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Utility of the American College of Surgeons National Surgical Quality Improvement Program Surgical Risk… Al Lawati, Rihab 2016

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UTILITY	OF	THE	ACS	NSQIP	SURGICAL	RISK	CALCULATOR	TO	ACCURATELY	PREDICT	POSTOPERATIVE	OUTCOMES	AFTER	COLON	RESECTION				by		Rihab	Al	Lawati		M.D.,	Sultan	Qaboos	University,	2012			 		A	THESIS	SUBMITTED	IN	PARTIAL	FULFILLMENT	OF	THE	REQUIREMENTS	FOR	THE	DEGREE	OF		MASTER	OF	SCIENCE		in		The	Faculty	of	Graduate	and	Postdoctoral	Studies		(Surgery)				THE	UNIVERSITY	OF	BRITISH	COLUMBIA	(Vancouver)		July	2016			©Rihab	Al	Lawati,	2016				ii	Abstract			BACKGROUND:		Predicting	 potential	 complications	 from	 surgery	 is	 a	 crucial	 step	 to	 aid	 decision	 to	operate.	 The	 American	 College	 of	 Surgeons	 (ACS)	 initiated	 the	 National	 Surgical	Quality	 Improvement	 Program	 (NSQIP)	 which	 collects	 and	 analyses	 patients’	outcomes	 from	 surgery.	 ACS	 NSQIP	 developed	 a	 Surgical	 Risk	 Calculator	 (RC)	 to	predict	 risks	 of	 postoperative	 complications.	 Aim	 of	 this	 study	 was	 to	 assess	 RC	accuracy	for	predicting	complications	in	patients	undergoing	colon	resection.			METHODS:	Validation	 study	with	 secondary	use	of	administrative	data	 conducted	 in	a	 tertiary	care	center.	Patients	who	received	colorectal	procedures	in	our	Enhanced	Recovery	After	 Surgery	 (ERAS)	 program	 from	 November	 2013	 to	 December	 2015	 were	enrolled.	 RC	 predictions	 were	 calculated	 and	 compared	 with	 observed	 NSQIP	outcomes	 within	 30	 days	 follow-up.	 Observed	 versus	 predicted	 outcomes	 were	compared.	 RC	 accuracy	 was	 assessed	 by	 graphical	 examination	 of	 the	 model	calibration	for	outcomes	that	exceeded	50	events.	Predicted	versus	observed	length	of	stay	(days	mean±SD)	was	compared.			RESULTS:	A	 total	 of	 368	patients	were	enrolled.	 RC	predicted	 versus	observed	outcomes	 (n)	were:	 serious	 complication	 40.3	 vs.	 51;	 any	 complication	 60.5	 vs.	 70;	 surgical	 site	infection	 (SSI)	 31.8	 vs.	 51;	 pneumonia	 5.8	 vs.	 15;	 cardiac	 complication	 16.5	 vs.	 9;	urinary	 tract	 infection	 9.8	 vs.	 11;	 venous	 thromboembolism	 4.8	 vs.	 4;	 acute	 renal	failure	16.6	vs.	5;	return	to	operating	room	14.6	vs.	6;	death	4.2	vs.	2;	Discharge	to		iii	facility	20.2	vs.	12.	Good	calibration	was	observed	for	any	complication	and	serious	complications.	SSI	was	underestimated	but	RC	adjustment	by	surgeon	improved	SSI	prediction.	 Length	 of	 stay	 was	 inaccurately	 predicted:	 4.4±1.3	 predicted	 versus	8.6±12.1	days	observed	(p	<0.01,	Wilcoxon	Rank	Sum	Test).			CONCLUSION:	Application	of	RC	in	our	population	closely	predicts	serious	and	any	complication	but	less	 accurately	 predicts	 SSI	 unless	 adjusted	 by	 surgeon	 and	 inaccurately	 predicts	length	of	hospital	stay.	All	outcomes	including	the	above	require	analysis	of	greater	number	of	events	to	permit	final	conclusions	on	RC	use.																														iv		Preface			• Chapter	 two	 –	 Validation	 study	 with	 secondary	 use	 of	 administrative	 data	was	 conducted	 as	 collaboration	 between	 Vancouver	 General	 Hospital	Department	of	Surgery	the	Vancouver	Coastal	Health	Department	of	Clinical	Quality	 and	 Safety	 for	 NSQIP.	 The	 supervising	 committee	 consisted	 of	 Dr.	Garth	 Warnock	 (as	 the	 Principal	 Investigator)	 and	 Drs.	 Kelly	 Mayson	 and	Penny	Brasher	as	Co-investigators.			- Under	the	supervision	of	the	research	committee,	I	was	responsible	of	formulating	 the	 research	 objectives,	 study	 design,	 obtaining	 the	ethical	 approval,	 data	 collection,	 data	 analysis	 and	 writing	 the	manuscript.		- Ms.	Tracey	Hong	(Department	of	Clinical	Quality	and	Safety)	provided	the	study	participants	under	ERAS	protocol	who	underwent	colorectal	procedures.	- Dr.	 Penny	 Brasher	 (Center	 for	 Clinical	 Epidemiology	 and	 Evaluation)	provided	assistance	in	the	project	design	and	data	analysis.		- Mr.	 Markus	 Zurberg	 (Department	 of	 Clinical	 Quality	 and	 Safety)	provided	an	insight	about	the	current	situation	and	how	NSQIP	assists	in	quality	assurance.		- Ethical	approval	was	obtained	from	the	UBC	Clinical	Ethics	Board	(UBC	CREB	No.	H16-00821).													v	8			Table	of	Contents		Abstract	……………………………………………………………………………………………………………	 ii	Preface	…………………………………………………………………………………………………………….	 iv	Table	Of	Contents	……………………………………………………………………………………………	 v	List	Of	Tables	……………………………………………………………………………………………………	 vii	List	Of	Figures	………………………………………………………………………………………………….	 viii	List	Of	Abbreviations	……………………………………………………………………………………….	 ix	Acknowledgments	……………………………………………….………………………………………….	 x	Dedication	……………………………………………………………………………………………………….	 xi	Chapter	1	 Introduction	And	Literature	Survey	………………………………..	 1				1.1	Background	……………………………………………………………………...……………………..	 1							1.1.1		Mortality	and	morbidity	conference	………………….………………………………	 1							1.1.2		UBC	Mortality	and	Morbidity	Tool	………………………………………………..…...	 2							1.1.3		RIisk	assessment	tools/models	…………………………………………………………..	 3							1.1.4		National	Surgical	Quality	Improvement	Program	(NSQIP)	…………..…..….	 3							1.1.5		ACS	NSQIP	Surgical	Risk	Calculator	…………………………………………………….	 4							1.1.6		Enhanced	Recovery	After	Surgery	(ERAS)	…………....……………………………	 6				1.2	Overall	project	hypothesis	………………………………………………………………………	 7				1.3	Overall	project	objective	…………………………………………………………………………	 7	Chapter	2	 Utility	Of	The	ACS	NSQIP	Surgical	Risk	Calculator	To	Accurately	Predict		Postoperative	Outcomes	After	Colon	Resection:	A	Validation	Study	…………………………………………………………………						2.1	Methods	………………………………………………………………………………………………….	 8						2.1.1	Study	design	……………………………………………………………..………………………..	 8						2.1.2	Patient	selection	(inclusion	and	exclusion	criteria)	………………………………	 8						2.1.3	Data	collection	and	definitions	……………………………………………………..…….	 8						2.1.4	Data	analysis	…………………………………………………………………..………………….	 11		vi				2.2	Results	……………….………………………………………………………………………..…………..		13					2.2.1	Baseline	characteristics	of	the	study	population	………………………………....	 13					2.2.2	Frequency	of	complications	…………………………………………………………….…..	 16					2.2.3	Length	of	postoperative	hospital	stay	………………………………………………....	 17					2.2.4	Evaluation	of	the	accuracy	of	ACS	NSQIP	Surgical	Risk	Calculator	…………	 18					2.2.5	Less	frequent	outcomes	……………………………………………………………………….	 25			2.3	Discussion	………………………………………………….…………………………………………….	 25					2.3.1	Main	findings	………………………………………………………………………………….…..	 26					2.3.2	Limitations	…………………………………………………………………………………….….…	 28			2.4	Conclusion	…………………………………………………………………………………………….…	 29			2.5	Future	plan	…………………….…………………………………………………………………….....	 30	Bibliography	…………………………………………………………………………………………………….	 31	Appendices	………………………………………………………………………………………………………	 35			Appendix	A:		UBC	Mortality	&	Morbidity	Tool	webpage	…………………………...