Machine learning prediction of mechanical dilatation in transvenous lead extraction for cardiac device-related infections: insights from a high-volume centre.

Authors

R De Lucia, A Micheli, M Parollo, M Podda, L Pedrelli, F Aliprandi, P Pantelidis, L Segreti, A Di Cori, A Parlato, A Canu, G Grifoni, V Barletta, S Viani, M Giannotti Santoro, G Zucchelli, Raffaele De Lucia

Published in

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing. Jun 29, 2026. Epub Jun 29, 2026.

Abstract

Transvenous 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.
We aimed to develop an ML-based model to predict the need for MD in patients undergoing TLE for infections.
Five 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.
Data 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.
ML tools showed reliable performance in predicting MD need in TLE procedures, supporting planning and referral to high-volume centres.

PMID:
42371346
Bibliographic data and abstract were imported from PubMed on 29 Jun 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.