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How predictive is the Framingham's risk prediction algorithm in Indian perspective? A retrospective case-control
Santanu Guha1, Subrata Kr Pal, N Chatterjee
1Department of Cardiology, NRS Medical College and Hospital, Kolkata, India. guhas55@hotmail.com
Indian Heart Journal
|February 27, 2009
Summary
The Framingham risk algorithm shows some predictive ability in India, but it underestimates risk in many non-diabetic patients. A refined cardiovascular risk prediction tool is needed for the Indian population.
Area of Science:
- Cardiology
- Public Health
- Epidemiology
Background:
- Cardiovascular disease (CVD) is a major health concern in India.
- Accurate risk prediction is crucial for primary prevention of CVD.
- Existing risk algorithms may not perform optimally in diverse populations.
Purpose of the Study:
- To evaluate the predictive efficacy of the Framingham risk prediction algorithm in an Indian cohort.
- To assess the algorithm's performance in diabetic and non-diabetic individuals with and without acute coronary syndrome (ACS).
Main Methods:
- A retrospective case-control study involving 350 patients with first-time ACS and 293 age- and sex-matched controls.
- Application of the Framingham risk score to estimate 10-year CVD risk.
- Classification of individuals into high, moderately high, and low-risk categories based on predicted risk.
- Statistical comparison of risk scores between patient and control groups, stratified by diabetes status.
Main Results:
- In non-diabetic subjects, patients with ACS had a significantly higher mean risk score compared to controls (14.13% vs. 8.61%).
- The Framingham algorithm identified a substantial proportion of non-diabetic patients with ACS as low or moderately high risk, potentially underestimating their true risk.
- In diabetic subjects, no significant difference in mean projected risk was observed between those with ACS and those without (11.37% vs. 10.41%).
Conclusions:
- The Framingham risk prediction algorithm demonstrates moderate predictive efficacy in the Indian context, as evidenced by statistically significant differences in mean risk scores between patients and controls.
- The algorithm appears to under-predict cardiovascular risk in a significant number of high-risk, non-diabetic patients.
- Development of a tailored risk prediction protocol for the Indian population is warranted to improve cardiovascular risk assessment and management.
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