……	 35			Appendix	B:	ACS	NSQIP	Surgical	Risk	Calculator	software	example	…………….….			37																	vii	List	of	Tables			Table	2-1:	Data	collection	form	variables	………………………………………………………....	 10	Table	2-2:	Characteristics	of	the	study	population	and	procedures	……………………	 15	Table	2-3:	Frequency	of	complications	recorded	in	NSQIP	database	…………………	 16	Table	2-4:	Length	of	postoperative	hospital	stay	comparison	…………………………….	 17	Table	2-5:	Total	predicted	vs.	total	observed	outcomes	……………………………………..	 25																																		viii	List	of	Figures			Figure	2-1:	Calibration	graph	example	……………………………………………………………	 12	Figure	2-2:	Flow	diagram	of	patients	through	the	study	……………………………..…	 13	Figure	2-3:	Distribution	of	predicted	vs.	observed	length	of	hospital	stay	……..	 18	Figure	2-4:	Distribution	of	the	predicted	SSI	………………………………………………….	 19	Figure	2-5:	Comparison	of	predicted	vs.	observed	SSI	without	risk	adjustment	 20	Figure	2-6:	Comparison	of	predicted	vs.	observed	SSI	with	risk	adjustment	level	1	…………………………………………………………………………………………………………….		Figure	2-7:	Comparison	of	predicted	vs.	observed	SSI	with	risk	adjustment	level	2	…………………………………………………………………………………………………………….		Figure	2-8:	Distribution	of	predicted	any	complication	………………………..………..	 21	Figure	2-9:	Comparison	of	predicted	vs.	observed	any	complication	without	risk	adjustment	………………………………………………………………………………………………		Figure	2-10:	Comparison	of	predicted	vs.	observed	any	complication	with	risk	adjustment	level	1	…………………………………………………………………………………..…….		Figure	2-11:	Distribution	of	predicted	serious	complication	………………………....	 23	Figure	2-12:	Comparison	of	predicted	vs.	observed	serious	complication	without	risk	adjustment	………………………………………………………………………….…....		Figure	2-13:	Comparison	of	predicted	vs.	observed	serious	complication	with	risk	adjustment	level	1	…………………………………………………………………………….…….					20	21	22	22	24	24		ix			List	of	Abbreviations		VGH	 Vancouver	General	Hospital		UBC	 University	of	British	Columbia		ACS	NSQIP	 American	College	of	Surgeons	-	National	Surgical	Quality	Improvement	Program.	NSQIP	 National	Surgical	Quality	Improvement	Program.	ERAS	 Enhanced	Recovery	After	Surgery.	M&M	 Mortality	and	Morbidity		MRN	 Medical	Record	Number	RC	 Risk	Calculator		UTI	 Urinary	Tract	Infection	SSI	 Surgical	Site	Infection	PE	 Pulmonary	Embolism	DVT	 Deep	Vein	Thrombosis	VTE	 Venous	Thromboembolism		COPD	 Chronic	Obstructive	Pulmonary	Disease	DM	 Diabetes	Mellitus		HTN	 Hypertension		CHF	 Congestive	Heart	Failure		OR	 Operation	Room	CPT	 Current	Procedural	Terminology		ASA	 American	Society	of	Anesthesiologists		Cm	 Centimeters		Kg	 Kilograms			x		Acknowledgments		“We	always	aim	for	the	best	science”	The	inspiring	sentence	I	frequently	heard	from	Dr.	Warnock	and	which	immensely	encouraged	me	to	always	aim	for	the	best	quality	outcomes	throughout	my	project	period.		No	 words	 can	 express	 my	 deep	 and	 genuine	 gratitude	 to	 my	 supervisor,	 Dr.	Warnock,	 for	 his	 sincere	 dedication,	 keen	 interest	 and	 overwhelming	 support	throughout	my	master’s	journey.	His	 constructive	 advice,	 guidance	 and	 kind	 co-operation	 enabled	 me	 to	 complete	and	accomplish	this	task.		I	 am	 also	 thankful	 to	my	 committee	members	Dr.	 Kelly	Mayson	 for	 her	 input	 and	guidance,	 Dr.	 Penny	 Brasher	 for	 her	 opinions	 in	 the	 statistical	 analysis	 plan	 and	content	 revision,	 Ms.	 Tracey	 Hong	 for	 her	 constant	 cooperation	 during	 the	 data	collection	phase,	Mr.	Markus	Zurberg	for	his	valuable	opinions	during	the	period	of	finalizing	the	study	design.		My	special	Thanks	go	to	Dr.	Alice	Mui	 (MSc	program	coordinator)	who	always	was	there	to	facilitate	any	obstacles	appeared	through	the	way	and	Dr.	Morad	Hameed	for	accepting	to	be	part	of	the	defense	committee.			Finally,	I	would	be	honored	to	express	my	sincere	appreciation	and	gratitude	to	Dr.	Hani	 Al	 Qadhi	 for	 his	 ongoing	 support,	 keen	 encouragement	 and	 for	 making	 this	opportunity	of	pursuing	my	ambitions	possible.					xi					Dedication			I	dedicate	this	thesis	to:		My	 perfect	match,	 Sadiq	 for	 his	 continuous	 support	 and	 encouragement,	 without	him	this	would	not	have	been	possible.		My	loving	parents,	who	raised	me	to	the	person	I	am	today	and	who	taught	me	that	no	matter	how	hard	the	journey	might	be,	ambitions	must	be	chased	and	goals	must	be	reached.			My	wonderful	three	sisters,	Noor,	Bedoor	and	Lubna,	with	whom	I	shared	my	day-to-day	 details	 and	 difficulties	 until	 this	 work	 was	 finally	 formulated.	 Despite	 the	distance	their	presence	will	always	be	of	a	great	value	to	me.		My	only	best	friend,	Safiya,	without	hearing	her	advises	and	weekly	complaints	my	journey	would	not	have	been	as	joyful	as	it	was.		My	son,	Ali,	Who	I	cannot	wait	to	see	him	grow	up	into	a	successful	gentleman	with	enough	confidence,	knowledge	and	wisdom	to	choose	the	right	path	in	life.												 	 1	Chapter	1			Introduction	And	Literature	Survey					1.1 			Background		Complications	 after	 surgery	 increase	 mortality	 substantially	 (1).	 Colorectal	surgery	 itself	 carries	 a	 significant	 mortality	 risk,	 with	 reported	 rates	 of	 1–6%	 for	elective	 surgery	 and	 up	 to	 22%	 in	 the	 emergency	 setting	 (2-5).Understanding	 the	risks	and	possible	complications	of	any	surgical	intervention	before	proceeding	with	it	 is	 an	 important	 issue.	 The	process	of	 this	 decision	 is	 shared	between	physicians	and	 patients.	 In	 order	 to	 reach	 a	 final	 decision	 and	 provide	 the	 informed	 consent	that	 is	 required	 before	 any	 intervention,	 the	 patient	 has	 to	 have	 a	 thorough	understanding	of	the	potential	risks	of	surgery	(6,7).		Providing	accurate	information	to	patients	about	potential	complications	is	essential.	Historically,	health	institutions	adopted	what	is	called	a	minimum	standard	program	in	 which	 every	 staff	 is	 required	 to	 review	 and	 analyze	 at	 regular	 intervals	 their	clinical	experience	and	discuss	it	in	regular	meetings	(8,9).	With	some	modifications	over	the	years,	this	experience	nowadays	is	called	morbidity	and	mortality	meeting	or	morbidity	and	mortality	conference.			1.1.1			Mortality	and	morbidity	conference		The	morbidity	 and	mortality	 conference	has	 a	 long	history	 in	 the	 academic	pathway	as	many	medical	centers	use	 it	as	a	teaching	tool	 to	provide	trainees	real	examples	of	medical	errors	or	problematic	decision-making	situations.	Although	this	type	 of	meeting	 has	 not	 always	 resulted	 in	 lessons	 to	 prevent	 future	 error,	 it	 has	been	increasingly	used	as	a	part	of	the	measures	that	are	taken	to	enhance	patient	safety	and	quality	of	care	(10).		2	Currently,	this	type	of	meeting	is	considered	a	key	educational	tool	which	is	essential	for	 training	programs	 accreditation	 and	 a	 critical	 contribution	 to	 quality	 assurance	(11).	