27 - - - PMBS (year 1+) 1.00 (0.99, 1.01) 1.01 0.53 - - - Knowledgeable re. medication year 1+) 1.12 (0.93, 1.35) 1.10 0.26 - - - Prior rejection (year 1)  1.06 (0.86, 1.31) 1.11 0.57 - - - Prior eGFR slope (year 1) 0.98 (0.94, 1.02) 1.02 0.39 - - - Prior MAM-MM SR 100% adherence (year 1) 0.84 (0.67, 1.04) 1.12 0.11 0.83 (0.67, 1.02) 1.12 0.09 Current MAM-MM SR 100% adherence (year 1+) 0.92 (0.74, 1.15) 1.12 0.46 - - - Prior Tac CV% (year 1) 1.01 (1.00, 1.02) 1.00 0.15 1.00 (1.00, 1.02) 1.00 0.17 Current Tac CV% (year 1+) - - - - - - Transplant Centre    ICC: 0                                 78 MAM-MM Self-Reported 100% Adherence Year 1   Unadjusted Adjusted  OR Std Error p-value OR Std error p-value Age at transplant in years 0.99 (0.97, 1.01) 1.01 0.18 0.96 (0.86, 1.06) 1.05 0.43 Non-white race (ref white)  1.03 (0.85, 1.23) 1.10 0.78 - - - Female sex (ref male) 1.04 (0.86, 1.25) 1.10 0.69 - - - Non-glomerular disease (ref glomerular) 1.12 (0.92, 1.35) 1.10 0.26 - - - Had previous transplant 1.05 (0.71, 1.55) 1.22 0.81 - - - Knowledgeable re. Medication (year 1+) 0.96 (0.81, 1.16) 1.09 0.71 - - - DD kidney (ref LD) 1.02 (1.10, 1.30) 1.09 0.80 - - - AMBS (year 1+)  0.99 (0.98, 1.01) 1.01 0.26    PMBS (year 1+) 1.00 (0.99, 1.01) 1.00 0.54 - - - Prior rejection (year 1)  0.98 (0.80, 1.19) 1.11 0.83 - - - Prior eGFR slope (year 1) 1.02 (0.98, 1.06) 1.02 0.31 - - - Prior MAM-MM SR 100% adherence (year 1) 1.45 (1.19, 1.76) 1.10 <0.001*** 6.19 (2.04, 20.12) 1.78 0.002*** Current MAM-MM SR 100% adherence (year 1+) - - - - - - Prior Tac CV% (year 1) 0.99 (0.98, 1.00) 1.00 0.09 0.96 (0.91, 1.00) 1.03 0.09 Current Tac CV% (year 1+) 1.00 (0.99, 1.01) 1.00 0.68 - - - Transplant Centre    ICC: 0  Adjusted models included transplant centre as a random effect and candidate predictors (p<0.2) identified in unadjusted analyses. For the linear regression model (Tac CV%), exponentiated betas are reported on their original response scale, where their effect is multiplicative.  The response variable was log transformed in order to normalise regression residuals.  *p<0.05 ** p<0.01 ***p<0.001. Abbreviations: CV%: percentage coefficient of variance - represents tacrolimus trough level variability, Exp β: exponentiated beta - anti-log of beta coefficient, CI: confidence interval, DD: deceased donor, LD: living donor, PMBS:  parent medication barrier scale, AMBS: adolescent medication barrier scale, eGFR: estimated glomerular filtration rate, MAM-MM: medication adherence measure medication module, OR: odds-ratio, SR: self-reported. 79 Table 4.4. Linear (Tac CV%) and ordered logistic (MAM-MM self-reported 100% adherence) unadjusted and adjusted regression models assessing the influence of co-variates on tacrolimus adherence before and after start date including ER-Tac† Change in Tac CV%  Unadjusted Adjusted  β (95% CI) Std Error p-value β (95% CI) Std Error p-value Converted to ER-Tac -0.66 (-9.86, 8.53) 4.62 0.89 -5.89 (-15.72, 3.93) 5.16 0.26 Age at start date in years 0.72 (0.01, 1.43) 0.36 0.05* 0.79 (-0.04, 1.63) 0.44 0.08 Non-white race (ref white)  1.99 (-6.26, 10.23) 4.14 0.63 - - - Female sex (ref male) 1.01 (0.85, 1.71) 4.26 0.81 - - - Non-glomerular disease (ref glomerular)  -2.13 (-10.81, 6.55) 4.36 0.63 - - - Had previous transplant -11.26 (-28.48, 5.97) 8.66 0.19 -11.97 (-28.88, 4.94) 8.88 0.18 DD kidney (ref LD) -2.43 (0.69, 1.37) 4.09 0.55 - - - AMBS pre-start‡§ 0.38 (-0.09, 0.83) 0.23 0.10 - - - PMBS pre-start‡ 0.13 (-0.26, 0.52) 0.20 0.51 - - - Knowledgeable re. medication 1y pre-start) 8.22 (-28.48, 5.97) 4.66 0.08 3.51 (-6.23, 13.25) 5.12 0.50 Had prior rejection 3.27 (-5.67, 12.20) 4.94 0.47 - - - eGFR slope (1y pre-start) 0.04 (-0.12, 0.20) 0.08 0.61 - - - MAM-MM SR adherence (1y pre-start)  -15.69 (-27.87, -3.51) 6.11 0.01* -12.67 (-24.51, -0.84) 6.21 0.05* Transplant Centre    ICC: 0 Change in MAM-MM SR 100% Adherence   Unadjusted Adjusted  OR (95% CI) Std Error p-value OR (95% CI)  Std Error p-value Converted to ER-Tac 3.12 (0.93, 12.95) 1.93 0.08 2.89 (0.60, 13.89) 2.22 0.18 Age at start date in years 0.99 (0.89, 1.10) 1.05 0.83 - - - Non-white race (ref white)  1.12 (0.34, 3.75) 1.83 0.85 - - - Female sex (ref male) 2.21 (0.66, 8.12) 1.88 0.21 - - - Non-glomerular disease (ref glomerular) 0.84 (0.25, 2.85) 1.85 0.68 - - - Had previous transplant - - - - - - DD kidney (ref LD) 2.15 (0.69, 7.97) 1.84 0.21 - - - AMBS pre-start‡ 1.05 (0.98, 1.37) 1.04 0.16 1.06 (0.97, 1.17) 1.05 0.22 PMBS pre-start‡ 1.00 (0.95, 1.05) 1.03 0.93 - - - Knowledgeable re. medication (1y pre-start) 1.24 (0.38, 4.15) 1.83 0.73 - - - Had prior rejection 1.10 (0.29, 4.25) 1.98 0.89 - - - eGFR slope (1y pre-start) 1.00 (0.98, 1.01) 1.01 0.72 - - - Tac CV% (1y pre-start)  1.02 (0.98, 1.06) 1.02 0.42 - - - Transplant Centre    ICC: 0  80  †Start date is defined as conversion date for those who converted to ER-Tac and for non-converters is randomly allocated from the list of conversion dates in order to balance time since transplant in both groups. ‡ Results from most recent AMBS/PMBS questionnaire pre-start §AMBS not included in adjusted analyses secondary to high rate of missing data. Adjusted models included conversion to ER-Tac as an obligate predictor, transplant centre as a random effect and candidate predictors (p<0.2) identified in unadjusted analyses. Abbreviations: CV%: percentage coefficient of variance - represents tacrolimus trough level variability, β: beta coefficient, CI: confidence interval, DD: deceased donor, LD: living donor, PMBS:  parent medication barrier scale, AMBS: adolescent medication barrier scale, MAM-MM: medication adherence measure medication module, SR: self-reported, ICC: intraclass correlation, OR: odds-ratio 81 Chapter 5.  The Impact of Extended-Release Tacrolimus on Graft Outcomes in Pediatric Kidney Transplantation  5.1 Introduction The last 30 years have heralded progressively lower first year rejection and graft loss rates in pediatric kidney transplant.17 However, long term graft outcomes have not improved in line with early gains.160,161  Non-adherence acts as a significant contributor to late graft failure and is a particular problem in adolescents.275   ER-Tac was developed to improve adherence as reducing medication doses is associated with better adherence across a range of conditions including kidney transplantation.267,270,321 Hypothetically, improvements in adherence brought about by ER-Tac will correspond to improvements in graft outcomes. Thus far, ER-Tac has been associated with either equivalent efficacy to IR-Tac in terms of kidney function or in some cases improvements in function.254,262-264,271,272 This study aims to assess the impact of ER-Tac on kidney function in the year following conversion compared to those who did not convert. In addition, we will compare survival to 50% decline in eGFR, rejection and a composite endpoint of rejection, 50% decline in eGFR and graft failure between converters and non-converters.   5.2 Methods  The study population and definition for start date is equivalent to Aim 2 (Chapter 4, Methods). Adherence measures and eGFR slope are defined in Chapter 2.   82  Outcome Variables  The primary endpoint used to assess the impact of ER-Tac on graft outcomes was short term percentage change in eGFR per year (ml/min/1.73 m2/year) in the year prior to and following start date. Secondary endpoints included early percentage eGFR change within the first year, and whether ER-Tac was associated with superior long-term outcomes to IR-Tac. Long term outcomes were assessed using a composite endpoint - 50% persistent decline in eGFR, time to first rejection following start date and graft failure. Fifty-percent decline in eGFR and rejection events post-start were also assessed as individual endpoints.   Co-Variates  Tacrolimus formulation and its impact on adherence was the main exposure of interest, defined as having taken ER-Tac at any time point post-transplant. This was an intention to treat analysis and participants were included in the ER-Tac group even if they were subsequently re-converted to IR-Tac. Demographic (age, sex, race), transplant related (ESKD diagnosis, previous transplant, cold ischemia time, donor type, HLA mismatch) and post-transplant confounders (medication knowledge, rejection history, adherence and conversion to ER-Tac) that may influence outcomes were included in this analysis. Prior rejection is defined as having a rejection episode that required treatment prior to start date. Adherence measures and medication knowledge were calculated for the year prior to conversion.  Being knowledgeable regarding medication was defined as an individual remembering their medication through free recall more than 50% of the time when asked as part of the MAM-MM (as opposed to taking medication secondary to adult prompting/support).  83   Statistical Analysis  Predictors of percentage eGFR decline in the first year after transplant, with the exception of tacrolimus formulation, were analyzed using a linear regression model. Univariable associations were included in the adjusted multivariable models if they met criteria of p<0.1.   