# Quantification of Cardiovascular Disease Risk Among Hypertensive Subjects in Active Romanian Population Using New Echocardiographic, Biological and Atherogenic Markers

## TL;DR

This study introduces PulsIn, a new machine learning-based risk score that improves cardiovascular disease prediction in hypertensive patients by combining clinical, echocardiographic, and biomarker data.

## Contribution

PulsIn integrates multimodal data and machine learning to enhance cardiovascular risk prediction beyond traditional scores.

## Key findings

- PulsIn-based models achieved higher predictive accuracy (AUC 0.85–0.88) compared to traditional scores (AUC 0.60–0.78).
- Echocardiographic indices, microalbuminuria, homocysteine, and paraoxonase-1 activity were key predictors in the model.
- PulsIn improved risk reclassification, especially for patients at intermediate risk by traditional tools.

## Abstract

Background and Objectives: The objective of this study is to assess the efficacy of a novel software risk score, PulsIn, in predicting cardiovascular diseases within an independent study conducted on subjects from the western region of Romania. Accurate prediction of cardiovascular events in hypertensive patients remains challenging when relying solely on traditional risk scores. This study proposes PulsIn, a composite risk score that integrates classical, echocardiographic, inflammatory, renal, and metabolic markers, combined with machine learning, to refine cardiovascular risk stratification. Materials and Methods: In a prospective cohort of 300 hypertensive adults without prior major cardiovascular events, we collected demographic and clinical data, standard risk factors, laboratory biomarkers (including homocysteine, paraoxonase-1 activity, microalbuminuria, and lipid profile), and advanced echocardiographic parameters (3D left ventricular ejection fraction, diastolic function, global longitudinal strain, and left atrial strain). PulsIn was constructed as an extended composite score and used as input to machine learning models (random forest, XGBoost, and other tree-based algorithms) to predict incident major cardiovascular events. Model performance was assessed by receiver operating characteristic curves, discrimination, calibration, and feature importance and compared with established risk scores (SCORE2, Framingham, QRISK, and others). Results: PulsIn-based models showed improved predictive performance compared with traditional scores, with XGBoost and random forest achieving area under the curve values up to approximately 0.85–0.88, versus 0.60–0.78 for conventional scores. Echocardiographic indices of subclinical cardiac damage, microalbuminuria, homocysteine, and paraoxonase-1 activity emerged as key predictors, particularly enhancing reclassification in patients at intermediate risk by traditional tools. Conclusions: The PulsIn composite risk score, integrating multimodal clinical, echocardiographic, and biomarker data within a machine learning framework, offers more accurate cardiovascular risk prediction than conventional algorithms in hypertensive patients. External validation in larger, independent, and more diverse populations is required before routine clinical implementation.