Scholarlink

Background <p>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.</p> Objective <p>We aimed to develop an ML-based model to predict the need for MD in patients undergoing TLE for infections.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>ML tools showed reliable performance in predicting MD need in TLE procedures, supporting planning and referral to high-volume centres.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2026年6月29日

查看全文
  • 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

摘要

Background

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.

Objective

We aimed to develop an ML-based model to predict the need for MD in patients undergoing TLE for infections.

Methods

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.

Results

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.

Conclusions

ML tools showed reliable performance in predicting MD need in TLE procedures, supporting planning and referral to high-volume centres.

Graphical Abstract