A Linear regression model was employed to analyze covariate influence on change in eGFR from the year prior to the year after start-date. Tacrolimus formulation was included as an obligate predictor in this model. As with first year eGFR decline, univariable associations were included in the adjusted multivariable models if they met criteria of p<0.1.   Transplant centre was included as a random effect in all multivariable models and reported using intra-class correlation (ICC).    To analyze time to late graft outcomes post-start date, Cox Proportional Hazards Regression was used. The proportional hazards (PH) assumption was tested using Schoenfeld residuals and Harrel’s Rho and was satisfied. Those who had reached a permanent fall in eGFR of 50% or experienced graft loss after one year but prior to start date (n = 4) were excluded from these analyses. The use of AFT modelling with left censoring of participants who had reached the event of interest before start date was considered but due to limited distributions capable of modelling a time to event of zero in left censored participants, no parsimonious distribution was able to appropriately model the hazard function.   84  As a result of limited number of rejection events post-start date (n= 24) and persistent eGFR decline of 50% (n = 14), multivariable analyses could not be performed for individual endpoints. All analyses were therefore bivariable, adjusted for conversion to ER-Tac. For the composite endpoint (n =36) candidate predictors (p<0.1) were selected for inclusion into the multivariable model. The impact of transplant centre was also included as a random effect. Centre effect in the survival model was assessed using the likelihood ratio test.  As with previous analyses, co-linearity was assessed and found to be acceptable for all multivariable models in this study, the highest VIF was 1.23.   5.3 Results  The study population included is equivalent to that in Aim 2 (Chapter 4, Table 4.1.), 95 participants were included and patients were representative of a typical pediatric transplant population. Forty-five participants (47%) experienced an episode of rejection that required treatment during their follow-up and 24 (25%) experienced an episode of rejection following their start date. In total, 18 experienced a 50% persistent decline in eGFR, of which 14 occurred after start date. Three individuals experienced allograft failure after start-date.   Mean decline in eGFR in the first year was -10.6% but this was highly variable (SD 38.6%). In terms of absolute eGFR, nadir baseline eGFR for the group was 84.5 ± 33.0 ml/min/1.73m2 with a mean first year decline of -7.15 ± 29.0 ml/min/1.73 m2 /year.    85 In unadjusted analyses class II HLA mismatches (β -4.45, CI -8.28, -0.61, p = 0.02)  and non-glomerular kidney disease (β -24.42, CI -35.08, -13.75 , p = <0.001) were associated with a steeper percentage decline in first year eGFR, effects which persisted in adjusted analyses (Table 5.1). Experiencing rejection during the first year was also associated with steeper decline in eGFR, which trended toward significance in exploratory analyses (β -10.82, CI -22.74, 1.09, p = 0.06) and became significantly associated in the multivariable model (β -15.18, CI -27.30, -2.12, p = 0.02). Non-white race was associated with an increase in eGFR in the first year compared to white race including after adjustment (β 12.39, CI -0.34, 24.34, p = 0.06), although neither unadjusted nor adjusted quite reached significance.  Measures of adherence during the first year did not associate with functional decline over the same time period including in univariable analyses. Transplant centre did not affect eGFR decline in the first year (ICC 0.06).   Impact of Conversion on eGFR Decline  Sixty-seven people had >6 months of eGFR data in both annual windows pre- and post-start date. Mean decline in the year pre-start -4.4% (-5.9 ml/min/1.73m2/year) was similar to the year post-start – 3.3% (-4.4 ml/min/1.73m2/year) (p = 0.80). Percentage change in eGFR across the pre- to post- start periods was not influenced by conversion (Unadjusted: β 3.33, CI -13.94, 20.59, p =0.7; Adjusted: β 0.69, CI -15.34, 16.71, p=0.94) (Table 5.2). Absolute change in eGFR for non-converters and converters is visualized in Figure 5.1.   