title: Personalized prediction of lifetime benefits with statin therapy for asymptomatic individuals: a modeling study.
abstract: BACKGROUND
  Physicians need to inform asymptomatic individuals about personalized outcomes of statin therapy for primary prevention of cardiovascular disease (CVD). However, current prediction models focus on short-term outcomes and ignore the competing risk of death due to other causes. We aimed to predict the potential lifetime benefits with statin therapy, taking into account competing risks.
  
  
  RESULTS
  A microsimulation model based on 5-y follow-up data from the Rotterdam Study, a population-based cohort of individuals aged 55 y and older living in the Ommoord district of Rotterdam, the Netherlands, was used to estimate lifetime outcomes with and without statin therapy. The model was validated in-sample using 10-y follow-up data. We used baseline variables and model output to construct (1) a web-based calculator for gains in total and CVD-free life expectancy and (2) color charts for comparing these gains to the Systematic Coronary Risk Evaluation (SCORE) charts. In 2,428 participants (mean age 67.7 y, 35.5% men), statin therapy increased total life expectancy by 0.3 y (SD 0.2) and CVD-free life expectancy by 0.7 y (SD 0.4). Age, sex, smoking, blood pressure, hypertension, lipids, diabetes, glucose, body mass index, waist-to-hip ratio, and creatinine were included in the calculator. Gains in total and CVD-free life expectancy increased with blood pressure, unfavorable lipid levels, and body mass index after multivariable adjustment. Gains decreased considerably with advancing age, while SCORE 10-y CVD mortality risk increased with age. Twenty-five percent of participants with a low SCORE risk achieved equal or larger gains in CVD-free life expectancy than the median gain in participants with a high SCORE risk.
  
  
  CONCLUSIONS
  We developed tools to predict personalized increases in total and CVD-free life expectancy with statin therapy. The predicted gains we found are small. If the underlying model is validated in an independent cohort, the tools may be useful in discussing with patients their individual outcomes with statin therapy.
authors.affiliation: Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands.
authors.forename: Bart S
authors.initials: BS
authors.lastname: Ferket
authors.forename: Bob J H
authors.initials: BJ
authors.lastname: van Kempen
authors.forename: Jan
authors.initials: J
authors.lastname: Heeringa
authors.forename: Sandra
authors.initials: S
authors.lastname: Spronk
authors.forename: Kirsten E
authors.initials: KE
authors.lastname: Fleischmann
authors.forename: Rogier L G
authors.initials: RL
authors.lastname: Nijhuis
authors.forename: Albert
authors.initials: A
authors.lastname: Hofman
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.forename: M G Myriam
authors.initials: MG
authors.lastname: Hunink
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D058070:Asymptomatic Diseases / Q000628:therapy*; D001786:Blood Glucose; D001794:Blood Pressure; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000401:mortality* / Q000517:prevention & control*; D016001:Confidence Intervals; D003404:Creatinine / Q000097:blood; D003920:Diabetes Mellitus / Q000453:epidemiology; D018572:Disease-Free Survival; D005260:Female; D005544:Forecasting / Q000379:methods*; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use*; D006973:Hypertension / Q000453:epidemiology; D008017:Life Expectancy; D008055:Lipids / Q000097:blood; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012737:Sex Factors; D012907:Smoking; D049629:Waist-Hip Ratio
chemical_list: D001786:Blood Glucose; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D008055:Lipids; D003404:Creatinine