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Cholesterol and Hypertension Treatment Improve Coronary Risk Prediction but Not Time-Dependent Covariates or

Isaac Subirana1,2, Anna Camps-Vilaró1,2, Roberto Elosua2,3,4

  • 1REGICOR Study Group, Department of Epidemiology and Public Health, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.

Clinical Epidemiology
|October 18, 2022
PubMed

Insights

Improving coronary risk prediction requires including cholesterol and hypertension treatments in risk functions. Incorporating competing risks or multiple covariate measurements does not significantly enhance prediction accuracy.

Area of Science:

  • Cardiovascular epidemiology
  • Biostatistics
  • Preventive cardiology

Background:

  • Cardiovascular (CV) risk functions are crucial for identifying high-risk individuals but have suboptimal discrimination.
  • Previous studies focused on biomarkers, with limited data on incorporating time-dependent covariates into CV risk prediction models.

Purpose of the Study:

  • To evaluate the impact of including time-dependent covariates, competing risks, and treatments on coronary risk prediction.
  • To compare multi-state Markov models with traditional Cox models for CV risk assessment.

Main Methods:

  • Utilized data from 8470 participants in the REGICOR cohorts (North-Eastern Spain) aged 35-74 without prior CV disease.
  • Employed a multi-state Markov model incorporating competing risks and time-dependent risk factors/treatments, compared against Cox models.
  • Assessed model performance using cross-validation, ROC curve analysis, Hosmer-Lemeshow tests, and net reclassification index.

Main Results:

  • Cancer mortality was the most frequent cumulative-incidence event.
  • Adding cholesterol and hypertension treatments improved coronary event discrimination by 2% and reclassification by 7-9%.
  • Inclusion of competing risks or two covariate measurements yielded similar coronary event prediction compared to single measurements.

Conclusions:

  • Coronary risk prediction is enhanced by incorporating cholesterol and hypertension treatments into risk functions.
  • The predictive performance for coronary events is not improved by using multiple covariate measurements or by including competing risks in the model.
Abstract

Keywords:
coronary diseaselongitudinal studiesrisk assessmentrisk factors

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