Unlike first year percentage change in eGFR where non-glomerular disease was associated with significantly steeper decline, non-glomerular disease (compared to glomerular disease) was associated with an improvement in eGFR from pre- to post- start date including when adjusted  86 for transplant centre and conversion (β 20.86, CI 4.22, 37.50, p = 0.02) (Table 5.2). No other factors were associated with eGFR change across pre- and post-start intervals.  Understanding the Impact of ESKD Diagnosis on eGFR  It was hypothesized that the impact of ESKD diagnosis on eGFR in the first year may be a factor of body surface area (BSA) and donor-recipient size mismatch given the preponderance of non-glomerular disease in younger transplant recipients. We performed a post-hoc analysis assessing the relationship of BSA at transplant (calculated using the Du Bois formula403) with eGFR decline in the first year, and adjusted for BSA when assessing the relationship between ESKD diagnosis and early eGFR change. BSA and eGFR were not significantly associated (β 9.38, CI -3.1, 21.9, p =0.14) and adjusting for BSA did not influence the relationship for non-glomerular disease (β -24.04, CI -34.9, -13.2, p = <0.001) .   Long-term Allograft Outcomes   Conversion to ER-Tac was not associated with reduced progression to 50% eGFR, rejection post-start date or the composite endpoint (graft loss, rejection and persistent 50% fall in eGFR) (Tables 5.3/5.4). Being a recipient of re-transplantation increased the likelihood of reaching 50% reduction in eGFR by 8-fold (HR 8.42, CI 2.08, 34.07, p=0.003), rejection after start-date by 4-fold (HR 4.33 CI 1.26, 14.84, p = 0.02) and the composite endpoint by 5 fold (HR 5.15, CI 1.58, 16.82, p =0.007). No other factors were associated with eGFR decline. Female sex (HR 2.23, CI 0.99, 5.09 p=0.06) and higher Tac CV% (HR 1.02, CI 0.99, 1.05, p = 0.08) demonstrated trends to increased rejection (Table 5.3). Finally, although not associated with individual endpoints of  87 eGFR decline or rejection, non-glomerular disease was protective from the composite endpoint by a factor of 0.57 (HR 0.43 CI 0.20, 0.90, p = 0.03) (Table 5.4).  5.4 Discussion In brief, this study demonstrated that ER-Tac was not superior to IR-Tac in terms of short-term stabilization of eGFR nor was it superior in the prevention of rejection, decline to 50% eGFR or allograft loss post start-date. On the other hand, having undergone re-transplantation increased the hazard of all three survival endpoints. Non-glomerular disease was the only factor associated with a flattening of eGFR decline between pre and post start date. Non-glomerular disease also reduced the likelihood of reaching the composite endpoint. Female sex and higher Tac CV% both trended toward increasing rejection after start-date. Finally, early eGFR decline in this study was increased by HLA class II mismatch, rejection and non-glomerular disease, and decreased in those of non-white race.   ER-Tac, for the most part, has been shown to be equivocal to IR-Tac in terms of kidney function262,263,352,359,404 and transplant outcomes,253,254,360,405-408 although there are studies that report an improvement in eGFR following conversion.271,272 This study, in line with the greater body of literature, did not show an association between conversion and eGFR, rejection post start or allograft loss. Theoretically, ER-Tac improves outcomes and kidney function through reducing non-adherence and the subsequent risks associated with under immunosuppression such as late rejection and graft failure. However, as we have seen in the analyses for Aims 1 and 2 of this thesis, convenience sampling of those converted to ER-Tac in routine practice does not accurately capture those who are non-adherent and ER-Tac does not improve adherence when  88 baseline adherence is already high. Therefore, our ability to detect the potential effect of ER-Tac on adherence and functional outcomes in this and other similarly designed studies may be reduced.   Moreover, confirmation of similar graft outcomes between IR-Tac and ER-Tac in the long-term remains an important finding. Although it would be clinically exciting if ER-Tac improved allograft outcomes, it is important to remember that ER-Tac and once daily medication in general is much preferred by patients and burdens their daily life less than IR-Tac.260,348 Given the challenges of living with a chronic illness and the difficulties faced by pediatric transplant recipients, a tablet that improves their day-to-day life is valuable if it can provide clinical equivalency.   