Relative Estimate of Revised Cardiovascular Risk Combining Traditional and Non-traditional Image-based CV Markers: A Kerala Based Study.

Abstract

Non-traditional image markers can improve the traditional cardiovascular risk estimation, is untested in Kerala based participants. To identify the relationship between the 'Modified CV risk' categories with traditional and non-traditional image-based risk markers. The correlation and improvement in reclassification, achieved by pooling atherosclerotic non-traditional markers with Intermediate (≥7.5% and <20%) and High (≥20%) 10-year participants is evaluated. The cross-sectional study with 594 participants has the ultrasound measurements recorded from the medical archives of clinical locations at Ernakulum district, Kerala. With carotid Intima-Media Thickness (cIMT) measurement, the Plaque (cP) complexity was computed using selected plaque characteristics to compute the carotid Total Plaque Risk Score (cTPRS) for superior risk tagging. Statistical analysis was done using RStudio, the classification accuracy was verified using the decision tree algorithm. The mean age of the participants was (58.14±10.05) years. The mean cIMT was (0.956±0.302) mm, with 65.6% plaque incidence. With 94.90% variability around its mean, the Multinomial Logistic Regression model identifies cIMT and cTPRS, age, diabetics, Familial Hypercholesterolemia (FH), Hypertension treatment, the presence of Rheumatoid Arthritis (RA), Chronic Kidney Disease (CKD) as significant (p<0.05). cIMT and cP were found significant for 'Intermediate High', 'High' and 'Very High' 'Modified CV risk' categories. However, age, diabetes, gender and use of hypertension treatment are significant for the 'Intermediate' 'Modified CV risk' category. The overall performance of the MLR model was 80.5%. The classification accuracy verified using the decision tree algorithm has 78.7% accuracy. The use of atherosclerotic markers shows a significant correlation suitable for a nextlevel reclassification of the traditional CV risk.