New Delhi: Heart disease risk calculators used across hospitals may not accurately identify Indians, who face imminent danger, states a new research involving
nearly 5,000 patients. The study found that close to 80 per cent of individuals who eventually suffered a first heart attack had not been labelled ‘high-risk’ by widely accepted global scoring systems. These findings raise serious concerns about preventive cardiology practices in India, where heart disease often strikes earlier and progresses differently compared to Western populations.
Researchers say physiological patterns, diabetes burden, fat distribution and genetic factors unique to South Asians may be distorting predictions generated by international tools. When risk is underestimated, treatment may be delayed, and opportunities for timely intervention lost. The findings strengthen calls for the development of India-specific cardiovascular risk assessment models that reflect local realities and improve early detection strategies in routine clinical practice across urban and semi-urban healthcare settings.
ASCVD risk prediction models in STEMI: Findings
Study scope and institutions involved
The research, titled “Comparison of ASCVD Risk Prediction Models in STEMI: Insights from a South Asian Cohort”, was conducted by scientists at Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, ESIC Medical College, Faridabad, the Delhi Cancer Registry at AIIMS and other centres.
Researchers analysed records of 4,975 patients aged 40 to 79 years who were admitted with their first acute myocardial infarction. Pre-event clinical data, including blood pressure, cholesterol, diabetes status and smoking history, were assessed using five established global risk calculators.
Five global models under scanner
The study compared Framingham Risk Score (FRS), ACC/AHA ASCVD 2013 model, WHO risk charts, JBS-3 calculator and Predicting Risk of Cardiovascular Disease Events (PREVENT) score.
Marked variation emerged in how these tools categorised patients. Some classified around 20 per cent as ‘high-risk’, while the ASCVD 2013 model identified only about 12.3 per cent in that category. The majority were placed in low or moderate brackets despite later suffering heart attacks.
Why Western tools fall short in Indians
According to the researchers, heart disease manifests differently in Indians, often at a younger age and with a distinct metabolic pattern. Higher diabetes prevalence, abdominal fat distribution and environmental stressors may alter risk trajectories.
Dr Mohit Gupta, Professor of Cardiology at GB Pant Hospital and a key investigator, said many Indian patients are incorrectly labelled when assessed through Western models. “When we put Indian heart attack patients through these Western models, many of them are wrongly classified. Physiologically, they should be considered ‘high-risk’ patients, especially since they went on to have a heart attack, but these models place them in low- and medium-risk categories, raising serious concerns about prevention,” he said.
Moderate-risk grey zone creates a clinical dilemma.
Another concern was the clustering of large numbers of patients in the moderate-risk band. This broad categorisation makes it difficult for clinicians to decide who requires aggressive medication, lifestyle intervention or closer follow-up.
The PREVENT score distributed patients more evenly across categories, yet it too failed to detect a significant number who later experienced acute myocardial infarction.
Call for an indigenous risk calculator.
Researchers emphasised that South Asian ethnicity itself carries elevated cardiovascular vulnerability. Indian patients behave differently from others. Many factors like genetics, pollution, lifestyle and stress levels have an impact. Even being Indian or South Asian in itself is a risk factor. We urgently need an indigenous risk calculator tailored to our population,” Dr Gupta added.
The study concludes that dependence on global models may lead to underestimation of cardiovascular danger in India, expressing the need for validated, region-specific prediction tools to strengthen early prevention strategies.