The relationship between non-glomerular disease and eGFR changed as time after transplant increased. Initially associated with steeper functional decline in the first year, non-glomerular disease was later associated with a stabilization of eGFR in the year following start date compared to the year prior, and reduced the hazard of reaching the composite end-point compared to glomerular disease by more than 50%. Those with glomerular disease do tend to experience more rapid allograft decline and have shorter allograft survival trajectories compared to children with non-glomerular disease due to the risk of glomerular disease recurrence.156,409 However, this does not explain the early detrimental effect of non-glomerular disease seen in this study. Non-glomerular disease occurs more commonly in younger recipients;15,152,410 to explore the impact of donor-recipient size mismatch on early decline in glomerular filtration, we performed a post-hoc analysis adjusting the relationship between ESKD etiology and first year  89 eGFR by body surface area. However, this did not influence the effect of non-glomerular disease on eGFR. A potential alternative explanation may be a higher rate of urinary tract infections in the first year in children with non-glomerular disease, resulting in periods of acute kidney dysfunction that negatively impact the overall eGFR slope in the early post-transplant period.156,411  Early eGFR decline was further influenced by HLA class II mismatch, early acute rejection and race. Acute rejection is a well-known risk factor for functional decline.379 Acute rejection is also a recognized risk factor for late and chronic rejection episodes,191,192,412 and dnDSA110 but rejection prior to start date did not influence later rejection or graft decline in our study. HLA mismatch is also associated with early graft failure and dysfunction in other studies413 However, we were not able to reproduce the association between HLA mismatch and increased risk of later graft failure found by others.106,414,415 It is unclear why non-white race was associated with a less steep decline in eGFR in the first year compared to white race. Prior studies suggest a detrimental rather than protective impact of non-white race on kidney transplant outcomes.416,417 Also, it cannot be attributed to poorer adherence in white children as there were no differences seen between race and early adherence in chapter 4.   Those who had undergone re-transplantation were at significantly higher risk of persistent 50% eGFR decline, biopsy proven rejection following start date and the composite endpoint. Prior transplantation acts as a significant sensitizing event, reducing the pool of compatible donors for subsequent transplantation. Development of non-HLA antibodies and cognate B and T cell memory may also develop in response to the first transplantation, which are not clinically  90 screened for and can contribute to silent sensitization.418-422 When great care is taken to avoid all historical antibodies, the risk of re-transplantation can be somewhat mitigated against, however the overall risk of alloreactivity is higher in re-transplantation.104,110,122  Despite not reaching significance, female sex and higher Tac CV% appeared to be associated with increased rates of rejection following start date, although neither associated with graft loss or 50% fall in eGFR. Female sex is known to associate with higher rates of rejection, the mechanism of which may be founded in increased immunogenicity from estrogen upregulation of immune processes and targeting of H-Y antigens when the donor is male.72,73   Medication non-adherence has been consistently identified as a leading cause of chronic rejection and graft loss in the pediatric cohort.113,330,331,339,341 Poor adherence has been directly implicated in almost 50% of pediatric kidney allograft losses and increases the rate of graft loss 5 fold.193,275,326 The lack of a clear association in this study may be based on excellent overall baseline adherence identified in this cohort and a relatively low rate of events post-start date.   