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Prediction of coronary artery disease risk based on multiple longitudinal biomarkers
Author(s) -
Yang Lili,
Yu Menggang,
Gao Sujuan
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6754
Subject(s) - medicine , coronary artery disease , cohort , disease , predictive modelling , blood pressure , clinical practice , cardiology , framingham risk score , computer science , physical therapy , machine learning
In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well‐documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow‐up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort. Copyright © 2015 John Wiley & Sons, Ltd.