From	these	meetings,	critical	information	can	be	gathered	to	inform	knowledge	about	certain	cases	and	complications.		As	part	of	the	educational	process,	trainees	must	learn	to	report	errors	and	quality	issues,	 to	 participate	 in	 the	 wider	 picture	 of	 providing	 a	 better	 environment	 for	patient	 safety.	 Trainees	 should	 also	 be	 engaged	 in	 projects	 to	 improve	 systems	of	care,	 decrease	 health	 care	 disparities	 and	 improve	 patient	 outcomes	 (12).	 If	 this	process	 is	 conducted	with	attention	 to	best	practices	 such	as	non-punitive	 review,	debriefing,	 and	 follow	up	on	 systems	 improvements	 it	 can	 support	building	 strong	safety	cultures	in	medicine	(13).			1.1.2			UBC	Mortality	and	Morbidity	Tool		To	be	 contemporary	with	 the	 recommendations	 and	 apply	 the	best	 quality	measures,	UBC	has	established	Mortality	and	Morbidity	(M&M)	Tool	in	2013.	Many	modifications	 were	 introduced	 to	 the	 tool	 over	 the	 previous	 years	 to	 improve	 its	quality.	Appendix	A	demonstrates	 the	website	page	of	 the	 tool	and	 the	categories	that	 must	 be	 completed	 in	 order	 to	 report	 a	 case	 of	 mortality	 or	 morbidity,	 for	example,	patients’	 last	name,	MRN,	 type	of	complication	and	Clavien	Dindo	grade.	Recently	 the	 tool	 was	 approved	 by	 British	 Columbia	 Freedom	 of	 Information	 and	Protection	of	Privacy	Act	and	The	Vancouver	Costal	Health	Authority.		Since	August	2015,	 the	tool	has	been	used	for	residents	 in	 the	UBC	General	Surgery	Program	to	report	 complications	 for	 presentations	 and	 discussions	 at	 a	 weekly	 M&M	conference.	Furthermore,	a	database	was	established	and	a	summary	provided	 for	feedback	 to	 surgeon	 attending	 faculty	 on	 the	 frequency	 of	 complications	 on	 their	teams.	Surgical	team	members	are	requested	to	provide	feedback	to	front	line	care	providers	 on	 their	 team-encountered	 patients’	 mortality	 and	 morbidity.	 This	experience	was	 prepared	 as	 an	 abstract	 submitted	 for	 presentation	 in	 one	 of	 the		3	future	NSQIP	meetings	(personal	communication;	Mr.	Markus	Zurberg,	Dec	2015).			Despite	 potential	 advantages	 of	 M&M	 reporting	 tool	 such	 as	 the	 one	 we	 have	developed,	literature	is	showing	a	very	low	reporting	rate	for	custom	reporting	tools	in	other	centers	(9)	which	made	it	necessary	to	find	other	ways	to	improve	patient	safety	by	creating	a	variety	of	pre-procedural	risk	assessment	tools.				1.1.3			Risk	assessment	tools/models	Many	institutions	created	tools	for	risk	assessment	and	risk	prediction	to	help	in	 the	 process	 of	 decision	making.	 Some	 of	 the	 currently	 available	 models	 in	 the	literature	 include	 Cleveland	 Clinic	 Foundation	 colorectal	 cancer	 model	 and	 the	Physiological	 and	 Operative	 Severity	 Score	 for	 enUmeration	 of	 Mortality	 and	morbidity	 (POSSUM).	 These	 models	 either	 solely	 assess	 risk	 of	 mortality	 or	 are	difficult	and	complex	to	assess	at	patient’s	bedside	(14).	Other	models	which	tried	to	simplify	the	previously	mentioned	ones	include	the	Colorectal	preOperative	Surgical	Score	(CrOSS),	which	may	be	easier	to	apply	but	still	encounters	the	same	problem	of	 assessing	 the	 risk	 of	 mortality	 only	 without	 addressing	 the	 other	 important	aspects	 of	 postoperative	 morbidity	 (15).	 In	 order	 to	 solve	 this	 issue,	 ACS	 NSQIP	developed	 a	 model	 called	 the	 Surgical	 Risk	 Calculator	 (RC)	 which	 addresses	postoperative	morbidity	and	mortality.				1.1.4			National	Surgical	Quality	Improvement	Program	(NSQIP)		The	American	College	of	Surgeons	 (ACS)	 initiated	a	program	called	National	Surgical	 Quality	 Improvement	 Program	 (NSQIP)	 that	 collects	 high-quality,	standardized	 clinical	 data	 on	 preoperative	 risk	 factors	 and	 postoperative	complications	from	patients	who	have	surgery	 in	more	than	500	hospitals	 in	North	America	and	selected	 international	sites	(16,17).	Clinical	reviewers	 in	these	centers		4	are	 extensively	 trained	 to	 collect	 data	 in	 different	 methods	 like	 chart	 review,	surgeon	and	patient	interview	to	ensure	high	quality	of	the	collected	data	(18).	This	program	applied	 high-quality	 data	 that	 they	 collected	 to	 develop	 a	 tool	 to	 predict	surgical	 risk	 in	 the	 form	 of	 software	 that	 predicts	 postoperative	 mortality	 and	morbidity	and	from	here	it	gains	its	importance,	as	it	is	not	addressing	mortality	only	like	the	previous	models	but	it	also	includes	morbidity	assessment	(17).			1.1.5			ACS	NSQIP	Surgical	Risk	Calculator		The	ACS	NSQIP	Surgical	Risk	Calculator	is	generated	from	1.4	million	patients’	information	 gathered	 between	 2009	 till	 2012	 from	 all	 the	 NSQIP-participating	institutions	 (19,29).	 Those	 institutions	 ranged	 from	 rural	 community	 hospitals	 to	large	 academic	 and	 university-affiliated	 centers	 representing	 a	 wide	 variety	 of	surgeries	in	variable	clinical	settings	(29).		The	 initial	 RC	 first	 released	 in	 2013	 (29)	 was	 an	 online	 software	 requesting	information	 input	 of	 demographics,	 functional	 status,	 comorbidities	 and	 American	Society	 of	 Anesthesiologists	 (ASA)	 class.	 Figure	 1,	 Appendix	 B	 shows	 the	 risk	calculator	software	 interface	and	a	screen	shot	of	the	risk	factor	entry	screen.	This	data	is	entered	by	the	surgeon	or	anesthesiologist	who	is	assessing	the	patient	in	the	preoperative	period.	All	the	risk	factors	can	be	entered	using	a	drop	down	menu	of	each	category	which	makes	the	process	of	entering	the	data	easier	and	quicker.	The	information	 required	 to	 generate	 the	 risk	 estimates	 includes	 procedure	 name,	patient	 age,	 sex,	 comorbidities	 (DM,	 HTN,	 cardiac	 events,	 etc.),	 ASA	 class,	 wound	class	 and	 others.	 Figure	 2	 (appendix	 B)	 is	 the	 risk	 generated	 report	 screen	 which	demonstrates	 the	way	 that	 risk	 estimates	 are	 presented	 in	 each	 category	 starting	from	 serious	 complication	 and	 ending	 with	 discharge	 to	 nursing	 or	 rehabilitation	facility.	 Clear	 definitions	 are	 provided	 by	 NSQIP	 explaining	 what	 each	 category	 of	outcomes	 indicates	and	 the	outcome	definition	will	appear	 in	a	pop	up	dialogue	 if	clicked	 on	 the	 question	 mark	 sign	 beside	 each	 outcome.	 For	 example,	 “any		5	complication”	defined	as	all	the	NSQIP	recorded	morbidity	and	they	are:		Superficial	incisional	SSI,	deep	incisional	SSI,	organ	space	SSI,	pneumonia,	unplanned	intubation,	PE,	 DVT,	 ventilator	 >	 48	 hours,	 acute	 renal	 failure,	 UTI,	 cardiac	 arrest,	myocardial	infarction,	return	to	the	operating	room,	systemic	sepsis.	While	Serious	complication	included	 all	 the	 outcomes	 mentioned	 in	 any	 complication	 except	 superficial	incisional	SSI	and	ventilator	>	48	hours.	The	report	also	provides	an	explanation	on	how	 to	 interpret	 the	 results	 by	 showing	 a	 sample	 at	 the	 bottom	 of	 the	 reporting	page.	