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Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models
Ankush Jamthikar1, Deep Gupta1, Luca Saba2
1Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India.
Insights
Machine learning (ML)-based cardiovascular risk calculators (CVRC) integrating plaque burden show superior 10-year CVD/stroke prediction compared to traditional statistical models. This ML approach offers a 42% performance increase, enhancing risk stratification accuracy.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Conventional cardiovascular risk calculators (CVRC) often misestimate cardiovascular disease (CVD) and stroke risk due to limited integration of plaque burden.
- This study addresses the need for improved risk prediction by investigating machine learning (ML)-based CVRC.
Purpose of the Study:
- To compare the predictive performance of ML-based CVRC (CVRCML) against statistically derived CVRC (CVRCStat).
- To evaluate CVRCML and CVRCStat using conventional factors and integrated factors including plaque burden.
Main Methods:
- Developed and evaluated 13 types of CVRCStat using conventional and integrated risk factors for 10-year CVD/stroke risk stratification.
- Developed CVRCML using support vector machine (SVM) with the same risk factors.
- Assessed calculator performance using Area Under the Curve (AUC) from Receiver Operating Characteristic (ROC) analysis.
Main Results:
- CVRCML with integrated risk factors achieved an AUC of 0.88 (P<0.001), representing a 42% performance improvement.
- The overall risk-stratification accuracy for CVRCML with integrated factors was 92.52%.
- All 13 CVRCStat models showed lower AUC values compared to the integrated CVRCML.
Conclusions:
- ML-based CVD/stroke risk calculators demonstrate superior predictive ability for 10-year CVD/stroke events.
- The integrated ML model outperformed traditional statistical models, including the AECRS 2.0 integrated model.
Background:
Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRCML) and compares against statistically derived CVRC (CVRCStat) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors).
Methods:
The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRCStat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRCStat, the patients were again risk-stratified using CVRCML based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events.
Results:
An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRCStat calculators were: AECRS2.0 (AUC 0.83, P<0.001), QRISK3 (AUC 0.72, P<0.001), WHO (AUC 0.70, P<0.001), ASCVD (AUC 0.67, P<0.001), FRScardio (AUC 0.67, P<0.01), FRSstroke (AUC 0.64, P<0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P<0.001), NIPPON (AUC 0.63, P<0.001), PROCAM (AUC 0.59, P<0.001), RRS (AUC 0.57, P<0.001), UKPDS60 (AUC 0.53, P<0.001), and SCORE (AUC 0.45, P<0.001), while the AUC for the CVRCML with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRCML with integrated risk factors was 92.52% which was higher compared all the other CVRCStat.
Conclusions:
ML-based CVD/stroke risk calculator provided a higher predictive ability of 10-year CVD/stroke compared to the 13 different types of statistically derived risk calculators including integrated model AECRS 2.0.
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