Predictive ability of Framingham risk score in Indian population - A retrospective study in a tertiary care hospital in patients with first acute coronary syndrome

Abstract

Background: Framingham risk score (FRS) is a widely recognized tool used by clinicians worldwide to determine and estimate the 10-year risk of manifesting clinical cardiovascular disease (CVD) of an individual and classify them into different risk categories on the basis of which necessary intervention can be taken. However, there is certain evidence questioning its predictive ability for risk stratification among ethnic Indian and Asian population. Aims and Objectives: In this study, we wanted to determine the predictive ability of FRS in the Indian population and to assess how the risk score is associated with certain variables which are not included in original FRS. Materials and Methods: It was a single institutional, retrospective, and observational study in patients with evidence (by ECG or Cardiac biomarker test -Preferably Troponin T) of diagnosis of acute myocardial infarction for first time. Eligible patients were assessed for their previous clinical records to calculate risk score as per FSR-coronary heart disease 2002. Predictive ability of FRS and association with different risk factors was tested for statistical significance. Results: The most of the study participants (63.8%) had low risk and only 4.8% of study population had high risk for developing CVD. Patients in the low risk group had significantly better high-density lipoprotein profile than other risk groups (P=0.001). Mean systolic blood pressure was significantly higher in high-risk population (155 vs. 131 vs. 138 mm of Hg; P=0.005). The left ventricular ejection fraction was significantly lower in high-risk group (39.2% vs. 53% vs. 52%, P=0.008). Serum creatinine level was also significantly higher in high-risk group (P=0.03). Conclusion: This clearly showed underestimation of FRS in prediction of CVDs in our study. Perhaps FRS needs a calibration and modified form with inclusion of other risk factors and parameters to determine and predict the future risk of development of CVD more accurately in Indian population.