The	sample	shows	 that	 the	bolded	black	 line	 represents	average	patient	 risk	and	 the	 concerned	 patient	 risk	 is	 demonstrated	 in	 three	 methods:	 graphically,	percentage	of	estimated	risk	and	chance	of	outcome	(below	average,	average,	above	average).	 	 The	 report	 also	 provides	 an	 estimation	 of	 the	 length	 of	 hospital	 stay	postoperatively.	Finally,	 the	calculator	gives	 the	surgeon	or	physician	assessing	 the	patient	 the	option	of	adjusting	 the	 risks	because	 the	calculator	doesn’t	 capture	all	risk	factors.	The	risks	adjustment	has	three	levels,	level	0	with	no	adjustment,	level	one	is	when	the	risk	 is	somewhat	higher	than	estimated	and	level	two	is	when	the	risk	is	significantly	higher	than	estimated.		So	far,	the	literature	presents	scattered	papers	studying	the	accuracy	of	ACS	NSQIP	Surgical	Risk	Calculator	in	estimating	postoperative	complications	in	certain	types	of	surgeries.	 Bilimoria	 et	 al	 concluded	 in	 their	 study	 that	 ACS	 NSQIP	 Surgical	 Risk	Calculator	 level	 of	 prediction	 was	 reasonable	 and	 this	 was	 demonstrated	 by	 c-statistics	 results	 which	 ranged	 between	 0.806	 to	 0.944	 for	most	 of	 the	 outcomes	(20).	 Some	 studies	 showed	 that	 complications	 might	 not	 be	 accurately	 estimated	(21).	Other	studies	showed	that	complications	were	effectively	estimated	in	patients	with	average	risk	factors	but	less	so	in	predicting	complications	in	patients	with	lots	of	 risk	 factors	 (22).	 	 These	 studies	 were	 performed	 in	 surgical	 specialties	 of	gynecology,	 orthopedics	 and	 surgical	 oncology.	 One	 study	 compared	 the	 data	collected	 using	 a	 traditional	 M&M	 tool	 with	 data	 collected	 using	 the	 ACS	 NSQIP	techniques	concluding	 that	 the	M&M	tool	considerably	underreported	 for	both	 in-hospital	 and	 post-discharge	 complications	 and	 deaths	 compared	 with	 ACS	 NSQIP	techniques	(9).	No	study	in	the	literature	was	identified	that	addresses	utility	of	the		6	RC	 in	 the	 context	 of	 general	 surgery	 cases	 rather	 than	 subspecialized	 fields	 like	surgical	oncology,	gynecology	and	orthopedics	surgery.	Recently,	ACS	NSQIP	Surgical	Risk	Calculator	was	updated.	New	prediction	equations	were	 based	 on	 larger	 and	more	 recent	 samples	 of	 surgical	 patients.	 The	 updated	calculator	tends	to	assign,	for	highest	risk	patients,	higher	predicted	risk	of	mortality	than	 the	 old	 calculator.	 Otherwise,	 the	 prediction	 for	 other	 outcomes	 is	 almost	similar	to	the	version	used	in	this	study	(personal	communication	with	Tracey	Hong,	May	2016).	Hence,	the	idea	of	this	project	came	to	study	the	accuracy	of	ACS	NSQIP	Surgical	Risk	Calculator	in	the	setting	of	elective	colorectal	surgery	procedures	which	are	enrolled	in	 ERAS	 program.	 ERAS	 database	 combined	 with	 standardized	 NSQIP	 reporting	allows	for	reliable	definitions	to	test	the	RC	tool.			1.1.6			Enhanced	Recovery	After	Surgery	(ERAS)	The	 project	 aims	 to	 use	 the	 Enhanced	 Recovery	 After	 Surgery	 (ERAS)	database	as	a	source	for	the	sample	population	that	will	be	studied.	ERAS	program	(also	called	fast	track	perioperative	care)	is	an	evidence-based	collection	of	protocols	that	patients	undergoing	elective	surgeries	are	recommended	to	follow	(23,24).		Literature	 shows	 that	 undergoing	 colorectal	 surgery	 involving	 bowel	 resection	carries	 s	 15%	 to	 20%	 rate	 of	 complications	 (25,26).	 In	 an	 effort	 to	 reduce	 post	colorectal	surgery	complications	and	decrease	the	length	of	hospital	stay,	Kehlet	et	al.	 (27)	was	 the	 first	 to	 describe	 in	 detail	 the	 fast	 track	 or	 the	 enhanced	 recovery	after	 surgery	 protocols.	 This	 was	 achieved	mainly	 by	 harnessing	 the	 physiological	principles	 to	 improve	 patient	 outcomes	 by	 reducing	 the	 profound	 stress	 response	induced	 by	 surgery,	 there	 by	 reducing	 postoperative	 complications,	 minimizing	hospital	 stay,	and	ultimately	 reducing	health	 costs	without	 compromising	patients'	safety	(28,30).	Moreover,	the	aim	is	to	provide	pain	and	stress-free	pathway	to	full	recovery.				7	Combining	all	 the	above	factors,	the	main	objective	of	this	study	 is	to	examine	the	evidence	 that	 supports	 and	 validates	 the	 accuracy	 of	 the	 level	 of	 risk	 prediction	generated	 by	 the	 RC	 in	 our	 patient	 population	 and	 how	 accurately	 it	may	 help	 in	predicting	postoperative	complications.	The	ultimate	aim	is	to	observe	if	pre-surgical	application	 of	 the	 RC	 will	 highlight	 ways	 to	 predict	 and	 reduce	 postoperative	complications	further	in	patients	entering	ERAS	programs	(30).					1.2 			Overall	project	hypothesis:		We	 hypothesize	 that	 the	 RC	 predicts	 postoperative	 complications	 that	 can	 be	detected	through	routine	NSQIP	screening.			1.3 			Overall	project	objective:		The	overall	objective	of	this	thesis	is	to	provide	a	more	robust	ability	to	predict	and	 prevent	 postoperative	 complications	 in	 a	 population	 of	 patients	 who	 are	undergoing	scheduled	elective	colon	resections	using	a	standardized	procedure	care	protocol.																			8	Chapter	2	 	 	Utility	Of	The	ACS	NSQIP	Surgical	Risk	Calculator	To	 Accurately	 Predict	 Postoperative	 Outcomes	 After	 Colon	Resection:	A	Validation	Study.				2.1	Methods			2.1.1	Study	design			This	project	 is	a	validation	study	with	secondary	use	of	administrative	data.	This	 study	 compared	 observed	 postoperative	 outcomes	 for	 patients	 undergoing	elective	colorectal	procedures	 in	VGH	to	the	predicted	outcomes	generated	by	the	RC.	This	comparison	was	conducted	to	assess	validity	of	the	surgical	risk	prediction	model	 provided	 by	NSQIP	 in	 our	 patient	 population.	Data	 for	 a	 cohort	 of	 patients	who	underwent	elective	colorectal	surgery	under	ERAS	protocol	at	VGH	during	the	period	from	November	2013	to	December	2015,	and	who	were	selected	for	the	VGH	NSQIP	sample	was	extracted	from	the	ERAS	database.			2.1.2	Patient	selection	(inclusion,	exclusion	criteria)		All	 adult	 (≥18	 years	 old)	 patients	 of	 all	 ages	 who	 underwent	 colorectal	procedure	 under	 Enhanced	Recovery	After	 Surgery	 program	 in	Vancouver	General	Hospital	were	included.	Any	patients	presented	and	operated	 in	an	emergency	setting	were	excluded	from	the	study.			2.1.3	Data	collection	and	definitions		Consecutively	 treated	 patients	 enrolled	 in	 ERAS	 who	 underwent	 elective	colorectal	procedures	between	the	periods	from	November	2013	to	December	2015	were	identified	through	reviewing	the	lists	of	OR	slates	for	patients	who	underwent	colorectal	 procedure	under	 ERAS	during	 the	 above	mentioned	period.	 