title: Identification of risk factors in hypertensive patients: contribution of randomized controlled trials through an individual patient database.
abstract: BACKGROUND
  Predicting individual risk is needed to target preventive interventions toward people with the highest probability of benefit over a given time period. We assessed which prognostic factors should be used in predicting risk for hypertensive patients and in searching for treatment modifiers.
  
  
  RESULTS
  Data from 24 390 hypertensive participants who constituted the control groups from 8 controlled trials (1726 deaths over 5 years) were analyzed in multivariate survival models. Outcomes were coronary heart disease death, stroke death, and cardiovascular death. We explored systematically the heterogeneity of results between trials. Left ventricular hypertrophy was electrocardiographically confirmed to be a powerful risk factor and should be included in risk scoring. Height, glomerular filtration rate, and serum uric acid deserve further exploration. Body mass index and heart rate were not confirmed as independent cardiovascular risk factors in this population. The association between male sex and coronary heart disease death was significantly stronger in British cohorts. The lack of prognostic value of diastolic blood pressure was explained by an interaction with age, with a positive association before 65 years and a negative association thereafter. Previous antihypertensive treatment was a significant risk factor.
  
  
  CONCLUSIONS
  Clinical trials provide valuable information for risk prediction. Carefully exploring the heterogeneity among trials is a way to assess the generalizability of findings. This approach, if systematically performed, should increase the ability to identify risk modifiers and to predict individual therapeutic benefit.
authors.affiliation: Clinical Pharmacology Department, Claude Bernard University, Lyon Hospitals, Lyon, France. fg@upcl.univ-lyon1.fr
authors.forename: F
authors.initials: F
authors.lastname: Gueyffier
authors.forename: J P
authors.initials: JP
authors.lastname: Boissel
authors.forename: S
authors.initials: S
authors.lastname: Pocock
authors.forename: F
authors.initials: F
authors.lastname: Boutitie
authors.forename: J
authors.initials: J
authors.lastname: Coope
authors.forename: J
authors.initials: J
authors.lastname: Cutler
authors.forename: T
authors.initials: T
authors.lastname: Ekbom
authors.forename: R
authors.initials: R
authors.lastname: Fagard
authors.forename: L
authors.initials: L
authors.lastname: Friedman
authors.forename: K
authors.initials: K
authors.lastname: Kerlikowske
authors.forename: M
authors.initials: M
authors.lastname: Perry
authors.forename: R
authors.initials: R
authors.lastname: Prineas
authors.forename: E
authors.initials: E
authors.lastname: Schron
mesh_terms: D000367:Age Factors; D000368:Aged; D002318:Cardiovascular Diseases / Q000209:etiology / Q000401:mortality*; D003327:Coronary Disease / Q000209:etiology / Q000401:mortality; D005260:Female; D006801:Humans; D006973:Hypertension / Q000150:complications* / Q000628:therapy; D008297:Male; D015999:Multivariate Analysis; D016017:Odds Ratio; D011379:Prognosis; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012307:Risk Factors; D012737:Sex Factors; D020521:Stroke / Q000209:etiology / Q000401:mortality; D016019:Survival Analysis