Interestingly, adolescent age did not associate with poorer graft outcomes in this study despite being a well-recognized cause of inferior allograft survival and rejection.354,423 The period of adolescence has been dubbed the “high risk” age window as it has the highest graft failure rates of any age group.60,61 Although adolescents are at an inherently higher risk due to immune changes that last into early adulthood,67,424 graft loss in teenagers is also driven by high rates of non-adherence.275 Age in this study did not associate with either early or late adherence, which may explain the lack of association between age and graft loss seen here.   91  Strengths and Limitations  A major strength in this study is the longitudinal assessment of ER-Tac and graft outcomes where adherence markers also feature as an inclusion criterion. Limitations include power and a limited ability to include multiple predictors in the model secondary to a low number of rejection, eGFR decline of 50% and graft failure events reached. Finally, the assessment of DSA data as an outcome would have been a useful addition given the association between non-adherence and subsequent development of DSA 110,425 and because DSA is strongly associated with later graft failure.378  Conclusions  This study was unable to find that ER-Tac improved short-term kidney function following start date, or survival to 50% persistent decline in eGFR, rejection post-start date or graft loss. This may relate to the lack of association also observed between ER-Tac and adherence measures in this study. Previous transplant was strongly associated with an increased hazard of graft loss, rejection and 50% decline in eGFR.    92  5.5 Figures and Tables   93 Table 5.1. Unadjusted and adjusted linear regression models assessing the influence of co-variates on eGFR slope in the first year after transplant  Adjusted models transplant centre as a random effect and candidate predictors (p<0.1) identified in unadjusted analyses. Abbreviations: CV%: percentage coefficient of variance - represents tacrolimus trough level variability, β: beta coefficient, CI: confidence interval, DD: deceased donor, LD: living donor, MAM-MM: medication adherence measure medication module, SR: self-reported, ICC: intraclass correlation.                eGFR Slope Year 1  Unadjusted Adjusted  β (95% CI) Std Error p-value β (95% CI) Std Error p-value Age at transplant in years 0.69 (-0.31, 1.70) 0.51 0.18 - - - Non-white race (ref white)  11.48 (-0.66, 21.97)  5.70 0.06 12.39 (-0.34, 24.34) 6.53 0.06 Female sex (ref male) 3.94 (-7.56, 15.44) 5.79 0.50 -  - - Non-glomerular disease (ref glomerular)  -24.42 (-35.08, -13.75) 5.37 <0.001* -17.90 (-29.41, -4.63) 6.57 0.006** Had previous transplant 3.47 (-21.43, 28.36) 12.53 0.78 - - - DD kidney (ref LD) 5.49 (-5.74, 16.72) 5.65 0.33 - - - HLA A, B mismatch 2.55 (-2.15, 9.63) 2.52 0.31 - - - HLA DQ, DR mismatch -4.45 (-8.28, -0.61) 1.91 0.02* -3.58 (-7.49, -0.53) 1.85 0.05* Cold ischemia time in hours 1.10 (-0.33, 2.53) 0.72 0.13 - - - Knowledgeable re. medication (year 1) -6.65 (-18.10, 4.80) 5.76 0.25 - - - Had rejection (year 1) -10.82 (-22.74, 1.09) 5.99 0.07 -15.18 (-27.30, -2.12) 6.67 0.02* Tac CV% (year 1) 0.04 (-0.49, 0.57) 0.27 0.88 - - - MAM-MM SR adherence (year 1) 3.32 (-9.50, 16.14) 6.45 0.61 - - - Transplant Centre    ICC: 0.03  94 Table 5.2.  Unadjusted and adjusted linear regression models assessing the influence of co-variates on eGFR slope in the year pre- and post-start date†   †Start date is defined as conversion date for those who converted to ER-Tac and for non-converters is randomly allocated from the list of conversion dates in order to balance time since transplant in both groups. Adjusted models included Converted to ER-Tac as an obligate predictor, transplant centre as a random effect and candidate predictors (p<0.1) identified in unadjusted analyses. Abbreviations: CV%: percentage coefficient of variance - represents tacrolimus trough level variability, β: beta coefficient, CI: confidence interval, ESKD: end-stage kidney disease, DD: deceased donor, LD: living donor, MAM-MM: medication adherence measure medication module, SR: self-reported, ICC: intraclass correlation.            