Information		9	on	 patients’	 demographics,	 functional	 status,	 smoking,	 medical	 background	 (HTN,	DM,	 COPD,	 previous	 cardiac	 history,	 ventilation	 dependence,	 cancer,	 acute	 renal	failure,	 dialysis,	 ascites	 and	 sepsis)	 and	 procedural	 details	 (procedure	 name,	 date,	CPT	code	and	description)	was	abstracted	from	the	ERAS	database	and	entered	into	Excel	spreadsheets	as	summarized	in	table	2-1.		Admission,	 discharge	 dates	 and	 discharge	 destination	 were	 also	 obtained	 to	calculate	the	length	of	hospital	stay.		Duration	of	hospital	length	of	stay	was	defined	as	 the	 total	days	postoperatively	 in	hospital	 from	the	 surgery	date	until	discharge.	NSQIP	outcomes	detected	through	the	30	days	follow	up	included	any	complication,	serious	 complication,	 pneumonia,	 cardiac	 complications,	 surgical	 site	 infection,	urinary	 tract	 infection,	 acute	 renal	 failure,	 ileus,	 deep	 vein	 thrombosis,	 pulmonary	embolism,	 unplanned	 intubation,	 ventilation	 more	 than	 48	 hours,	 death	 and	discharge	destination	as	shown	in	table	2-1.		All	 categories	 were	 defined	 by	 NSQIP	 standard	 definitions.	 The	 category	 “any	complication”	included	an	aggregate	of	all	the	NSQIP-recorded	morbidities	including:		Superficial	incisional	SSI,	deep	incisional	SSI,	organ	space	SSI,	pneumonia,	unplanned	intubation,	 PE,	 DVT,	 ventilator	 >	 48	 hours,	 acute	 renal	 failure,	 UTI,	 cardiac	 arrest,	myocardial	 infarction,	 return	 to	 the	 operating	 room,	 systemic	 sepsis.	 “Serious	complication”	 was	 defined	 as	 all	 the	 outcomes	 mentioned	 in	 any	 complication	except	superficial	incisional	SSI	and	ventilator	>	48	hours.											10	Table	2-1	Data	collection	form	variables		Preoperative	variables	(ERAS	database)	 30	days	follow	up	variables	(NSQIP	database)	Age	Sex	Date	of	admission		Date	of	discharge	Date	of	surgery		Procedure	name	CPT	code	and	description		Height	in	(cm)	&	weight	(kg)	DM	HTN	Smoking	Dyspnea	Ventilation	dependence	Disseminated	cancer	Functional	status		COPD	Previous	cardiac	history/	CHF	Acute	renal	failure/	dialysis		Steroid	use	Ascites		Systematic	sepsis	ASA	class	Wound	class		Pneumonia	Cardiac	complications	SSI	UTI	Ileus		DVT	PE	Acute	renal	failure	Transfusion		Sepsis	Return	to	OR	Death	Unplanned	intubation		Ventilation	>48	hours	Discharge	destination			If	 study	 data	 was	 missing	 from	 the	 ERAS	 or	 NSQIP	 database	 the	 information	 was	obtained	 by	 chart	 reviews	 of	 preoperative	 assessment	 reports	 in	 the	 Vancouver	General	Hospital	Patient	Care	Information	System	(PCIS)	files.	Since	all	of	the	patients	were	managed	according	to	the	standard	of	care	at	VGH,	the	project	was	approved	to	be	 of	 minimal	 risk	 to	 patient	 confidentiality	 by	 the	 University	 of	 British	 Columbia	Clinical	 Research	 Ethics	 Board	which	 approved	 the	 study	 protocol	 to	 be	 conducted	with	a	waived	consent	(UBC	CREB	No.	H16-00821).			2.1.4	Data	analysis		Descriptive	 statistics	were	 used	 to	 describe	 the	 demographic,	 preoperative	and	 operative	 characteristics	 of	 the	 study	 population.	 Discrete	 variables	 were		11	summarized	 by	 frequencies	 and	 percentages.	 Continuous	 variables	 were	summarized	by	mean	(standard	deviation).		A	table	of	the	number	(percent)	of	patients	with	a	NSQIP-recorded	complication	was	constructed.	 Postoperative	 outcomes	 were	 broken	 down	 by	 NSQIP	 category,	 i.e.,	serious	 complication,	 any	 complication,	 pneumonia,	 cardiac	 complication,	 SSI,	UTI,	VTE,	 acute	 renal	 failure,	 return	 to	 OR,	 death	 and	 discharge	 to	 nursing	 or	 rehab	facility.			The	NSQIP	predicted	and	the	observed	lengths	of	hospital	stay	postoperatively	were	compared	visually	using	histograms.	A	table	showing	the	mean,	standard	deviation,	range	of	hospital	stay	was	constructed	and	the	Wilcoxon	rank	sum	test	was	used	to	compare	the	two	groups.		Evaluation	of	the	accuracy	of	the	ACS	NSQIP	Surgical	Risk	Calculator:				To	 assess	 the	model	 performance,	 calibration	measures	 were	 assessed	 for	the	outcome	categories	which	had	at	least	50	events.		Calibration	of	a	prediction	model	generally	studies	the	agreement	between	observed	outcome	frequencies	and	predicted	probabilities	(31).		In	 our	 study	 the	 calibration	 of	 the	 model	 was	 assessed	 graphically	 by	 comparing	predicted	 and	 observed	 risks	 for	 the	 categories	 serious	 complication,	 any	complication	 and	 SSI.	 The	 predicted	 risk	 for	 each	 outcome	 was	 cut	 into	approximately	equally-sized	"bins".			A	bin,	is	away	of	sorting	data	by	evenly	distributing	the	data	set	in	carefully	chosen	categories,	as	demonstrated	in	figure	2-1	(x-axis).			12		Figure	2-1	Calibration	graph	example:	The	thick	line	indicates	perfect	calibration;	the	thin	line	shows	the	relationship	between	the	predicted	and	observed	probabilities.	Circles	indicate	observed	events	per	quintile	of	predicted	probabilities			After	that,	the	mean	predicted	risk	for	each	bin	was	calculated	(x-axis)	and	plotted	versus	 the	observed	 risk	 (y-axis)	 (Figure	2-1)	and	a	 linear	model	 line	was	 fit	 to	 the	data	 (thin	 line).	 Finally	 the	 line	 of	 equality	was	 superimposed	 on	 the	 graph	 (Thick	line).			The	line	of	equality	is	used	as	a	reference	in	comparing	two	sets	of	data	expected	to	be	identical.	If	the	prediction	model	is	perfectly	accurate,	the	fitted	linear	model	line	should	 follow	 the	 line	of	equality.	 In	 the	example	provided	 in	 figure	2-1	 the	 linear	model	 line	 is	 shifted	 to	 the	 left	 of	 the	 line	 of	 equality,	 which	 indicates	 that	 the	observed	 events	 are	 higher	 than	 predicted.	 For	 outcomes	 that	 did	 not	 have	 a	sufficient	number	of	events	 to	assess	 calibration,	we	provided	a	 table	of	observed	versus	predicted	events.		For	length	of	postoperative	hospital	stay,	a	p	value	<0.05	was	considered	statistically	significant.	All	data	was	collected	using	Microsoft	Excel	(2011)	and	data	analysis	was	carried	out	using	R	program	(version	3.2.3).		13	2.2	Results		2.2.1	Baseline	characteristics	of	the	study	population			Figure	 2-2	 shows	 the	 selection	 of	 patients	 to	 be	 included.	 A	 total	 of	 416	patients	were	 enrolled	 in	 ERAS	program	 to	undergo	 a	 colorectal	 procedure	during	the	period	from	Nov	1st,	2013	to	December	31st,	2015.	Study	population	colorectal	procedures	 included	 left	 and	 right	 hemicolectomy,	 sigmoid	 and	 segmental	 colon	resection,	 rectosigmoid	 and	 rectal	 excision.	 Diagnoses	 of	 colon	 resection	 were	malignancy,	benign	polyp,	diverticular	disease	and	inflammatory	bowel	disease.	Out	of	these,	thirty-seven	were	excluded	because	they	were	not	sampled	by	NSQIP	and	another	eleven	patients	were	excluded	because	of	missing	data.							Figure	2-2	Flow	diagram	of	patients	through	the	study			 		14	Finally,	 a	 total	 of	 368	 patients	 were	 included	 in	 the	 study.	 Table	 2-2	 shows	 the	demographics,	procedure	measures	and	operative	characteristics	of	the	population.	