Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models

Ankush Jamthikar; Deep Gupta; Luca Saba; Narendra N Khanna; Tadashi Araki; Klaudija Viskovic; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; Vijay Viswanathan; Aditya Sharma; Andrew Nicolaides; George D Kitas; Jasjit S Suri

doi:10.21037/cdt.2020.01.07

Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models

Cardiovasc Diagn Ther. 2020 Aug;10(4):919-938. doi: 10.21037/cdt.2020.01.07.

Authors

Ankush Jamthikar¹, Deep Gupta¹, Luca Saba², Narendra N Khanna³, Tadashi Araki⁴, Klaudija Viskovic⁵, Sophie Mavrogeni⁶, John R Laird⁷, Gyan Pareek⁸, Martin Miner⁹, Petros P Sfikakis¹⁰, Athanasios Protogerou¹¹, Vijay Viswanathan¹², Aditya Sharma¹³, Andrew Nicolaides¹⁴, George D Kitas¹⁵, Jasjit S Suri¹⁶

Affiliations

¹ Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India.
² Department of Radiology, University of Cagliari, Cagliari, Italy.
³ Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India.
⁴ Division of Cardiovascular Medicine, Toho University, Tokyo, Japan.
⁵ Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, Zagreb, Croatia.
⁶ Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece.
⁷ Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA.
⁸ Minimally Invasive Urology Institute, Brown University, Providence, Rhode Island, USA.
⁹ Men's Health Center, Miriam Hospital Providence, Rhode Island, USA.
¹⁰ Rheumatology Unit, National and Kapodistrian University of Athens, Athens, Greece.
¹¹ Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology, National and Kapodistrian University of Athens, Athens, Greece.
¹² MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India.
¹³ Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA.
¹⁴ Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia, Cyprus.
¹⁵ R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK.
¹⁶ Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.

Abstract

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 (CVRC_ML) and compares against statistically derived CVRC (CVRC_Stat) 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 CVRC_Stat (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 CVRC_Stat, the patients were again risk-stratified using CVRC_ML 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 CVRC_Stat 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), FRS_cardio (AUC 0.67, P<0.01), FRS_stroke (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 CVRC_ML with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRC_ML with integrated risk factors was 92.52% which was higher compared all the other CVRC_Stat.

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.

Keywords: 10-year risk; Atherosclerosis; cardiovascular disease (CVD); integrated models; machine learning-based calculator; statistical risk calculator; stroke.