The use of repeated blood pressure measures for cardiovascular risk prediction: a comparison of statistical models in the ARIC study.
Michael J SweetingJessica K BarrettSimon G ThompsonAngela M WoodPublished in: Statistics in medicine (2016)
Many prediction models have been developed for the risk assessment and the prevention of cardiovascular disease in primary care. Recent efforts have focused on improving the accuracy of these prediction models by adding novel biomarkers to a common set of baseline risk predictors. Few have considered incorporating repeated measures of the common risk predictors. Through application to the Atherosclerosis Risk in Communities study and simulations, we compare models that use simple summary measures of the repeat information on systolic blood pressure, such as (i) baseline only; (ii) last observation carried forward; and (iii) cumulative mean, against more complex methods that model the repeat information using (iv) ordinary regression calibration; (v) risk-set regression calibration; and (vi) joint longitudinal and survival models. In comparison with the baseline-only model, we observed modest improvements in discrimination and calibration using the cumulative mean of systolic blood pressure, but little further improvement from any of the complex methods. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Keyphrases
- blood pressure
- cardiovascular disease
- primary care
- risk assessment
- hypertensive patients
- heart failure
- left ventricular
- type diabetes
- randomized controlled trial
- metabolic syndrome
- coronary artery disease
- molecular dynamics
- cross sectional
- atrial fibrillation
- low cost
- climate change
- cardiovascular events
- health information
- weight loss