The	 age	of	 the	 study	population	 ranged	between	24	 and	100	 years	 and	 the	mean	was	69	years	with	a	standard	deviation	of	13.4	and	54.1	%	of	them	were	males.	The	mean	 population	 BMI	was	 26.7	 (SD=	 5.3)	 and	 the	 preoperative	measures	 showed	that	45.4%	of	the	sample	were	hypertensive	and	15.8	%	were	with	a	previous	cardiac	history.57.3	 %	 were	 classified	 as	 ASA	 class	 2	 and	 71.2	 %	 of	 the	 total	 procedures	performed	were	minimally	invasive	surgery.																											15	Table	2-2	Characteristics	of	the	study	population	and	procedures	(n=368)		Demographics		 Mean±SD	or	Number	 	%	Age	(years)	 69±13.4	 ⎯	Males	–	number	(%)	 199		 54.1	Body	Mass	Index	(kg/m2)	 26.7±5.3	 ⎯	- Height	(cm)		 163.5±18.8	 ⎯	- Weight	(kg)	 74.1±18.5	 ⎯	Preoperative	measures		 Number	 %	Functional	status		 	 	- Independent	 365		 99.2	- Partially	Dependent		 3		 0.8	Diabetes*		 37		 10.1	Hypertension		 167		 45.4	Smoker**	 29		 7.9	Dyspnea	on	moderate	exertion		 18		 4.9	Ventilator	dependent		 5		 1.6	Disseminated	Cancer		 17		 4.6	COPD†	 14		 3.8	Previous	cardiac	history	 58		 15.8	Dialysis	 3		 0.8	Recent	steroids	use	 15		 4.1	Sepsis††	 5		 1.4	Operative	characteristics		 Number	 %	ASA	Class	∞	 	 	- Class	1	 15		 4.1	- Class	2	 211		 57.3	- Class	3	 128		 34.8	- Class	4	 14		 3.8	Procedure		 	 	- Laparoscopic	technique		 262		 71.2	- Open	technique		 105		 28.5	Wound	Class	 	 	- Clean-Contaminated	 350		 95.1	- Contaminated	 10		 2.7	- Dirty-Infected	 8		 2.2	Data	are	shown	as	number	(%)	or	mean	(standard	deviation).	•Diabetes	category	including	Insulin	and	non-insulin	dependent;	••smoking	recorded	within	a	year	from	the	surgery;	†COPD,	Chronic	Obstructive	Pulmonary	Disease;	††Sepsis	includes	its	different	stages;	∞	ASA,	American	Society	of	Anesthesiologists.			 							16			2.2.2	Frequency	of	complications			 The	occurrence	of	a	complication	was	recorded	by	NSQIP	is	shown	in	table	2-3.	There	were	a	total	of	70	patients	with	recorded	NSQIP	morbidity,	representing	19	%	 of	 the	 total	 sample	 population.	 Fifty-one	 of	 the	 total	 complications	 met	 the	criteria	 of	 serious	 complication	 representing	 13.9%	 of	 the	 total	 population	 of	 the	study.	 Surgical	 site	 infection	 was	 the	 highest	 represented	 complication	 against	 all	other	 recorded	 outcomes	 reaching	 up	 to	 13.8	 %	 of	 the	 total	 complications.	Postoperative	blood	transfusion	and	ileus	scored	10.3	%	and	8.7	%	respectively	but	these	two	complications	are	not	predicted	by	the	tool	that	we	are	evaluating	in	this	study	so	they	were	not	further	included	in	the	analysis.				Table	2-3	Frequency	of	complications	recorded	in	NSQIP	database		NSQIP-recorded	complication																	Number		 Percent	%		Serious	complication	 51	 13.9	Any	complication	 70	 19	Pneumonia		 15	 4.1	Cardiac	complication		 9	 2.4	SSI		 51	 13.9	UTI		 11	 3	DVT	 3	 0.8	Ileus	 32	 8.7	Acute	renal	failure	 5	 1.4	PE	 1	 0.3	Transfusion	 38	 10.3	Sepsis	 20	 5.4	Return	to	OR		 6	 1.6	Death	 2	 0.5	Intubation	 10	 2.7	Ventilation	>48	hours	 11	 3									17		2.2.3	Length	of	postoperative	hospital	stay			Length	 of	 postoperative	 hospital	 stay	 is	 shown	 in	 table	 2-4.	 The	 mean	 of	 the	predicted	length	of	stay	was	4.4±1.3	days	(range	2.5-10.5,	median	4).	Corresponding	stay	was	8.6±12.1	days	(range	1-171,	median	6).	The	difference	between	predicted	and	observed	lengths	was	significant	(p	<	0.01,	Wilcoxon	rank	sum	test).			Table	2-4	Length	of	postoperative	hospital	stay	comparison																															Length	of	postoperative	hospital	stay	(days)		 Predicted		 Observed		 P	value	*	Mean	(days)	 4.4	 8.6		 <	0.01		SD	 1.3	 12.1	 	Range	of	hospital	stay	(days)	2.5	-	10.5		 1	-	171	 	 	*	Wilcoxon	rank	sum	test			Figure	2-3	is	shows	the	distribution	of	the	predicted	length	of	postoperative	hospital	stay	versus	the	observed	length	of	postoperative	hospital	stay.	It	clearly	illustrates	a	wider	pattern	of	distribution.	Multiple	outliers	are	shown	in	the	graph	of	observed	length	of	stay.										18												Figure	2-3	Distribution	of	predicted	vs.	observed	length	of	hospital	stay		(The	numerical	values	in	the	observed	graph	are	the	outliers	detected	in	that	group)				2.2.4	Evaluation	of	the	accuracy	of	ACS	NSQIP	Surgical	Risk	Calculator			 In	this	part	of	the	analysis	the	calibration	of	the	risk	prediction	model	was	assessed	for	the	following	outcomes:				1.	Surgical	site	infection:			 Surgical	 site	 infection	 was	 the	 highest	 detected	 complication	 as	 shown	previously	in	table	2-3	and	the	pattern	of	its	predicted	distribution	is	shown	in	figure	2-4.	The	calibration	of	the	model	for	this	outcome	was	assessed	without	increasing	the	 level	 of	 surgeon	 adjusted	 risk	 in	 the	 RC	 in	 figure	 2-5	 and	 showed	 that	 the	observed	outcomes	are	higher	than	the	predicted	outcomes,	which	means	that	this	model	is	underestimating	the	risk	of	surgical	site	infection.				19			Figure	2-4	Distribution	of	the	predicted	SSI					To	 further	 assess	 the	 model,	 the	 same	 process	 was	 repeated	 for	 but	 with	introduction	of	 the	RC	surgeon	adjusted	risk	according	 to	 the	RC	report	 screen.	As	shown	 in	 figure	 2-6	 and	 figure	 2-7	 respectively,	 predicted	 outcomes	 for	 SSI	 were	much	more	accurate	and	closer	to	the	observed	outcomes	in	level	1	adjustment	but	in	 level	 2	 the	 predicted	 outcomes	 exceeded	 the	 observed	 outcomes.	 From	 the	assessment	of	the	model	calibration	taking	in	consideration	its	three	levels	(without	adjustment,	adjustment	level	1,	adjustment	level	2),	prediction	with	risk	adjustment	level	one	provided	 the	 closest	prediction	of	postoperative	 surgical	 site	 infection	 in	our	patient	population.								20			Figure	2-5	Comparison	of	predicted	vs.	observed	SSI	without	risk	adjustment														Figure	2-6	Comparison	of	predicted	vs.	observed	SSI	with	risk	adjustment	level	1					21		Figure	2-7	Comparison	of	predicted	vs.	observed	SSI	with	risk	adjustment	level	2				2.	Any	complication:			 This	category	of	outcomes	included	the	sum	of	all	the	predicted	morbidity	outcomes	by	NSQIP	and	figure	2-8	is	showing	the	pattern	of	its	distribution.			Figure	2-8	Distribution	of	predicted	any	complication		22	Figure	2-9	and	2-10	are	 the	graphs	of	 the	model	 calibration	 for	outcome	category	“any	 complication”	 without	 risk	 adjustment	 and	 with	 risk	 adjustment	 level	 1	respectively.	 For	 this	 outcome,	 the	 model	 showed	 a	 similar	 level	 of	 prediction	without	risk	adjustment,	but	a	lower	agreement	of	prediction	at	higher	adjustment.			Figure	2-9	Comparison	of	predicted	vs.	observed	any	complication	without	risk	adjustment				Figure	2-10	Comparison	of	predicted	vs.	observed	any	complication	with	risk	adjustment	level	1			23			3.	Serious	complication:			 This	 category	 of	 outcomes	 shows	 the	 distribution	 in	 figure	 2-11.	 