title: Personalized cardiovascular disease prevention by applying individualized prediction of treatment effects.
abstract: Large-scale randomized clinical trials have established the efficacy of cholesterol-lowering, blood pressure-lowering, and anti-platelet therapy to prevent cardiovascular diseases. A challenge for clinicians is to apply group-level evidence from these trials to individual patients. Trials typically report a single treatment effect estimate which is the average effect of all participants, comprising patients who respond poorly, intermediately, and well. Clinicians would preferably make patient-tailored treatment decisions. Therefore, one would require an estimate of an individual patient's response to therapy. Although not yet widely recognized, trials contain this type of information. In this paper, we show how available information from landmark trials can be translated to an individual 'treatment score' through the use of multivariable therapeutic prediction models. These models provide an individual estimate of the absolute risk reduction in cardiovascular events given the specific combination of multiple clinical characteristics of a patient under care. Based on this individualized treatment estimate and metrics such as the individual number-needed-to-treat, clinicians together with their patients can decide whether drug treatment or what treatment intensity is worthwhile. Selective treatment of those who can anticipate the greatest benefit and the least harm on an individualized basis could reduce the number of unnecessary treatments and healthcare costs beyond that currently achievable by subgroup analyses based on single patient characteristics.
authors: Department of Vascular Medicine, University Medical Center Utrecht, the Netherlands.
authors: Joep
authors: J
authors: van der Leeuw
authors: Paul M
authors: PM
authors: Ridker
authors: Yolanda
authors: Y
authors: van der Graaf
authors: Frank L J
authors: FL
authors: Visseren
mesh_terms: D002318:Cardiovascular Diseases / Q000517:prevention & control*; D005260:Female; D005464:Fluorobenzenes / Q000627:therapeutic use; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use; D008297:Male; D008875:Middle Aged; D061366:Numbers Needed To Treat; D057285:Precision Medicine / Q000379:methods*; D011364:Professional Practice; D011743:Pyrimidines / Q000627:therapeutic use; D016032:Randomized Controlled Trials as Topic; D012307:Risk Factors; D000068718:Rosuvastatin Calcium; D055502:Secondary Prevention / Q000379:methods; D013449:Sulfonamides / Q000627:therapeutic use
chemical_list: D005464:Fluorobenzenes; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D011743:Pyrimidines; D013449:Sulfonamides; D000068718:Rosuvastatin Calcium
keywords: Individualized cardiovascular medicine; Net benefit; Prediction of treatment effect