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

INTRODUCTION

Cardiovascular disease was 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 preventive measures. This has been traditionally done using the Singapore-adapted Framingham Risk Score (SG FRS). However, its most recent recalibration was 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 has led to new data types with the potential to improve risk prediction.

METHODS

In healthy Singaporeans enrolled in SingHEART study (absence of any clinical outcomes), we investigated improvements in SG FRS to predict myocardial infarction risk based on high/low classification of the Agatston score (surrogate outcome). Logistic regression, receiver operating characteristic and net reclassification index (NRI) analyses were conducted.

RESULTS

We demonstrated a significant improvement in the area under curve (AUC) of SG FRS (AUC = 0.641) after recalibration and incorporation of additional variables (fasting blood 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).

CONCLUSION

Existing Singapore cardiovascular disease risk prediction guidelines should be updated to improve risk prediction accuracy. Recalibrating existing risk functions and utilising wearable metrics that provide a large pool of objective health data can improve existing risk prediction tools. Lastly, activity levels and prediabetic state are important factors for coronary heart disease risk stratification, especially in low-risk individuals.