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STAR Webinar - December 20th, 2012 Stroke POpulation Risk Tool.

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Presentation on theme: "STAR Webinar - December 20th, 2012 Stroke POpulation Risk Tool."— Presentation transcript:

1 STAR Webinar - December 20th, 2012 Stroke POpulation Risk Tool

2 STAR Webinar - December 20th, 2012 What is a Stroke?

3 STAR Webinar - December 20th, 2012

4 Effects of a Stroke

5 STAR Webinar - December 20th, 2012 Stroke Prediction Models FraminghamUSA ScoreAustralian Interstroke22 Countries QriskEngland-Wales 1993 – 2008 SPoRTCanada

6 STAR Webinar - December 20th, 2012 Development Cohort CCHS 1.132,848Sep 2000/Nov 2001 CCHS ,679Jan 2003 / Jan 2004 CCHS 3.133,402Jan 2005 / Jan 2006 Linked to Ontario health administration data until March 31, Registered Person Database CIHI/DAD

7 STAR Webinar - December 20th, 2012 Development Cohort (CCHS 1.1 – CCHS 3.1)

8 STAR Webinar - December 20th, 2012 Restrictions Risk factors available in public use files Excluding intermediate (ex. BMI) risk factors

9 STAR Webinar - December 20th, 2012 Competing Risk The type of failure that prevents the observation or fundamentally alters the probability of the occurrence of the event of interest. Death is a competing risk for stroke

10 Outcome Diagnostic codes for stroke were taken from Canadian Stroke Network Time to stroke STAR Webinar - December 20th, 2012

11 Variables STAR Webinar - December 20th, 2012 Age Smoking Alcohol Stress Physical activity Diet Education High blood pressure Diabetes

12 Risk Factor Categories Risk BehaviourCategoryDefinition SmokingHeavy smokerDaily current smoker (1 pack/day) Light smokerDaily current smoker (<1 pack/day) Former smokerFormer daily smoker Non-smokerFormer occasional smoker or never smoker AlcoholHeavy drinker >24 (men) or >17 (women) drinks/week in previous month or at least one binging in a week Moderate drinker5 to 24 (men) or 3 to 17 (women) drinks/week Light drinker0 to 4 (men) or 0 to 2 (women) drinks/week Occasional drinker<1 drink/month Current non-drinkerNo alcohol consumption in the last 12 months Physical activityInactive0 to <1.5 METs/day Moderately active1.5 to <3 METs/day Active3 METs/day DietPoor dietWeekly vegetable serving <7 Fair diet7 <= Weekly vegetable serving <14 Adequate diet14 <= Weekly vegetable serving Stress Very high stressSelf-perceived stress: quite a bit or extremely Somewhat stressSelf-perceived stress: a bit Low stressSelf-perceived stress: not at all or not very STAR Webinar - December 20th, 2012

13 Univariate Analysis Risk Factors and CategoriesMaleFemale SmokingHR (95% CI) Heavy 1.55 (1.17,2.04)2.20 (1.71,2.84) Light 1.41(1.07,1.84)1.67 (1.36,2.05) Former 1.16 (0.98,1.41)1.02 (0.87,1.2) Non-smokerRef. Alcohol Heavy drinker 1.25 (0.93,1.70)1.12 (0.59,2.13) Moderate drinkerRef. Light drinker 0.99 (0.81,1.21)1.10 (0.88,1.38) Occasional drinker 1.09 (0.84,1.44)1.20 (0.95, 1.52) Current non-drinker 1.18 (0.95,1.47) 1.43 (1.16,1.76) Physical activity Inactive 1.28 (1.05,1.56)1.25 (1.02,1.54) Moderately active 1.21 (0.96,1.51)1.04 (0.82,1.32) ActiveRef. Diet Poor 1.54 (1.25,1.90)1.43 (1.18,1.73) Fair 1.25 (1.03,1.52)1.25 (1.06,1.46) AdequateRef. Stress Very high 1.22 (0.98,1.52)1.40 (1.16,1.69) Somewhat 1.06 (0.90,1.26)1.04 (0.90,1.22) LowRef.

