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Prognostic Modelling Studies of Coronary Heart Disease-A Systematic Review of Conventional and Genetic Risk Factor
Nayla Nasr1,2, Beáta Soltész3, János Sándor2
1Doctoral School of Health Sciences, University of Debrecen, 4032 Debrecen, Hungary.
Insights
This review of coronary heart disease (CHD) models found many studies but limited clinical evidence. More validation is needed for prognostic models predicting CHD risk using conventional and genetic factors.
Area of Science:
- Cardiovascular Disease Epidemiology
- Biostatistics and Health Informatics
- Genomic Medicine
Background:
- Multivariable prognostic models are crucial for predicting coronary heart disease (CHD) risk in the general population.
- Existing models often use conventional risk factors, with emerging interest in incorporating genomic data.
- A comprehensive overview and performance comparison of these models are lacking.
Purpose of the Study:
- To systematically review and compare multivariable prognostic models for CHD in the general population.
- To assess the performance of existing models based on discrimination, reclassification, and calibration.
- To identify gaps and provide recommendations for future prognostic modeling research in CHD.
Main Methods:
- Systematic literature review conducted across Embase, PubMed, Cochrane, Web of Science, and Scopus databases up to November 2019.
- Inclusion criteria focused on prognostic studies of multivariable models for CHD using conventional and/or genomic risk factors.
- Eligible studies (72 out of 4021 screened) were evaluated using CHARM and GRIPS statements for consistency and reliability.
Main Results:
- Most identified models were developed in North America and targeted the general population.
- Common predictors included age, sex, smoking, cholesterol, blood pressure, BMI, and diabetes.
- Significant heterogeneity in CHD event definitions was noted, and the clinical utility of most models remains undemonstrated.
Conclusions:
- While incorporating genetic risk scores shows potential for improved CHD incidence prediction, the generalizability of current models is uncertain.
- There is a critical need for robust validation studies to enhance the quality, transparency, and clinical applicability of CHD prognostic models.
- Further research should address heterogeneity in outcome definitions and rigorously validate models before widespread clinical adoption.
Abstract:
This study aims to provide an overview of multivariable prognostic modelling studies developed for coronary heart disease (CHD) in the general population and to explore the optimal prognostic model by comparing the models' performance. A systematic review was performed using Embase, PubMed, Cochrane, Web of Science, and Scopus databases until 30 November 2019. In this work, only prognostic studies describing conventional risk factors alone or a combination of conventional and genomic risk factors, being developmental and/or validation prognostic studies of a multivariable model, were included. A total of 4021 records were screened by titles and abstracts, and 72 articles were eligible. All the relevant studies were checked by comparing the discrimination, reclassification, and calibration measures. Most of the models were developed in the United States and Canada and targeted the general population. The models included a set of similar predictors, such as age, sex, smoking, cholesterol level, blood pressure, BMI, and diabetes mellitus. In this study, many articles were identified and screened for consistency and reliability using CHARM and GRIPS statements. However, the usefulness of most prognostic models was not demonstrated; only a limited number of these models supported clinical evidence. Unfortunately, substantial heterogeneity was recognized in the definition and outcome of CHD events. The inclusion of genetic risk scores in addition to conventional risk factors might help in predicting the incidence of CHDs; however, the generalizability of the existing prognostic models remains open. Validation studies for the existing developmental models are needed to ensure generalizability, improve the research quality, and increase the transparency of the study.
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