Lifetime vs. 10-year risk to allocate treatments for the primary prevention of cardiovascular disease

Transcription

1 Lifetime vs. 10-year risk to allocate treatments for the primary prevention of cardiovascular disease Eleni Rapsomaniki Farr Institute for Health Informatics Research, Epidemiology & Public Health University College London

2 Motivation Screening for primary prevention of CVD NHS Health Check, screening program in England since 2008 Eligible population is healthy, aged Invited for screening every 5 years. Statins and other appropriate medication offered to those at high risk. Treatment based on predicted 10-year CVD risk. Treatment rule: treat if 10-year risk = 20% 2

3 Alternative treatment rule: Lifetime CVD risk Criticism of 10-year risk based treatment is that anyone aged 70+ will be treated on the basis of age alone, even if risk factors optimal Lifetime risks (to 95 years) more likely to identify people with high risk due to risk factors other than age

4 Net benefit of treatment decisions Population at risk Apply a model to predict risk Treat people with high risk BENEFIT COST NET BENEFIT e.g. averting or delaying a heart attack medication, adverse effects, inconvenience, etc Benefit and cost quantified in Event-Free Life-Years (EFLYs) Rapsomaniki et al, Stats in Med, 2011

5 Net benefit of treatment decisions Population at risk Apply a model to predict risk Treat people with high risk 10-year risk or lifetime risk? BENEFIT COST NET BENEFIT e.g. averting or delaying a heart attack medication, adverse effects, inconvenience, etc Benefit and cost quantified in Event-Free Life-Years (EFLYs)

6 DATA CVD-free patients, aged at baseline Mean (SD), % or median Total N=100,000 (IQR) Age, years 54.1 (9.5) Females 56% Diabetic 4.8% Current smoker 21% Total cholesterol, mmol/l 5.7 (1.2) Systolic BP, mmhg (17.8) Follow-up, years 9.0 ( ) CVD events 4,971 Data source: CALIBER (Linked electronic health records between primary care, secondary care and deaths), study period

7 Estimation of 10-year and lifetime risk Cox model with age as timescale Competing risks adjusted CVD risk factors are stronger at younger ages so added interaction with age Hazard at age t years is where: h 0 (t) baseline hazard at age t bvector of coefficients h(t) = h 0 (t)e βx xvector of covariates, includes (: denotes interaction, RCS restr.cubic splines) age, sex, diabetes, smoking:age, TCHOL:age, HDL:age,RCS(SBP,n.knots=3):age (SBP has U shape association with non-cvd deaths)

8 Frequency Frequency Risk distribution 10-year CVD risk Lifetime CVD risk year CVD risk Lifetime CVD risk median (IQR) 4.8 ( ) % 34.1 ( ) %

9 Patients treated under each rule

10 Screening/treatment framework aged at baseline screen Apply treatment rule Treated - risk > threshold - allocated to treatment earlier Net Benefit for 5 years 5 years Not treated update covariates age=+5 years x i (t)=x0 i + bt i + e i... [mixed models from available repeats] aged 95+ Cumulative Net Benefit = sum of 5-year NB from age screened to age 95

11 S(t) Estimation of Net Benefit S(T) q treated Relative treatment cost k : from external sources Treatment HR q from trials Treatment cutoff c(t): risk(t) where benefit=cost S(T) Benefit ( T) in treated untreated T q S( u) S( u) du Time T u Cost( T ) in treated k T u S( u) q du Relative Cost k example: - EFLY equivalent to 20,000 -Treatment costs 400 pp py: k ,000 2% Net Benefit n treated N screened ( Benefit ( T) treated Cost( T) treated ) 12 Rapsomaniki et al, Stats in Med, 2011

12 Assumptions Treatment benefit: Statin treatment reduces total cholesterol by 1.5 (SD=0.5) mmol/l Blood pressure lowering drugs lower systolic blood pressure by 10 (SD=3) mmhg. Offered only if blood pressure in the hypertensive range. on average these reduce risk with HR~ 0.8 Treatment cost pppy (k) = 2% of an event-free life year i.e. if EFLY is worth 20,000, treatment costs 400 pppy this cost balances benefit assuming treatment HR 0.8 and treatment threshold at 20% 10-year risk

13 HR for treatment risk reduction Mean age at treatment Results Age at treatment and treatment benefit Treatment benefit vs. age Mean age at treatment 10-year risk Lifetime risk Age at treatment Screening interval

14 Treatment rate % Net benefit/100 screened Cumulative Net benefit/100 screened Net benefit/100 screened Results Treatment rate and net benefit Treatment rate Net Benefit per 5-year interval Cumulative Net Benefit Difference in cumulative net benefit 10-year risks Lifetime risks Screening interval Screening interval Screening interval Screening interval

15 Conclusions Using lifetime risk to allocate treatments is less cost-effective initially, equally cost-effective at ~20 years, and more costeffective after 20 years So for short-term benefit 10-year risk is the best rule, but for to select candidates to treat over long periods using lifetime risks proves more cost-effective in the long run. 16

16 Limitations/further work Sensitivity analysis on treatment thresholds, costs, and treatment effects Lifetime risks are crude (accuracy impossible to establish with current data). Use different rules, e.g. treat those with linear predictors too high for their age Account for reduced compliance/benefit over time Treatment effect may be modified by length of treatment Trials report short-term treatment benefit and harms, no data on long-term benefit (some data is emerging from cohorts that were followed-up after the end of the trial)

17 Acknowledgements Ian White (BSU) Colleagues at Farr Institute For collaborations using electronic health records data visit: Farr London