Change in eGFR slope   Unadjusted Adjusted  β (95% CI) Std Error p-value β (95% CI) Std Error p-value Converted to ER-Tac 3.33 (-13.94, 20.59) 8.64 0.70 0.69 (-15.34, 16.71) 8.38 0.94 Age at start date 0.29 (-1.27, 1.86) 0.78 0.71 - - - Non-white race (ref white)  -2.62(-19.33, 14.09)  8.37 0.76 - - - Female sex (ref male) -10.18 (-26.88, 6.53) 8.37 0.23    Non-glomerular disease (ref glomerular)  20.93 (-22.18, 4.12) 8.55 0.02* 20.86 (4.22, 37.50) 8.63 0.02* Had previous transplant 25.68 (-13.89, 65.24) 19.81 0.20 - - - DD kidney (ref LD) -1.20 (-17.79, 15.39) 8.31 0.89 - - - HLA A, B mismatch 0.25 (-7.15, 7.65) 3.71 0.95 - - - HLA DQ, DR mismatch 0.52 (-5.76, 6.80) 3.10 0.87 - - - Cold ischemia time -0.41 (-2.46, 1.64) 1.02 0.69 - - - Knowledgeable re. medication (1y pre-start) 2.53 (-14.74, 19.81) 8.65 0.77 - - - Had prior rejection 4.93 (-12.68, 22.53) 8.81 0.58 - - - Tac CV% (1y pre-start) -0.29 (-0.83, 0.25) 0.27 0.29 - - - MAM-MM SR adherence (1y pre-start) -0.09 (-25.0, 24.82) 12.43 0.99 - - - Transplant Centre    ICC: 0  95 Table 5.3. Cox Proportional Hazard Regression models assessing the influence of co-variates on time to long-term outcomes following start date† (biopsy proven rejection, persistent decline of 50% eGFR and a composite endpoint of rejection, persistent 50% eGFR decline and graft loss) †Start date is defined as conversion date for those who converted to ER-Tac and for non-converters is randomly allocated from the list of conversion dates in order to balance time since transplant in both groups. All associations are bivariable adjusted for conversion to ER-Tac. Multivariable analyses were not performed due to a low number of events per variable. ‡Composite endpoint (events = 36) – adjusted for centre as a frailty term in addition to adjustment for conversion to ER-Tac.  Abbreviations: HR: hazard ratio, CI: confidence interval, DD: deceased donor, LD: living donor, CV%: percentage coefficient of variance - represents tacrolimus trough level variability, MAM-MM: medication adherence measure medication module, SR: self-reported.    Persistent 50% fall in eGFR  Rejection post-start date  HR (95% CI) p-value HR (95% CI) p-value Converted to ER-Tac 1.47 (0.49, 4.39) 0.49 0.76 (0.31, 1.84) 0.54 Conversion Adjusted Model     Age at start date in years 1.03 (0.92, 1.16) 0.63 1.05 (0.97, 1.13) 0.24 Non-white race (ref white)  0.58 (0.18, 1.88)  0.37 1.39 (0.62, 3.11) 0.62 Female sex (ref male) 0.53 (0.16, 1.77) 0.30  2.23 (0.998, 5.09) 0.06 Non-glomerular disease  (ref glomerular) 2.01 (0.61, 6.58) 0.25 0.51 (0.23, 1.16) 0.11 Had previous transplant 8.42 (2.08, 34.07) 0.003** 4.33 (1.26, 14.84) 0.02* DD kidney (ref LD) 0.65 (0.21, 1.99) 0.45 1.26 (0.56, 2.82) 0.57 HLA A, B mismatch 1.39 (0.82, 2.36) 0.22 0.77 (0.53, 1.12) 0.17 HLA DQ, DR mismatch 0.77 (0.52, 1.17) 0.22 0.84 (0.63, 1.12) 0.24 Cold ischemia time 0.97 (0.85, 1.11) 0.70 1.05 (0.97, 1.15) 0.22 Had prior rejection  1.81 (0.59, 5.56) 0.30 1.92 (0.84, 4.40) 0.12 Knowledgeable re. medication  (1y pre-start) 0.57 (0.14, 2.15) 0.40 2.29 (0.74, 7.07) 0.15 Tac trough CV% (1y pre-start) 1.02 (0.98, 1.06) 0.29 1.02 (0.99, 1.05) 0.08 MAM-MM SR adherence (1y pre-start) 3.33 (0.42, 26.10) 0.25 0.46 (0.16, 1.30) 0.14  96 Table 5.4. Cox Proportional Hazard Regression models assessing the influence of co-variates on time to the composite endpoint of biopsy proven rejection, persistent 50% eGFR decline and graft loss following start date†   †Start date is defined as conversion date for those who converted to ER-Tac and for non-converters is randomly allocated from the list of conversion dates in order to balance time since transplant in both groups. All associations are bivariable adjusted for conversion to ER-Tac. Multivariable analyses were not performed due to a low number of events per variable. ‡Composite endpoint (events = 36) – adjusted for centre as a frailty term in addition to adjustment for conversion to ER-Tac.  Abbreviations: HR: hazard ratio, CI: confidence interval, DD: deceased donor, LD: living donor, CV%: percentage coefficient of variance - represents tacrolimus trough level variability, MAM-MM: medication adherence measure medication module, SR: self-reported.     Composite Endpoint  Unadjusted  Adjusted   HR (95% CI) p-value HR (95% CI) p-value  Converted to ER-Tac 0.75 (0.36, 1.53) 0.42 0.69 (0.33, 1.44) 0.32 Conversion Adjusted Model      Age at start date in years 1.05 (0.99, 1.12) 0.12   Non-white race (ref white)  0.80 (0.39, 1.60) 0.52   Female sex (ref male) 1.52 (0.76, 3.04) 0.24   Non-glomerular disease (ref glomerular) 0.54 (0.28, 1.07) 0.08 0.43 (0.20, 0.90) 0.03* Had previous transplant 3.80 (1.30, 11.10) 0.01* 5.15 (1.58, 16.82) 0.007** DD kidney (ref LD) 0.89 (0.46, 1.76) 0.76   HLA A, B mismatch 0.80 (0.59, 1.10) 0.17   HLA DQ, DR mismatch 0.86 (0.68, 1.10) 0.24   Cold ischemia time 1.00 (0.92, 1.09) 0.95   Had prior rejection  1.77 (0.74, 3.51) 0.12   Knowledgeable re. medication (1y pre-start) 1.80 (0.73, 4.39) 0.20   Tac trough CV% (1y pre-start) 1.02 (0.98, 1.04) 0.12   MAM-MM SR adherence (1y pre-start) 0.67 (0.27, 1.70) 0.40   Transplant Centre   χ2 0.04  0.85  97 Chapter 6.  