Cardiovascular Risk Prediction using JBS3 Tool: A Kerala based Study

Abstract

Background: The accuracy of Joint British Society calculator3 (JBS3) cardiovascular risk prediction may vary within Indian population, and is not yet studied using south Indian Kerala based population data. Objectives: To evaluate the cardiovascular disease (CV) risk estimation using the traditional CVD risk factors (TRF) in Kerala based population. Methods: This cross sectional study has 977 subjects aged between 30 and 80 years. The traditional CVD risk markers are recorded from the medical archives of clinical locations at Ernakulum district, in Kerala The 10 year risk categories used are low (<7.5%), intermediate (≥7.5% and <20%), and high (≥20%). The lifetime classifications low lifetime (≤39%) and high lifetime (≥40%) are used. The study was evaluated using statistical analysis. Chi-square test was done for dependent and categorical CVD risk variable comparison. Multivariate ordinal logistic regression for 10-year risk model and odds logistic regression analysis for lifetime model was used to identify significant risk variables. Results: The mean age of the study population is 52.56±11.43 years. The risk predictions has 39.1% in low, 25.0% in intermediate, and 35.9% had high 10-year risk. The low lifetime risk had 41.1% and 58.9% is high lifetime risk. Reclassifications to high lifetime are higher from intermediate 10-year risk category. The Hosmer-Lemeshow goodness-of-fit statistics indicates a good model fit. Conclusion: The risk prediction and timely intervention with appropriate therapeutic and lifestyle modification is useful in primary prevention. Avoiding short-term incidences and reclassifications to high lifetime can reduce the CVD mortality rates.

Comparison of Framingham Risk Scores (FRS), Joint British Society (JBS3), and American College of Cardiology/American Heart Association (ACC/AHA) Cardiovascular Risk Scores Among Adults With First Myocardial Infarction

“…The results are similar to a study done by Garg et al [ 9 ]. However, in a study done by Paul et al [ 10 ] on 579 Kerala-based subjects aged between 40 and 60 years (mean age of 49.9±5.7) showed that JBS3 was a better model for cardiovascular risk assessment in Kerala-based Indian sub-ethnic population. Similar results were found in a study done by Bansal et al [ 3 ].…”

GWO based Atherosclerotic Cardiovascular Prediction model integrating Traditional and Non-Traditional parameters

“…In this proposed study, an optimized hybrid ASCV risk prediction and classification framework were studied to identify the 'present' and '10-years' ASCV risk status and risk class of a subject at an early stage. The input for the study has used JBS3 based Traditional Risk Factors (TRFs) [24] and ultrasound-based non-Traditional Risk Factors (non-TRFs) [25] that define the atherosclerotic risk status of a subject.…”

“…The ML-based framework implemented in this study was compared with similar studies in the literature, the established ASCV risk assessment tool, and the results from the statistical modelling as well. The ACC/AHA ASCVD risk prediction tool has demonstrated wide variations in performance when studied in different ethnicities [24,25,38]. Several studies have tried the combination of non-traditional US carotid markers of cIMT and cP burden status as a powerful link to indicate and predict atherosclerotic CV (ASCV) risk.…”