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Cardiovascular Risk Prediction using JBS3 Tool: A Kerala based Study
Paulin Paul1, Noel George2, B Priestly Shan3
1Sathyabama Institute of Science and Technology, Chennai, India.
Insights
This study assessed cardiovascular risk in Kerala, India, finding a significant portion of the population faces high 10-year and lifetime risks. Early interventions are crucial for preventing cardiovascular disease mortality.
Area of Science:
- Cardiology and Public Health
- Epidemiological Studies of Cardiovascular Disease
Background:
- The Joint British Society calculator 3 (JBS3) cardiovascular risk assessment tool's accuracy may differ across Indian states.
- This accuracy has not been previously verified in the South Indian population of Kerala.
Purpose of the Study:
- To evaluate cardiovascular risk estimation using traditional risk factors in the Kerala population.
- To assess the prevalence of 10-year and lifetime cardiovascular risk categories within this demographic.
Main Methods:
- A cross-sectional study of 977 individuals aged 30-80 years from Ernakulum district, Kerala.
- Utilized standard risk categories for 10-year (Low <7.5%, Intermediate ≥7.5% to <20%, High ≥20%) and lifetime (Low ≤39%, High ≥40%) risk.
- Employed Chi-square tests and multivariate logistic regression for statistical analysis.
Main Results:
- The study population (mean age 52.56±11.43 years) showed 35.9% with high 10-year risk and 58.9% with high lifetime risk.
- A notable 25.0% were in the intermediate 10-year risk category, with many reclassifying to high lifetime risk.
- Statistical analysis indicated a good model fit (Hosmer-Lemeshow test).
Conclusions:
- Risk stratification using traditional factors is essential for cardiovascular disease prevention in Kerala.
- Identifying intermediate-risk individuals allows for targeted interventions to prevent progression to high lifetime risk.
- Proactive therapeutic and lifestyle modifications based on risk prediction can significantly reduce cardiovascular disease mortality.
Background:
Accuracy of Joint British Society calculator3 (JBS3) cardiovascular (CV) risk assessment tool may vary across the Indian states, which is not verified in south Indian, Kerala based population.
Objectives:
To evaluate the traditional risk factors (TRFs) based CV risk estimation done in Kerala based population.
Methods:
This cross-sectional study uses details of 977 subjects aged between 30 and 80 years, recorded from the medical archives of clinical locations at Ernakulum district, in Kerala. The risk categories used are Low (<7.5%), Intermediate (≥7.5% and <20%), and High (≥20%) 10-year risk classifications. The lifetime classifications are Low lifetime (≤39%) and High lifetime (≥40%) are used. The study evaluated using statistical analysis; the Chi-square test was used for dependent and categorical CV risk variable comparisons. A multivariate ordinal logistic regression analysis for the 10-year risk and odds logistic regression analysis for the lifetime risk model identified the significant risk variables.
Results:
The mean age of the study population is 52.56±11.43 years. With 39.1% in low, 25.0% in intermediate, and 35.9% has high 10-year risk. Low lifetime risk with 41.1%, the high lifetime risk has 58.9% subjects. The intermediate 10-year risk category shows the highest reclassifications to High lifetime risk. The Hosmer-Lemeshow goodness-of-fit statistics indicates a good model fit.
Conclusion:
Timely interventions using risk predictions can aid in appropriate therapeutic and lifestyle modifications useful for primary prevention. Precaution to avoid short-term incidences and reclassifications to a high lifetime risk can reduce the CVD related mortality rates.
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