The	calibration	 graph	 showed	 that	 observed	 risks	 were	 slightly	 higher	 than	 predicted	(figure	2-12)	but	using	the	level	1	adjustment	produced	larger	discrepancies	(figure	2-13).					Figure	2-11	Distribution	of	predicted	serious	complication				24				Figure	2-12	Comparison	of	predicted	vs.	observed	serious	complication	without	risk	adjustment					Figure	2-13	Comparison	of	predicted	vs.	observed	serious	complication	with	risk	adjustment	level	1						25		2.2.5	Less	frequent	outcomes				 The	 rest	 of	 the	 outcome	 categories	were	 not	 assessed	 graphically	 because	the	number	of	the	detected	events	per	category	was	very	small.	Pneumonia	and	UTI	were	 the	 only	 two	 categories	 that	 showed	 a	 larger	 number	 of	 observed	complications	than	the	predicted	ones.	The	outcome	category	discharge	to	nursing	or	rehab	facility	showed	less	number	of	observed	events	than	predicted	and	this	was	the	same	for	all	other	complications.					Table	2-5	Total	predicted	vs.	total	observed	outcomes		Outcome	category		 Total	predicted		 Total	observed	Pneumonia		 5.8	 15	Cardiac	complications	 16.5	 9	UTI	 9.8	 11	VTE	 4.8	 4	Acute	renal	failure		 16.6	 5	OR	return	 14.6	 6	Death	 4.2	 2	Discharge	to	facility		 20.2	 12					2.3	Discussion			Validation	studies	are	crucial	to	evaluate	any	prediction	model	performance.	Two	qualities	 are	 assessed,	 including	 calibration	 (compare	observed	and	predicted	event	rates	for	a	group	of	patients)	and,	discrimination	(quantify	the	model	ability	to	distinguish	between	patients	who	do	or	do	not	experience	the	event	of	interest)	or	both	 (36).	Testing	 the	generalizability	of	 these	models	before	recommending	them	for	clinical	use	is	essential	(32).	The	more	diverse	the	setting	the	model	is	tested	and	found	accurate,	the	more	it	will	generalize	and	become	widely	applicable	(33,	34).		In	any	surgical	field,	surgeons	are	frequently	asked	to	provide	prognostic	assessment		26	for	operative	and	postoperative	risks	(39).	To	do	so,	surgeons	usually	depend	on	the	literature	that	is	based	on	results	of	aggregate	data	or	they	depend	on	their	personal	experience	and	they	often	worry	that	their	assessment	will	prove	incorrect	(33,35).		The	 American	 College	 of	 Surgeons	 provided	 a	 tool	 that	 may	 guide	 surgeons	 to	accurately	predict	postoperative	risk	and	make	the	process	of	assessment	consistent	in	different	surgical	environments	worldwide.	The	ACS	NSQIP	Surgical	Risk	Calculator	is	 a	 model	 that	 was	 built	 on	 a	 multi-institutional,	 well-collected	 data	 of	 a	 large	sample	size.	However,	 this	 tool	 is	 still	underutilized	 in	 the	clinical	 field.	One	of	 the	possible	 reasons	 for	 this	 is	 that	 this	 tool	 remains	 incompletely	 tested	 to	 prove	 its	validity	 and	 gain	 trust	 of	 surgeons	 and	 stakeholders	 to	 incorporate	 it	 in	 the	preoperative	assessment	protocols.				2.3.1	Main	findings		Our	 study	 applied	 this	 tool	 on	 a	 sample	 of	 patients	 from	 a	 single	 tertiary	center	 and	 compared	 the	 predictions	 to	 our	 actual	 experience.	 Starting	 with	 the	length	 of	 postoperative	 hospital	 stay,	 the	 observed	 stay	 showed	multiple	 outliers.	Even	 after	 we	 tried	 to	 exclude	 the	 outliers,	 the	 observed	 distribution	 was	 still	different	than	the	predicted	one.	The	RC	did	not	show	an	accurate	prediction,	as	the	range	of	 the	prediction	was	very	 small	despite	having	multiple	patients	with	many	risk	 factors.	 This	 may	 indicate	 that	 the	 tool	 incompletely	 adjusts	 the	 risk	 on	 the	individual	 level.	 	This	observation	agrees	with	other	 studies	 in	 the	 literature	which	had	 similar	 findings	 (22).	 Our	 study	 population	 was	 ERAS	 which	 has	 designed	 to	reduce	postoperative	length	of	hospital	stay,	yet	we	still	did	not	see	accuracy.		To	test	the	model	performance	our	study	assessed	the	calibration	graphically.	Three	outcome	categories	of	any	complication,	serious	complication	and	SSI	were	chosen	to	 be	 assessed	 by	 this	 method	 and	 the	 reason	 for	 choosing	 them	 was	 that	 they	contained	a	reasonable	number	of	events.	In	order	to	rigorously	assess	calibration	at	least	100	events	are	necessary	(31).			27	Despite	 the	 fact	 that	 our	 sample	 population	 is	 on	 standardized	 procedure	 care	protocols	within	ERAS	under	high	compliance	with	perioperative	protocols	to	reduce	SSI,	we	still	found	that	SSI	scored	the	highest.	The	model	prediction	for	this	outcome	showed	an	overall	underestimation	of	the	postoperative	SSI.	In	order	to	improve	the	model	prediction,	we	generated	 the	calibration	graphs	 for	 the	predicted	outcomes	with	risk	adjustment	 in	 its	 two	 levels.	This	step	was	done	to	examine	 if	 simple	risk	adjustment	will	modulate	 the	model	performance.	This	particular	 step	adds	 to	 the	concepts	 in	previous	literature.	 	Our	analysis	for	this	outcome	showed	by	adjusting	the	 risk	 to	 level	 one,	 the	 calibration	 graph	 demonstrated	 a	 better	 agreement	between	the	predicted	and	the	observed	outcomes.	Hence,	we	can	suggest	that	to	improve	the	tool	performance	 for	 this	outcome	 in	our	patient	population,	 the	 first	level	risk	adjustment	can	be	employed.			The	 same	 steps	 of	 testing	 the	 agreement	 between	 the	 predicted	 and	 observed	outcomes	were	performed	for	the	outcome	categories	any	complication	and	serious	complication.	For	these	two	variables,	the	model	showed	fairly	good	calibration.			Our	study	did	not	perform	the	same	steps	of	assessment	and	analysis	for	the	rest	of	the	 outcome	 categories	 (Pneumonia,	 Cardiac	 complications,	 UTI,	 VTE,	 acute	 renal	failure,	OR	return,	death)	because	the	number	of	events	detected	was	too	small	to	draw	 conclusions	 from	 it.	 A	 comparison	 between	 the	 sums	 of	 observed	 outcomes	and	 the	 sums	 of	 the	 detected	 outcomes	were	 performed	 and	 it	 showed	 that	 VTE	predicted	 and	 observed	 events	 were	 similar,	 while	 Pneumonia	 and	 UTI	 showed	higher	observed	events	and	all	other	outcome	categories	showed	a	lower	number	of	observed	 events	 than	 predicted	 by	 the	 model.	 However,	 these	 findings	 must	 be	interpreted	cautiously	given	the	small	number	of	events.		We	observed	a	total	of	19%	of	overall	complications	in	our	sample	population.	This	percent	 is	 still	 considered	 high	 and	 greater	 efforts	 are	 required	 to	 address	 this	problem	 in	 order	 to	 find	 new	 methods	 for	 improvement.	 It	 is	 possible	 the	prospective	 application	 of	 the	 RC	 in	 advance	 of	 surgery	 for	 this	 population	 could		28	allow	better	preoperative	preparation	and	enhance	partnerships	between	patients,	surgeons,	 anesthesiologists	 and	 nurses,	 but	 this	 possibility	 remains	 unproven.	 As	ERAS	 standardized	 protocols	 improve	 in	 this	 population,	 the	 overall	 complication	rate	is	trending	down	in	slow	steps	and	this	was	noticed	in	comparison	to	a	recent	study	done	in	the	same	center	with	similar	characteristics	of	the	sample	population	which	 showed	 an	 overall	 complication	 rate	 of	 22%.	 