Cholesterol behandeling bij de individuele patient; wie wel en wie niet

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2 Cholesterol behandeling bij de individuele patient; wie wel en wie niet WCN symposium Frank LJ Visseren

3 Disclosures Research support: ZonMw, Vrienden van UMC Utrecht, Stichting Leatare Trials: Merck, Regeneron, Amgen, Sanofi, Pfizer, Genzyme Verder: niks

4 De individuele patient

5 Uitdaging voor de clinicus Vertalen van resultaten van (grote) trials naar de behandeling van individuele patienten

6 Number needed to treat INTERVENTIE EINDPUNT NNT Bloeddruk verlaging MI/CVA/dood 30 (5 jaar) Cholesterol verlaging MI/CVA/dood 30 (5 jaar) Antistolling bij AF CVA/dood 20 (5 jaar) Carotis endarteriectomie CVA Cholecystectomie complicatie/dood

7 Individualized prediction of treatment effects

8 Rationale Cardiovascular disease runs in my family. Do I need to use a statin? Does that mean that I will benefit too? Research shows the average patient benefits from statin.

9 Translating the results of trials to clinical practice Trials report a single average relative effect. Applicable to which patiënt? Average patiënt? There is a range of treatment effect! Presumably based on patiënt characteristics. One treatment fits all is an oversimplification of clinical reality.

10 Average patient???

11 Baseline tables. NEJM 2010;363:

12 Average patient? % female

13 Average patient? 70% male % female

14 Average patient? years old

15 Average patient? 70 years old years old

16 Average patient? Average treatment? Current practice: providing average treatment to an average patient! Large Numbers Needed to Treat (>30 in 10 years to prevent 1 outcome event). Unable to identify individual patients that have large benefit from treatment and have little/no harm.

17 Not every patient benefits from treatment! Glasziou et al, BMJ Nov 18;311(7016):

18 Subgroup analyses are not the solution! Univariable analyses of patient characteristics that modify relative treatment effect Disadvantages: Multiple testing (FP) Loss of statistical power One patient characteristic at the time: Full range of treatment effect heterogeneity remains undiscovered Correlation of characteristics (FN) Does not address heterogeneity of absolute treatment effect

19 SHARP (resultaten) Lancet 2011;377:

20 Then what do we need? A model for estimation of absolute treatment effect for individual patients, based on multiple characteristics together......allowing to test heterogeneity of both absolute and relative treatment effect......while minimizing the risk for FP and FN results. Applicable before start of intended therapy Also applicable in fields of medicine where risk scores are not yet available

21 ??? Genes? Biomarkers? Imaging?? Solution: individualized prediciton of treatment effects by modelling based on available information!

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23 Aims To show how absolute treatment effect for individual patients can be predicted based on data from randomised trials. To evaluate the net benefit of making treatment decisions based on individual predicted absolute treatment effect.

24 JUPITER Primary Trial Endpoint : MI, Stroke, UA/Revascularization, CV Death Cumulative Incidence Ridker et al NEJM 2008 HR 0.56, 95% CI P < Placebo 251 / % Rosuvastatin 142 / 8901 Number at Risk Rosuvastatin Placebo Follow-up (years) 8,901 8,631 8,412 6,540 3,893 1,958 1, ,901 8,621 8,353 6,508 3,872 1,963 1,

25 Strategies for predicting treatment effect BMJ 2011;343:d5888

26 Model performance C-statistic 0.65 (95%CI ) Concordance statistic: rcorr.cens() from Hmisc library in R C-statistic 0.66 (95%CI ) Based on Somers' rank correlation for censored response variabel: Dxy / C-statistic 0.71 (95%CI ) Probability of pairs of subjects who can be ordered such that the subject with the higher predicted survival is the one who survived longer BMJ 2011;343:d5888

27 Distribution of individual treatment effect Framingham based Reynolds based Optimal fit model based Median 4.4% (IQR %) Mean 5.1% Median 4.2% (IQR %) Mean 5.4% Median 3.9% (IQR %) 4.5% BMJ 2011;343:d5888

28 Individualized treatment effect prediction Age Gender Current smoking BP lowering meds Family Hx CHD LDLc hscrp Diabetes 60 years male no yes positive 2.8 mmol/l 4.3 mg/l No

29 Individualized treatment effect prediction Age Gender Current smoking BP lowering meds Family Hx CHD LDLc hscrp Diabetes 55 years female yes no negative 2.8 mmol/l 4.3 mg/l No

30 Net benefit of individualized treatment effect prediction (based on JUPITER) BMJ 2011;343:d5888

31 A prediction rule for the effect of high-dose statin therapy in patients with stable coronary artery disease based on data from the TNT and IDEAL trials [to be submitted soon] J.A. Dorresteijn, S.M. Boekholdt, Y. van der Graaf, J.J. Kastelein, J.C. LaRosa, T.R. Pedersen, D.A. DeMicco, P.M Ridker, N.R. Cook, F.L. Visseren

32 Prediction model for estimating the effect of highdose vs usual dose statin on CV risk reduction TNT IDEAL N Engl J Med 2005;352: JAMA 2005;294: What is your 5-year Number Willing to Treat (NWT) for atorvastatin 80mg vs low-dose statin therapy? (Harm of 1 MCVE = Harm of... persons treated)

33 Prediction model for estimating the effect of highdose vs usual dose statin on CV risk reduction

34 Observed 5-year event free survival Observed 5-year event free survival Model validation Predicted 5-year event free survival Predicted 5-year event free survival Derivation cohort (TNT): C-statistic: 0.67 (95% CI ) G&B-test p-value: 0.65 Validation cohort (IDEAL): C-statistic: 0.63 (95% CI ) G&B-test p-value: 0.30

35 Frequency Distribution of predicted baseline risk Predicted 5yr treatment effect of high dose statin NNT >50: 41.9% of patients NNT 25 to 50: 46.4% of patients NNT 17 to 25: 9.4% of patients NNT 12 to 17: 2.0% of patients NNT 12 or lower: 0.3% of patients Median [IQR]: 2.2% [1.6 to 3.1%] 0% 2% 4% 6% 8% 10% Absolute reduction of MCVE-risk

36 Model presentation

37 Results prediction model

38 Net benefit Net benefit 5 year Number Willing to Treat (NWT) infinite All usual-dose statin Tx All atorvastatin 80mg Prediction-based Tx Selective high-dose Tx of MS/DM-patients NB = (TP - ωfp) / N TP = True positives FP = False positives ω = Pr T / (1-Pr T ) Decision-threshold (% 5-yr MCVE-risk on usual-dose statin)

39 Clinical interpretation

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41 Chol: RR: Stolling: Patiënten met verhoogd risico: Patiënten met vaatziekten: Patiënten met diabetes:

42 App store: CVRM

43 Conclusion It is possible to predict the absolute treatment effect for an individual patient before start of a (any) given treatment. Results of clinical trials should not only be reported on group level, but also provide a prediction rule for estimating absolute treatment effect for individual patients.

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