title: Laboratory and non-laboratory-based risk prediction models for secondary prevention of cardiovascular disease: the LIPID study.
abstract: OBJECTIVE
  The aims of this study were to examine whether risk prediction models for recurrent cardiovascular disease (CVD) events have prognostic value, and to particularly examine the performance of those models based on non-laboratory data. We also aimed to construct a risk chart based on the risk factors that showed the strongest relationship with CVD.
  
  
  RESULTS
  Cox proportional hazards models were used to calculate a risk score for each recurrent event in a CVD patient who was enrolled in a very large randomized controlled clinical trial. Patients were then classified into groups according to quintiles of their risk score. These risk models were validated by calibration and discrimination analyses based on data from patients recruited in New Zealand for the same study. Non-laboratory-based risk factors, such as age, sex, body mass index, smoking status, angina grade, history of myocardial infarction, revascularization, stroke, diabetes or hypertension and treatment with pravastatin, were found to be significantly associated with the risk of developing a recurrent CVD event. Patients who were classified into the medium and high-risk groups had two-fold and four-fold the risk of developing a CVD event compared with those in the low-risk group, respectively. The risk prediction models also fitted New Zealand data well after recalibration.
  
  
  CONCLUSIONS
  A simpler non-laboratory-based risk prediction model performed equally as well as the more comprehensive laboratory-based risk prediction models. The risk chart based on the further simplified Score Model may provide a useful tool for clinical cardiologists to assess an individual patient's risk for recurrent CVD events.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia. jisheng.cui@deakin.edu.au
authors.forename: Jisheng
authors.initials: J
authors.lastname: Cui
authors.forename: Andrew
authors.initials: A
authors.lastname: Forbes
authors.forename: Adrienne
authors.initials: A
authors.lastname: Kirby
authors.forename: John
authors.initials: J
authors.lastname: Simes
authors.forename: Andrew
authors.initials: A
authors.lastname: Tonkin
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001315:Australia / Q000453:epidemiology; D002318:Cardiovascular Diseases / Q000401:mortality / Q000517:prevention & control*; D002423:Cause of Death; D003955:Diagnostic Tests, Routine; D016002:Discriminant Analysis; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D015337:Multicenter Studies as Topic; D009520:New Zealand / Q000453:epidemiology; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D055502:Secondary Prevention; D013997:Time Factors