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

Isaac Subirana; Anna Camps-Vilaró; Roberto Elosua; Jaume Marrugat; Helena Tizón-Marcos; Ivan Palomo; Irene R Dégano

doi:10.2147/CLEP.S374581

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

Clin Epidemiol. 2022 Oct 11:14:1145-1154. doi: 10.2147/CLEP.S374581. eCollection 2022.

Authors

Isaac Subirana^{1

2}, Anna Camps-Vilaró^{1

2}, Roberto Elosua^{2

3

4}, Jaume Marrugat^{1

2}, Helena Tizón-Marcos^{2

5

6}, Ivan Palomo⁷, Irene R Dégano^{1

2

3}

Affiliations

¹ REGICOR Study Group, Department of Epidemiology and Public Health, Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain.
² Consorcio de Investigación Biomédica en Red, Cardiovascular Diseases, Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
³ Department of Medicine, University of Vic-Central University of Catalonia (Uvic-UCC), Vic, Spain.
⁴ Cardiovascular Epidemiology and Genetics Group, Department of Epidemiology and Public Health, IMIM, Barcelona, Spain.
⁵ Cardiology Department, Hospital del Mar, Barcelona, Spain.
⁶ Biomedical Research in Heart Diseases Group, Department of Translational Clinical Research, IMIM, Barcelona, Spain.
⁷ Department of Clinical Biochemistry and Immunohematology, Thrombosis Research Center, Faculty of Health Sciences, Medical Technology School, Talca, Chile.

Abstract

Background and aims: Cardiovascular (CV) risk functions are the recommended tool to identify high-risk individuals. However, their discrimination ability is not optimal. While the effect of biomarkers in CV risk prediction has been extensively studied, there are no data on CV risk functions including time-dependent covariates together with other variables. Our aim was to examine the effect of including time-dependent covariates, competing risks, and treatments in coronary risk prediction.

Methods: Participants from the REGICOR population cohorts (North-Eastern Spain) aged 35-74 years without previous history of cardiovascular disease were included (n = 8470). Coronary and stroke events and mortality due to other CV causes or to cancer were recorded during follow-up (median = 12.6 years). A multi-state Markov model was constructed to include competing risks and time-dependent classical risk factors and treatments (2 measurements). This model was compared to Cox models with basal measurement of classical risk factors, treatments, or competing risks. Models were cross-validated and compared for discrimination (area under ROC curve), calibration (Hosmer-Lemeshow test), and reclassification (categorical net reclassification index).

Results: Cancer mortality was the highest cumulative-incidence event. Adding cholesterol and hypertension treatment to classical risk factors improved discrimination of coronary events by 2% and reclassification by 7-9%. The inclusion of competing risks and/or 2 measurements of risk factors provided similar coronary event prediction, compared to a single measurement of risk factors.

Conclusion: Coronary risk prediction improves when cholesterol and hypertension treatment are included in risk functions. Coronary risk prediction does not improve with 2 measurements of covariates or inclusion of competing risks.

Keywords: coronary disease; longitudinal studies; risk assessment; risk factors.

Grants and funding

This study was funded by the Carlos III Health Institute and the European Regional Development Fund (FIS PI12/00232, FIS PI15/00051, FIS PI18/00030, PI21/00163 and CB16/11/00229, CB16/11/00246 from CIBERCV), by the Government of Catalonia through the Agency for Management of University and Research Grants (PERIS SLT002/16/00088, 2017SGR222), and by the Spanish Society of Cardiology (Proyectos de la SEC para Investigación Clínica 2019). The sponsors were not involved in any of the stages from study design to submission of the paper.