Laboratory and non-laboratory-based risk prediction models for secondary prevention of cardiovascular disease: the LIPID study.
ABSTRACT 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.
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.
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.
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ABSTRACT: A marker strongly associated with outcome (or disease) is often assumed to be effective for classifying persons according to their current or future outcome. However, for this assumption to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiologic studies. In this paper, an illustration of the relation between odds ratios and receiver operating characteristic curves shows, for example, that a marker with an odds ratio of as high as 3 is in fact a very poor classification tool. If a marker identifies 10% of controls as positive (false positives) and has an odds ratio of 3, then it will correctly identify only 25% of cases as positive (true positives). The authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker's ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. In addition, the serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.American Journal of Epidemiology 06/2004; 159(9):882-90. · 4.78 Impact Factor
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ABSTRACT: To evaluate the predictive accuracy of the Systematic Coronary Risk Evaluation (SCORE) project high-risk function in Norway. We included 57 229 individuals screened in 1985-1992 from two population-based surveys in Norway (age groups 40-49, 50-59, and 60-69 years). The data have been linked to the Norwegian Cause of Death Registry. The SCORE high-risk algorithm for the prediction of 10-year cardiovascular disease (CVD) mortality was applied, and the risk factors entered into the model were age, sex, total cholesterol, systolic blood pressure, and smoking (yes/no). The number of expected events estimated by the SCORE model (E) was compared with the observed numbers (O). The SCORE low-risk algorithm was studied for comparison. In men, the observed number of CVD deaths was 718, compared with 1464 estimated by the SCORE high-risk function (O/E ratios 0.53, 0.53 and 0.45, for age groups 40-49, 50-59 and 60-69, respectively). In women, the observed and expected numbers were 226 and 547. The O/E ratios decreased with age (ratios 0.60, 0.45 and 0.37, respectively), i.e. the overestimation increased with age. The low-risk function predicted reasonably well for men (ratios 0.85, 0.92 and 0.79, respectively), whereas an overestimation was found for women aged 50-59 and 60-69 years (ratios 0.69 and 0.56, respectively). The SCORE high-risk model overestimated the number of CVD deaths in Norway. Before implementation in clinical practice, proper adjustments to national levels are required.European Journal of Cardiovascular Prevention and Rehabilitation 09/2007; 14(4):501-7. · 2.63 Impact Factor
- Atherosclerosis 05/2004; 173(2):381-91. · 3.71 Impact Factor