SSI	 remained	 the	 outcome	 of	concern	with	 a	 rate	 of	 14%	 in	 both	 studies	 (30,	 personal	 communication	with	Dr.	Garth	Warnock,	March	2016).		Accurate	assessment	of	treatment	risks	is	an	important	aid	for	good	decision-making	and	 a	 recent	 study	 published	 in	 the	 literature	 found	 that	 providing	 surgeons	with	objective	 data	 from	 a	 well-validated	 risk	 calculator	 resulted	 in	 improved	 and	 less	varied	judgments	of	operative	risks	that	more	closely	approximate	the	risk	calculator	values	 (38).	 	Due	 to	 this	 fact,	our	study	 tried	 to	 focus	on	one	of	 the	 tools	 that	are	currently	available	and	proof	 its	validation	or	suggest	a	simple	"fix"	to	 improve	the	model	and	make	the	tool	better	utilized	clinically.	The	 current	 study	 aims	 to	 determine	 if	 the	 RC	 is	 accurate	 in	 a	 colon	 resection	population.	 This	 is	 unique	 in	 the	 literature	 and	essential	 step	 to	 incorporating	 this	tool	 in	 preoperative	 clinics.	 The	 calculator	 can	 be	 completed	 by	 surgeon	 or	anesthesiologists	 in	 the	 preoperative	 assessment	 period	where	 they	 can	 have	 the	chance	to	discuss	the	results	with	patients	to	make	a	shared	decision	about	the	best	quality	of	care	provided	focusing	on	individual	patient	risk	factors	and	needs.				2.3.2	Limitations			There	were	 several	 limitations	 in	 this	 study	 that	were	 recognized.	First,	 the	reliance	on	historical	data	which	exposed	the	study	to	the	issue	of	missing	data	and	followed	by	patients	exclusion	due	 to	 this	 fact.	 	 Secondly,	 the	 sample	 size	and	we	were	unable	to	rigorously	assess	calibration,	thus	our	findings	should	be	interpreted	cautiously.	 Third,	our	 sample	population	was	based	on	patients	 treated	 in	a	 single	tertiary	 referral	 center.	 Therefore,	 the	 results	 are	unlikely	 to	be	generalizable	 to		29	other	 centers.	 Despite	 the	 limitations,	 our	 study	 has	made	 unique	 contribution	 to	the	 literature	 by	 assessing	 the	 calibration	 of	 the	 RC	 prediction	 model,	 including	prediction	among	the	three	 levels	of	 risk	adjustment.	This	allowed	us	to	test	 if	 the	model	can	be	improved	by	simply	raising	the	level	of	risk	adjustment	instead	of	just	stating	that	this	model	is	not	appropriately	predicting	postoperative	outcomes.				2.4	Conclusion			Prediction	models	for	treatment	risks	are	of	crucial	benefits	for	surgeons	to	predict	 their	 decision	 to	 operate.	 They	 can	 also	 allow	 patients	 to	 understand	 and	comprehend	 the	measures	of	 the	possible	 risks	 associated	with	 their	 treatment	 in	order	to	reach	a	final	decision	and	sign	the	informed	consent.		For	 a	 risk	 prediction	 model	 implementation	 in	 a	 clinical	 system,	 it	 must	 have	 a	rigorous	 proof	 of	 its	 validity	 and	 generalizability.	 Our	 study	 focused	 on	 the	 ACS	NSQIP	 Surgical	Risk	Calculator,	 tool	 generated	 from	a	 very	well	 collected	data	 and	vast	sample	size.	Calibration	of	the	model	was	mainly	examined	in	three	categories	of	 NSQIP	 outcomes	 for	 any	 complication,	 serious	 complication	 and	 SSI	 categories	which	 showed:	 any	 complication	 and	 serious	 complication	 predictions	 were	 fairly	accurate	but	SSI	prediction	was	lower	than	the	actual.	By	adjusting	the	level	of	risk,	model	 predictions	 improved	 notably.	 Length	 of	 postoperative	 hospital	 stay	 was	examined	too	but	the	model	showed	inaccurate	predictions	for	this	variable.	Operationally,	results	from	the	study	to	date	would	mean	using	the	values	predicted	by	 the	 RC	 for	 all	 outcomes	 including	 those	 less	 frequent,	 because	 we	 have	 no	evidence	to	support	not	using	them.		We	 conclude	 that	 this	 tool	 is	 a	 potentially	 useful	 tool	 in	 this	 population	 of	 colon	resection	patients	but	we	recommend	further	analysis	to	be	conducted	to	test	all	the	ACS	 NSQIP	 Surgical	 Risk	 Calculator	 predicted	 outcomes	 after	 obtaining	 larger		30	number	of	events	to	properly	validate	the	model	and	reach	a	solid	conclusion	about	its	level	of	accuracy	as	planned	for	the	ongoing	part	of	this	study.				2.5	Future	plan			All	that	have	been	done	so	far	is	considered	as	phase	I	of	the	project	that	we	proposed	and	it	is	the	initial	step	to	move	forward	towards	the	ultimate	goal	that	we	have	set	and	trying	to	achieve.		Further	data	will	be	collected	for	validating	the	tool	in	the	study	population	giving	our	center	continues	to	collect	NSQIP	data	on	all	colon	resection	patients	who	enter	ERAS	program.	Also	continuing	the	project	will	give	us	the	privilege	of	increasing	the	sample	size,	which	will	increase	the	overall	number	of	events	and	the	number	of	events	per	category	of	complication.	The	next	step	will	be	moving	 to	phase	 II	where	we	will	be	collecting	 the	data	prospectively	 to	avoid	 the	problem	of	missing	data	when	depending	on	hospital	records	alone.	This	will	allow	us	 to	 assess	 the	 model	 calibration	 for	 more	 outcome	 categories	 and	 perhaps	recommend	 it	 as	 a	 useful	 preoperative	 tool	 placed	 on	 the	 chart	 for	 patients,	surgeons,	 nurses	 and	 anesthesiologist	 to	 incorporate	 into	 the	 preadmission	 clinic	protocols	or	define	a	certain	recommendation	to	prepare	patients	for	a	safe	surgical	experience.																	31			Bibliography		1.	 Sandblom	G,	Videhult	P,	Crona	Guterstam	Y,	Svenner	A,	Sadr	Azodi	O.	 Mortality	 after	 a	 cholecystectomy:	 a	 population-based	 study.	HPB.	2015	Mar;17(3):239–43.		2.	 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Steyerberg	EW,	Borsboom	GJ,	Van	Houwelingen	HC,	 Eijkemans	MJ,	Habbema	 JD.	 	 	 	 Validation	 and	 updating	 of	 predictive	 logistic	regression	models:	a	study	on	sample	size	and	shrinkage.	Statistics	in	Medicine.	2004;	23:2567–2586.	38.			39.	Sacks	GD,	Dawes	AJ,	Ettner	SL,	Brook	RH,	Fox	CR,	Russell	MM,	et	al.	Impact	of	a	Risk	Calculator	on	Risk	Perception	and	Surgical	Decision	Making.	Annals	of	Surgery.	2016	May;:1–7.		Sacks	GD,	Dawes	AJ,	Ettner	SL,	Brook	RH,	Fox	CR,	Maggard-Gibbons	M,	 et	 al.	 Surgeon	 perception	 of	 risk	 and	 benefit	 in	 the	 decision	 to	operate.	Annals	of	Surgery.	2016	May.							35	Appendices				Appendix	A:	UBC	M&M	Tool	webpage			Figure	(1)	UBC	M&M	Tool	website	page		36				Figure	(2)	UBC	M&M	Tool	website	page						37	Appendix	B:		ACS	NSQIP	Surgical	Risk	Calculator	software	example									Figure	(1)	Data	required	in	the	RC	software		Figures	(1)	&	(2)	Screenshots	of	the	ACS	NSQIP	Surgical	Risk	Calculator	(http://riskcalculator.facs.org).		(A)	Risk	factor	entry	screen.	(B)	Example	of	report	screen.																38			Figure	(2)	Example	of	report	screen		Figures	(1)	&	(2)	Screenshot	of	the	ACS	NSQIP	Surgical	Risk	Calculator	(http://	riskcalculator.facs.org)(A)	Risk	factor	entry	screen.	(B)	Example	of	report	screen.						

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