Improving the predictive capability of Framingham Risk Score for risk of myocardial infarction based on coronary artery calcium score in healthy Singaporeans.

Abstract

INTRODUCTION\nCardiovascular disease emerged as the top cause of deaths and disability in Singapore in 2018, contributing extensively to the local healthcare burden. Primary prevention identifies at-risk individuals for the swift implementation of prevention or corrective measures. This has been traditionally done using the Singapore-adapted Framingham Risk Score (SG FRS). However, its most recent recalibration was done more than a decade ago. Recent changes in patient demographics and risk factors have undermined the accuracy of SG FRS, and the rising popularity of wearable health metrics have given rise to new data types with the potential to improve risk prediction.\n\n\nMETHODS\nIn healthy Singaporeans enrolled in the SingHEART study (in the absence of any clinical outcomes), we investigated potential improvements in the SG FRS to predict myocardial infarction risk based on high/low classifications of the Agatston score (surrogate outcome). Logistic regression, receiver operating characteristic and net reclassification index (NRI) analyses were conducted.\n\n\nRESULTS\nWe demonstrated a significant improvement in the area under curve (AUC) of the SG FRS (AUC=0.641) after recalibration and incorporation of additional variables (fasting glucose and wearable-derived activity levels) (AUC=0.774) (p<0.001). SG FRS++ significantly increases accuracy in risk prediction (NRI=0.219, p=0.00254).\n\n\nCONCLUSION\nWe suggest that existing Singapore CVD risk prediction guidelines be updated to improve risk prediction accuracy. Recalibrating existing risk functions and utilising wearable metrics which provide a large pool of objective health data can help improve existing risk prediction tools. Lastly, activity levels and pre-diabetic state are important factors to consider for CHD risk stratification methods, especially in low-risk individuals.