PREDICTION OF LEFT VENTRICULAR REMODELING IN PATIENTS WITHOUT CARDIOVASCULAR DISEASE THROUGH MACHINE LEARNING: AN ECG-BASED APPROACH. (April 2021)

• Record Type: Journal Article
• Authors: Angelaki, Eleni; Marketou, Maria; Barbaris, George; Patrianakos, Alexandros; Parthenakis, Fragiskos; Tsironis, George
• Is Part Of: Journal of hypertension. Volume 39(2021)e-Supplement 1
• Publication Date: 2021-04
• Subjects: Hypertension -- Periodicals; Hypertension -- Periodicals; 616.132005
• DOI: 10.1097/01.hjh.0000745456.62202.65
• Languages: English
• ISSNs: 1473-5598

Abstract

Abstract : Objective: Cardiac remodeling -even at early stages-is considered an important aspect of cardiovascular disease progression and is therefore emerging as a significant therapeutic target. We used machine learning (ML) techniques and investigated several ML classifiers built on basic clinical parameters and ECG features for detecting left ventricular remodeling (LVR), even at the early stages before the onset of LVH in a population without cardiovascular disease Design and method: We enrolled 475 subjects with and without essential hypertension and no other indications of cardiovascular disease. Based on left ventricular mass index (LVMi) and relative wall thickness (RWT), calculated using echocardiography, the subjects were classified into 3 groups; those with normal geometry, concentric remodelling, concentric and eccentric left ventricular hypertrophy (LVH). We trained a Random Forest, i.e. a nonlinear predictive ML model suitable for panel data, to classify subjects either in the normal group, or in the other three combined (LVH and both types of left ventricular hypertrophy-LVH). We also perform feature importance and feature interaction analysis by calculating SHAP values, a game theoretic tool that helps in interpreting the model's predictions. Results: Hypertension, age, and body mass index (BMI) were the most significant features, as expected. Additionally, a combination of BMI and Sokolow criteria as well as the areas under the QRS complex and the QTc duration, among others, were also important. The age of 65 marked a sharp change in the risk of being classified with LV remodelling and LVH. The random forest model was able to distinguish normal subjects from the ones with LVR and LVH with accuracy 80%, specificity 65% and sensitivity 91%. Conclusions: We found promising clinical and ECG features in the direction of machine based assistance in the detection of LVR even at early stages before the onset of LVH. Strategic and innovative solutions are needed to improve hypertension management, especially in primary care settings and our data indicate that ML may improve risk stratification of hypertensive population with simple clinical approaches.