ArticleJournal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing2026
Machine learning prediction of mechanical dilatation in transvenous lead extraction for cardiac device-related infections: insights from a high-volume centre.
Article in Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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- Erratum issued
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16 authors.
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Abstract
backgroundTransvenous lead extraction (TLE) requires high expertise in mechanical dilatation (MD), with double the risk of death and failure in low-volume centres. No automated tools support decision-making.
objectiveWe aimed to develop an ML-based model to predict the need for MD in patients undergoing TLE for infections.
methodsFive ML models (k-nearest neighbours, support vector machine, decision tree, random forest, and gradient boosting) were developed on a retrospective cohort of patients who underwent TLE at our centre. Each patient was described by 21 features (14 clinical, 7 device-related). Models were trained to distinguish MD from manual traction (MT). Five-fold nested cross-validation assessed performance and identified the best classifier. Feature importance analysis highlighted the most influential factors in model decisions.
resultsData for model training were extracted from our 25-year TLE database (June 1998-March 2023), including 491 patients (77.8% male; age 69.7 ± 12.8 years) and 938 leads (ICD 21.2%; pacing 78.8%; indwelling time 61 ± 60 months). MT was used in 27.5% and MD in 72.5% of cases. In total, 393 patients were used for training and 98 for testing. According to nCV, the Gradient Boosting Machine performed best, with 89% accuracy (SD 2%), 95% sensitivity (SD 3%), 73% specificity (SD 8%), and 92% AUROC (SD 1%). The most relevant features were lead dwelling time, ejection fraction, creatinine, ICD presence, prior cardiac surgery, and permanent atrial fibrillation.
conclusionsML tools showed reliable performance in predicting MD need in TLE procedures, supporting planning and referral to high-volume centres.
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