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Description

Background: Composite outcomes, which include mortality and readmission rates, are often used in risk prediction models following hospital discharge when event rates for the primary outcome of interest, mortality, are low. However, increased readmission rates may result in decreased mortality making interpretation of the composite outcome difficult. We assess the usefulness of a composite outcome of post-discharge readmission and mortality as a target outcome in this context.

Methods: This was a secondary analysis of data collected among mothers and their newborn(s) admitted for delivery at two regional referral hospitals in Uganda. Six-week post-discharge mortality (all-cause) and readmission in newborn infants were analyzed using a competing risk framework. The Sub distribution Hazard Ratios (SHRs) were compared across predictor variables to examine the relationship between the two outcomes.

Results: Of the 206 predictors, 81 had a consistent association with both outcomes. These include a higher weight (Mortality SHR: 0.14, Readmission SHR: 0.68) and length of the baby (Mortality SHR: 0.85, Readmission SHR: 0.91). However, 125 variables depicted an association in opposing directions for both outcomes which may be linked to social and financial barriers to care-seeking. These include a travel time to the hospital of greater than 1 hour (Mortality SHR: 1.4, Readmission SHR: 0.28).

Conclusion: While mortality is unequivocally a negative outcome, readmission may be a positive outcome, reflecting health seeking, or a negative outcome, reflecting recurrent illness. This directional dichotomy is reflected to varying degrees within different variables. When using a composite outcome for a prediction model, caution should be exercised to ensure that the model identifies individuals at risk of the intended outcomes of interest, rather than merely the proxies used to represent those outcomes. Identifying predictors with a consistent relationship for both outcomes may yield a more optimized and less biased prediction model for use in clinical care.

Data Collection Methods: All data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada).

Following delivery of newborns, written consent was obtained to complete a structured questionnaire in-person and a follow-up questionnaire over the phone six weeks later broadly categorized into the following five domains: 1) social and demographic, 2) pregnancy history and antenatal care, 3) delivery, 4) maternal discharge, and 5) neonatal discharge.

Data Processing Methods: The initial cleaned data file was created using R version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). Further processing to obtain the final dataset used for analysis including filtering for exclusion criteria, removing predictors with low incidence, and imputing missing values using multiple imputations were also performed in R in the R scripts titled “MBCO_Analysis_Code_SD.R”.

Data Analysis Methods: All analyses were conducted using R version 4.4.0 (R Foundation for Statistical Computing, Vienna, Austria). Libraries used within the script include: tidyverse, Hmisc, reshape2, mice, survival, cmprsk, riskRegression, survminer, ggplot2, ggfortify and gridExtra.

Ethics Declaration: This study was approved by Makerere University School of Public Health (MakSPH) Institutional Review Board (SPH-2021-177), the Uganda National Council of Science and Technology (UNCST) in Uganda (HS2174ES) and the University of British Columbia in Canada (H21-03709). This study has been registered at clinicaltrials.gov (NCT05730387).

Abbreviations:
ANC: Antenatal Care
CI: Confidence interval
HIV: Human immunodeficiency virus
HR: Heart rate
JRRH: Jinja Regional Referral Hospital
LMIC: Low-middle income country
MRRH: Mbarara Regional Referral Hospital
OR: Odds ratio
PNC: Postnatal care
PPD: Postpartum depression
Q1: First quartile
Q3: Third quartile
RR: Respiratory rate
SD: Standard deviation
SpO2: Oxygen saturation

Funding Source(s):
Funding was provided by British Columbia Children's Hospital Research Institute Healthy Starts Catalyst Grant: JMA, ACD. Abhroneel Ghosh also received funding from the Mitacs Globalink Research Internship to conduct research with the team at the Institute for Global Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Study Protocol & Supplementary Materials:
Smart Discharges for Mom & Baby 2.0: A cohort study to develop prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads


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