Conclusions and Future Directions   This was the first multi-centre prospective study in pediatric kidney transplant recipients to assess ER-Tac and its role in adherence and allograft outcomes. Furthermore, to our knowledge this is the first study in the kidney transplant literature to assess the patient and transplant related factors that affect clinician decision making in their decision to convert patients to ER-Tac. A major strength of these analyses was the comparison of converters to a control group of non-converters.   In chapter 3, we were able to show that both older age and female sex predicted conversion to ER-Tac. History of rejection, eGFR, adherence and barriers to adherence did not predict conversion. We concluded that age and female sex were acting as demographic proxies to easily identify high risk patients, given that both girls and adolescents experience higher rates of rejection and graft failure. This reflects heuristic thinking, a well-recognized method of clinical decision making in medicine that stems from pattern recognition developed in information dense environments.   In chapter 4, conversion to ER-Tac did not associate with improvements in either Tac CV% or self-reported adherence compared to non-converters. Compared to other studies that do show an effect of ER-Tac, this study reported very low rates of non-adherence in the whole population prior to conversion. Furthermore, non-adherent patients were not those selected to convert, as demonstrated in chapter 3, which may have limited our ability to assess the influence of ER on a  98 non-adherent cohort. In line with other reports, non-adherence in children is mostly inadvertent linked to forgetting.    In chapter 5, conversion to ER-Tac did not associate with a positive change in eGFR or a long-term survival benefit regarding rejection, 50% persistent eGFR decline or graft loss compared to those who remained on IR-Tac. This is consistent with the majority of studies that report stability in functional outcomes following conversion.   Future Directions  The ability to comprehensively adjust for a wide range of co-variates in adjusted analyses in the above studies was hindered by a low number of conversion events and a relatively low number of participants overall despite national recruitment and very broad inclusion criteria. Furthermore, several associations trended toward significance, which may be as a result of low power but should be interpreted nonetheless with caution and require replication. There would be benefit in repeating a multi-centre study focused on longitudinal adherence and ER-Tac, in a similar population with a larger sample size. Furthermore, for some parameters in this study registry level data could be used permitting a better understanding of this issue at a population level.   A further important limitation of this study was a lack of data on parental income, educational level, inter-personal relationships between participants and their families at home, and measures of participant psychological well-being. Repeated studies on adherence and ER-Tac should adjust for such co-variates.   99  Finally, the majority of studies performed thus far on ER-Tac efficacy, including this study, use convenience samples, whereby patients are converted at clinician discretion. This is a decision that we have shown in chapter 3 is not based on adherence in routine practice. Future studies should, therefore, identify and restrict conversion to non-adherent patients, preferably with a control group of non-adherent patients who are not converted in addition to adherent patients who also remain on IR-Tac.                            100 Bibliography  1. Clark ML, Kumar P. Kumar and Clark's Clinical Medicine. 2017. 2. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-612. 3. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med. 1999;130(6):461-470. 4. Levey AS, Coresh J, Greene T, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145(4):247-254. 5. Poggio ED, Wang X, Greene T, Van Lente F, Hall PM. 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Within-patient variability in tacrolimus blood levels predicts kidney graft loss and donor-specific antibody development. Transplantation. 2016;100(11):2479-2485.  127 Appendices Appendix A. Medication Adherence Measure (Medication Module)   128   129    130 Appendix B. Adolescent/Parental Medication Barrier Scale   131  


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