14 STAR Webinar - December 20th, 2012 The Index Risk Factors and CategoriesMaleFemale SmokingHR (95% CI) Index Score HR (95% CI) Index Score Heavy 1.55 (1.17,2.04) (1.71,2.84) 4 Light 1.41(1.07,1.84) (1.36,2.05) 3 Former 1.16 (0.98,1.41) (0.87,1.2) 1 Non-smokerRef.0 0 Alcohol Heavy drinker 1.25 (0.93,1.70) (0.59,2.13) 2 Moderate drinkerRef.0 0 Light drinker 0.99 (0.81,1.21) (0.88,1.38) 1 Occasional drinker 1.09 (0.84,1.44) (0.95, 1.52) 1 Current non-drinker 1.18 (0.95,1.47) (1.16,1.76) 2 Physical activity Inactive 1.28 (1.05,1.56) (1.02,1.54) 1 Moderately active 1.21 (0.96,1.51) (0.82,1.32) 0 ActiveRef.0 0 Diet Poor 1.54 (1.25,1.90) (1.18,1.73) 2 Fair 1.25 (1.03,1.52) (1.06,1.46) 1 AdequateRef.0 0 Stress Very high 1.22 (0.98,1.52) (1.16,1.69) 2 Somewhat 1.06 (0.90,1.26) (0.90,1.22) 0 LowRef.0 0

15 Models STAR Webinar - December 20th, 2012 MaleFemale Risk Behavior Index (1.080,1.193)1.175 (1.129,1.222) Age 1.113(1.095,1.131)1.107 (1.090,1.125) Age spline (0.939,0.986) Age time varying ( , ) ( , ) Family Education More than Secondary School Ref. Secondary school or less (1.064,1.588)1.286 (1.077,1.536) Missing (0.757,1.905)0.724 (0.384,1.364) High Blood pressure No Ref. Yes (1.074,1.553)1.527 (1.285,1.814) Missing (0.133,7.154)2.251 (0.387,13.106) Diabetes No Ref. Yes (1.119,1.753)1.773 (1.439,2.184)

16 Model Assessment MaleFemale C-stat (95% CI)0.85 (0.83 – 0.86)0.87 (0.85 – 0.88) 75/ / # O-P > 20% 5/53 3/51 STAR Webinar - December 20th, 2012

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19 Development Cohort (CCHS 4.1) FemaleMaleOverall Records Years follow up # strokes in overall follow-up

20 Development Cohort OverallMaleFemale n=82259n=37483n=44776 MeanSEMeanSEMeanSE Age Gender Male Female STAR Webinar - December 20th, 2012

21 Validation cohort OverallMaleFemale n= 28605n=13032n=15573 MeanSEMeanSEMeanSE Age Gender Male Female STAR Webinar - December 20th, 2012

22 Diagnostic codes Diagnostic codes for stroke were taken from Canadian Stroke Network definition as 362, 3623, 430, 431, 435, 436 for ICD-9 and G45, H340, H34.1, I60, I61, I63, I64 excluding I608, I636, and G454 for ICD-10.

23 STAR Webinar - December 20th, 2012 References: Lets talk about stroke. Heart & Stroke foundation Béland Y. Canadian Community Health Survey. Methodological overview. Health Reports, Vol. 13, No. 3, March 2002 OHIP Eligibility, Ontario Ministry of Health and Long Term Care Ref Type: Online source Pintilie M. Dealing with competing risks: testing covariates and calculating sample size. Stat Med 2002; 21 :

24 Melberg T, Nyg+Ñrd OK, Kuiper KK-J, Nordrehoug JE. Competing risk analysis of events 10 years after revascularization. Scand Cardiovasc J 2010; 44; Walter, Kremers, Concordance for survival time data: Fixed and time-dependent covariates and possible ties in predictor and time. Technical report series #80. April Claudia SanMartin STAR Webinar - December 20th, 2012 References:

25 Statistical evaluation of prognostic versus diagnostic models: Beyond the ROC curve Tripepi, G., Statistical methods for the assessment of prognostic biomarkers (part II); calibration and reclassification. Nephrol. Dial. Transplant 2010 May 25 (5): Epub 2010 Feb 18 Handbook of constructing composite indicators, OECD cysurveys/ pdf STAR Webinar - December 20th, 2012 References:


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