CARDIOMYOPATHY PREDICTION IN PATIENTS WITH PERMANENT VENTRICULAR PACING USING MACHINE LEARNING METHODS - Abstract and Methods

Aug 3, 2026

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Pacing-induced cardiomyopathy poses a clinically significant risk for patients requiring permanent ventricular pacing, making early identification of at-risk groups essential to prevent harm. The study develops predictive models from medical data using machine learning, comparing three approaches: decision tree, group method of data handling, and logistic regression. The resulting models achieve high accuracy for forecasting development of pacing-induced cardiomyopathy, supporting their suitability for clinical prediction. Age, paced QRS width, pacing mode, and ventricular index at implantation are highlighted as influential factors, demonstrating how machine learning can guide preventive strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Problem background and incidence

Clinical outcomes and research gap

Study objectives

Materials and Methods

Data source and ethical assessment

Patient cohort and outcome definition

UDC 004.852 + 616.12-07 DOI: 10.20535/SRIT.2308-8893.2024.1.03 CARDIOMYOPATHY PREDICTION IN PATIENTS WITH PERMANENT VENTRICULAR PACING USING MACHINE LEARNING METHODS E.O. PEREPEKA, V.V. LAZORYSHYNETS, V.O. BABENKO, I.V. DAVYDOVYCH, I.A. NASTENKO Abstract. Pacing-induced cardiomyopathy is a notable issue in patients needing permanent ventricular pacing. Identifying risk groups early and swiftly preventing the ailment can reduce patient harm. However, current prognostic methods require clarity. We employed machine learning to develop predictive models using medical data. Three algorithms—decision tree, group method of data handling, and logistic regression—formed models that forecast pacing-induced cardiomyopathy. These models displayed high accuracy in predicting development, signifying soundness. Factors like age, paced QRS width, pacing mode, and ventricular index during implantation significantly influenced predictions. Machine learning can enhance pacing-induced cardiomyopathy prediction in ventricular pacing patients, aiding medical practice and preventive strategies. Keywords: permanent ventricular pacing, risk factors, artificial intelligence, forecasting, machine learning. INTRODUCTION Right ventricular myocardial pacing remains dominating method in providing medical care to patients with various potentially fatal bradyarrhythmias, eventhough at the beginning of the 21st century, a relation between this form of cardiac pacing and the left ventricular contractility impairment [1], as well as deterioration of clinical outcomes in the distant period [2; 3] . According to data from various sources, the incidence of pacing-induced cardiomyopathy (PICM) in patients with conventional right ventricular pacing and with preserved initial left ventricle ejection fraction (LVEF) ranges from 7.5 to 26%[4–10] . The risk of heart failure hospitalizations (HFH) and overall mortality are significantly higher among patients with PICM, as was shown in a large retrospective study by Sung Woo Cho et al. [10] . Though in patients with initially reduced systolic function of the left ventricle and high burden of ventricular pacing, the factors of deterioration of the clinical outcomes are well established [3], inpatients with preserved LVEF, they have not yet been fully studied. Along with the wide availability and significant global experience of using this method of cardiac pacing in clinical practice, there is a growing number of publications focusing on the adverse effects of right ventricular myocardial pacing (and investigating risk factors that led to them), one of which is the development of the socalled pacing-induced cardiomyopathy, which is characterized by a decrease in the left ventricle contractility and negative remodeling of the heart chambers, 􀂤 E.O. Perepeka, V. V. Lazoryshynets, V.O. Babenko, I. V. Davydovych, I.A. Nastenko, 2024 Системні дослідження та інформаційні технології, 2024, № 1 33 The identification of risk factors and prediction of PICM development in patients with an implanted pacemaker is an objective of significant importance for modern medicine, considering the appearance of modern physiological methods of cardiac pacing (such as conduction system pacing) which allow preventing or minimizing the negative consequences of right ventricular myocardial pacing [11–14] . It is important to note that machine learning and artificial intelligence are becoming more prevalent in healthcare, particularly in cardiology. These technologies have successfully predicted disease cases and identified pathologies [15] . However, studies that apply machine learning to indicate PICM were not found after analyzing various literature sources. The primary focus of research in the intersection of cardiology and machine learning is centered around the prediction and diagnosis of diseases, including ischemic heart disease (IHD) [16; 17], HF [18], atrial arrhythmias [19; 20], and others, using data from patie

FAQs

What problem does the study address in permanent ventricular pacing patients?

The study focuses on pacing-induced cardiomyopathy and the need to identify patients at risk early to prevent adverse outcomes.

Which machine learning algorithms were used to build prediction models?

The study employed a decision tree, group method of data handling, and logistic regression to forecast pacing-induced cardiomyopathy development.

Which factors significantly influenced model predictions?

Age, paced QRS width, pacing mode, and ventricular index during implantation were reported as key factors affecting predictions.

Aug 3, 2026

Upload by Ezra