Background Clinical prediction models are often constructed using multicenter databases.
Such a data structure poses additional challenges for statistical analysis (clustered data) but …

Objective To evaluate the evidence on comparisons of established cardiovascular risk
prediction models and to collect comparative information on their relative prognostic …

Clinical risk prediction models are increasingly being developed and validated on
multicenter datasets. In this article, we present a comprehensive framework for the …

Background While machine learning (ML) algorithms may predict cardiovascular outcomes
more accurately than statistical models, their result is usually not representable by a …

Background Cardiovascular prevention guidelines advocate the use of statistical risk
equations to predict individual cardiovascular risk. However, predictive accuracy and clinical …

Objective To provide an overview of prediction models for risk of cardiovascular disease
(CVD) in the general population. Design Systematic review. Data sources Medline and …

Background Stability of risk estimates from prediction models may be highly dependent on
the sample size of the dataset available for model derivation. In this paper, we evaluate the …

Objective The identification of methodology for modelling cardiovascular disease (CVD) risk
using longitudinal data and risk factor trajectories. Methods We screened MEDLINE-Ovid …

Background Multivariable risk prediction equations attempt to quantify an individual's
cardiovascular risk. Those borne from the Framingham Heart Study remain the most well …

Background The WHO model has laboratory-based and non-laboratory-based versions for
10-year risk prediction of cardiovascular diseases. Due to the fact that in some settings …