Estimation of Cardiovascular Risk by Using WHO/ISH charts and Correlation & Regression Tree (CART) Analysis in Adults of Rural North Maharashtra, India

Amit P Gujarathi; Jagdish Powar; Rakesh Patil; Shekhar Padhyegurjar; Ashwini Khadatkar Ghodake; Moshaheed Hussian Mushir Ahmed Shaikh

doi:10.55489/njcm.161120255801

Authors

Amit P Gujarathi Department of Community Medicine, SMBT Institute of Medical Sciences and Research Centre, Nashik, India
Jagdish Powar Department of Community Medicine, SMBT Institute of Medical Sciences and Research Centre, Nashik, India
Rakesh Patil Department of Community Medicine, SMBT Institute of Medical Sciences and Research Centre, Nashik, India
Shekhar Padhyegurjar Department of Community Medicine, MGM Medical College, Panvel, Navi Mumbai, India
Ashwini Khadatkar Ghodake Department of Pathology, SMBT Institute of Medical Sciences and Research Centre, Nashik, India
Moshaheed Hussian Mushir Ahmed Shaikh SMBT Institute of Medical Sciences and Research Centre, Nashik, India

DOI:

https://doi.org/10.55489/njcm.161120255801

Keywords:

CVD, Risk Prediction, WHO/ISH chart, CART analysis, Rural India

Abstract

Background: Cardiovascular diseases (CVDs) are a growing cause of morbidity and mortality, especially in rural India. Accurate risk assessment is crucial for effective primary prevention. This study estimated 10-year CVD risk in rural North Maharashtra using Cholesterol & Non cholesterol based WHO/ISH charts, identified data gaps, assessed agreement between charts, and employed Classification and Regression Tree (CART) analysis.

Methods: The Cross-sectional study conducted among (n=110) rural adults (≥40 years). Data included demographics, smoking, diabetes, Systolic blood pressure (SBP), Fasting blood sugar level (FBS), and Total cholesterol (TC). CVD risk was determined using WHO/ISH charts (with/without cholesterol) for SEAR D countries. CART analysis used to predict the binary risk category (Mild Vs Moderate to severe) derived from Cholesterol based WHO/ISH chart, and Kappa statistics assessed chart agreement.

Results: Participants' mean age was 54.9 years. High prevalence included SBP >130 mmHg (62.7%), abnormal TC (33.6%), and diabetes (25.5%). The WHO/ISH chart classified 37.3% as moderate-to-severe risk. CART analysis identified age, FBS, and TC as key predictors. Moderate agreement (kappa = 0.499) was found between the WHO/ISH charts. Age, diabetes, and TC linked significantly to severe risk.

Conclusion: Rural North Maharashtra carries a significant CVD risk factor burden. Age, fasting blood sugar levels, and total cholesterol were key predictor of moderate to severe risk category. Including cholesterol in risk assessment is vital, as its omission may underestimate risk.

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