Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort.
This study developed an office-based cardiovascular risk calculator using a machine learning (ML) algorithm that utilized a focused carotid ultrasound. The design of this study was divided into three steps. The first step involved collecting 18 office-based biomarkers consisting of six clinical risk factors (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, and smoking) and 12 carotid ultrasound image-based phenotypes. The second step consisted of the design of an ML-based cardiovascular risk calculator-called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0ML) for risk stratification, considering chronic kidney disease (CKD) as the surrogate endpoint of cardiovascular disease. The last step consisted of comparing AECRS2.0ML against the currently utilized office-based CVD calculators, namely the Framingham risk score (FRS) and the World Health Organization (WHO) risk scores. A cohort of 379 Asian-Indian patients with type-2 diabetes mellitus, hypertension, and chronic kidney disease (stage 1 to 5) were recruited for this cross-sectional study. From this retrospective cohort, 758 ultrasound scan images were acquired from the far walls of the left and right common carotid arteries [mean age = 55 ± 10.8 years, 67.28% males, 91.82% diabetic, 86.54% hypertensive, and 83.11% with CKD]. The mean office-based cardiovascular risk estimates using FRS and WHO calculators were 26% and 19%, respectively. AECRS2.0ML demonstrated a better risk stratification ability having a higher area-under-the-curve against FRS and WHO by ~30% (0.871 vs. 0.669) and ~ 20% (0.871 vs. 0.727), respectively. The office-based machine-learning cardiovascular risk-stratification tool (AECRS2.0ML) shows superior performance compared to currently available conventional cardiovascular risk calculators.
['Jamthikar|Ankush D|AD|', 'Gupta|Deep|D|', 'Johri|Amer M|AM|', 'Mantella|Laura E|LE|', 'Saba|Luca|L|', 'Kolluri|Raghu|R|', 'Sharma|Aditya M|AM|', 'Viswanathan|Vijay|V|', 'Nicolaides|Andrew|A|', 'Suri|Jasjit S|JS|http://orcid.org/0000-0002-4600-8513']
D002318:Cardiovascular Diseases D003430:Cross-Sectional Studies D005260:Female D000082742:Heart Disease Risk Factors D006801:Humans D007231:Infant, Newborn D000069550:Machine Learning D008297:Male D012189:Retrospective Studies D018570:Risk Assessment D012307:Risk Factors
Office-based Machine learning Cardiovascular disease Non-laboratory biomarkers Risk assessment Stroke and artificial intelligence, “AtheroEdge composite risk score 2.0”