Related Experiment Video
Updated: Jun 4, 2025
Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
Published on: August 9, 2024
Simplifying coronary artery disease risk stratification: development and validation of a questionnaire-based
Michail Kokkorakis1, Pytrik Folkertsma2, Filippos Anagnostakis3
1Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Insights
A new questionnaire-based model, QUES-CAD, effectively identifies individuals at high risk for coronary artery disease (CAD) without needing physical measurements or biomarkers. This cost-effective tool is suitable for population-wide screening, especially in resource-limited settings.
Area of Science:
- Cardiovascular Medicine
- Public Health
- Biostatistics
Background:
- Coronary artery disease (CAD) is a leading cause of death globally.
- Current risk stratification models require costly physical measurements and biomarkers.
- These limitations hinder population-wide screening, particularly in resource-limited areas.
Purpose of the Study:
- To develop and validate a cost-effective, questionnaire-based risk stratification model for CAD.
- To enable population-scale screening for 10-year CAD incidence.
- To overcome limitations of existing clinical risk assessment tools.
Main Methods:
- Developed the Questionnaire-Based Evaluation for Estimating Coronary Artery Disease (QUES-CAD) model using ten questionnaire variables.
- Trained and validated models using Cox proportional hazards (CoxPH) and Cox gradient boosting (CoxGBT) on the UK Biobank.
- Externally validated QUES-CAD in the Lifelines cohort and compared its performance against established risk scores (e.g., Framingham, SCORE2).
Main Results:
- QUES-CAD demonstrated comparable performance to established clinical prediction models in external validation (C-indices for males: ~0.69-0.70, females: ~0.76-0.77).
- Adding physical measurements or biomarkers did not significantly improve QUES-CAD's predictive performance.
- Partial log-likelihood ratio tests and C-index comparisons confirmed QUES-CAD's efficacy.
Conclusions:
- QUES-CAD provides a reliable, low-cost method for identifying individuals at high risk of CAD.
- The model's reliance solely on questionnaire data overcomes logistical and cost barriers of traditional methods.
- QUES-CAD facilitates population-wide CAD risk assessment, especially in underserved regions.
Background:
Coronary artery disease (CAD) comprises one of the leading causes of morbidity and mortality both in the European population and globally. All established clinical risk stratification scores and models require blood lipids and physical measurements. The latest reports of the European Commission suggest that attracting health professionals to collect these data can be challenging, both from a logistic and cost perspective, which limits the usefulness of established models and makes them unsuitable for population-wide screening in resource-limited settings, i.e., rural areas. Therefore, the aim of this study was to develop and externally validate a questionnaire-based risk stratification model on a population scale at minimal cost, i.e., the Questionnaire-Based Evaluation for Estimating Coronary Artery Disease (QUES-CAD) to stratify the 10-year incidence of coronary artery disease.
Methods:
Cox proportional hazards (CoxPH) and Cox gradient boosting (CoxGBT) models were trained with 10-fold cross-validation using combinations of ten questionnaire variables on the White population of the UK Biobank (n = 448,818) and internally validated the models in all ethnic minorities (n = 27,433). The Lifelines cohort was employed as an independent external validation population (n = 97,770). Additionally, we compared QUES-CAD's performance, containing only questionnaire variables, to clinically established risk prediction tools, i.e., Framingham Coronary Heart Disease Risk Score, American College of Cardiology/American Heart Association pooled cohort equation, World Health Organization cardiovascular disease risk charts, and Systematic Coronary Risk Estimation 2 (SCORE2). We conducted partial log-likelihood ratio (PLR) tests and C-index comparisons between QUES-CAD and established clinical prediction models.
Findings:
In the external validation set, QUES-CAD exhibited C-index values of CoxPH: 0.692 (95% Confidence Interval [CI]: 0.673-0.71) and CoxGBT: 0.699 (95% CI: 0.681-0.717) for the male population and CoxPH: 0.771 (95% CI: 0.748-0.794) and CoxGBT: 0.759 (95% CI: 0.736-0.783) for the female population. The addition of measurement-based variables and variables that require a prior medical examination (i.e., insulin use, number of treatments/medications taken, prevalent cardiovascular disease [other than CAD, and stroke diagnosed by a doctor]) and the further addition of biomarkers/other measurements (i.e., high-density lipoprotein [HDL] cholesterol, total cholesterol, and glycated haemoglobin) did not significantly improve QUES-CAD's performance in most instances. C-index comparisons and PLR tests showed that QUES-CAD performs and fits the data at least as well as the clinical prediction models.
Interpretation:
QUES-CAD performs comparably to established clinical prediction models and enables a population-wide identification of high-risk individuals for CAD. The model developed and validated herein relies solely on ten questionnaire variables, overcoming the limitations of existing models that depend on physical measurements or biomarkers.
Funding:
University Medical Center Groningen.
Related Concept Videos
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:23Assessment of the Cardiovascular System I: Subjective Data
Initial Enquiry
Ask the patient about their primary concern and thoroughly explore all reported symptoms.
Medical History
Investigate past illnesses affecting the cardiovascular system, such as angina, anemia, rheumatic fever, congenital heart disease, stroke, thrombophlebitis, dysrhythmias, varicosities
Inquire about symptoms...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:23Assessment of blood pressure in brachial artery(two-step method)