Laboratory and non-laboratory-based risk prevention models for secondary prevention of cardiovascular disease : the LIPID study

Cui, Jisheng, Forbes, Andrew, Kirby, Adrienne, Simes, John and Tonkin, Andrew 2009, Laboratory and non-laboratory-based risk prevention models for secondary prevention of cardiovascular disease : the LIPID study, European journal of cardiovascular prevention and rehabilitation, vol. 16, no. 6, pp. 660-668.

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Title Laboratory and non-laboratory-based risk prevention models for secondary prevention of cardiovascular disease : the LIPID study
Author(s) Cui, Jisheng
Forbes, Andrew
Kirby, Adrienne
Simes, John
Tonkin, Andrew
Journal name European journal of cardiovascular prevention and rehabilitation
Volume number 16
Issue number 6
Start page 660
End page 668
Publisher Lippincott Williams & Wilkins
Place of publication Philadelphia, Pa.
Publication date 2009-12-01
ISSN 1741-8267
2047-4881
Keyword(s) cardiovascular disease
risk prediction
secondary prevention
Summary Aims : 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.

Methods and 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.

Conclusion : 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.
Language eng
Field of Research 111706 Epidemiology
Socio Economic Objective 920502 Health Related to Ageing
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2009, Lippincott Williams & Wilkins
Persistent URL http://hdl.handle.net/10536/DRO/DU:30022926

Document type: Journal Article
Collection: Public Health Research, Evaluation, and Policy Cluster
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