{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T21:53:05Z","timestamp":1791237185866,"version":"4.1.0"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Permanent pacemaker implantation (PPI) remains one of the most common complications following transcatheter aortic valve replacement (TAVR). Identifying patients at increased risk for post-procedural conduction disturbances is clinically important for procedural planning and patient management. The aim of this study was to develop and evaluate a machine learning-based model for predicting the risk of PPI after TAVR. Methods: This prospective study was conducted between 2019 and 2025, and included 179 patients with severe aortic stenosis who underwent TAVR. Patient eligibility was determined by a multidisciplinary Heart Team based on clinical, echocardiographic, and imaging criteria. The primary endpoint was PPI occurring during hospitalization or within 30 days after the procedure. Statistical analyses were performed using RStudio (v. 2024.09.1+394)and Python (v.3.12.3), including comparative tests for continuous and categorical variables, receiver operating characteristic analysis to assess model performance, and SHapley Additive exPlanations (SHAP) to evaluate feature importance and model interpretability. Results: A total of 179 patients undergoing TAVR were included in the analysis. PPI occurred in 17 patients (9.5%) within 30 days after the procedure. A machine learning model was developed to predict post-TAVR PPI. The model demonstrated good predictive performance, with an overall accuracy of 0.944 and a weighted F1-score of 0.947. The confusion matrix showed that the model correctly classified 155 patients without PPI and 14 patients with PPI, with only a small number of false predictions. Explainability analyses using SHAP and permutation feature importance revealed that anatomical and procedural variables had the greatest impact on model predictions. The most influential predictors included valve size, right coronary sinus diameter, prosthetic valve diameter, and mean aortic annulus diameter. In contrast, baseline clinical variables such as left ventricular ejection fraction, previous myocardial infarction, and mean transaortic gradient showed a comparatively lower contribution to the prediction of PPI after TAVR. Conclusions: This study demonstrates that machine learning models can effectively predict the risk of PPI after TAVR. Anatomical characteristics of the aortic root and prosthesis-related parameters were the main determinants of PPI, whereas baseline clinical variables had a lower impact. The use of explainable artificial intelligence methods, such as SHAP analysis, may improve risk stratification and support procedural planning in patients undergoing TAVR.<\/jats:p>","DOI":"10.3390\/diagnostics16111720","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:33:14Z","timestamp":1780479194000},"page":"1720","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning-Driven Probability of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement"],"prefix":"10.3390","volume":"16","author":[{"given":"Marcel","family":"Abras","sequence":"first","affiliation":[{"name":"Department of Cardiology, Nicolae Testemitanu State University of Medicine and Pharmacy, MD-2004 Chisinau, Moldova"},{"name":"Department of Interventional Cardiology, Public Healthcare Institution\u2014Institute of Cardiology, MD-2025 Chisinau, Moldova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7530-3012","authenticated-orcid":false,"given":"Daniela","family":"Bursacovschi","sequence":"additional","affiliation":[{"name":"Department of Heart Failure, Public Healthcare Institution\u2014Institute of Cardiology, MD-2025 Chisinau, Moldova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ecaterina","family":"Pasat","sequence":"additional","affiliation":[{"name":"Department of Interventional Cardiology, Public Healthcare Institution\u2014Institute of Cardiology, MD-2025 Chisinau, Moldova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria-Magdalena","family":"Vicol","sequence":"additional","affiliation":[{"name":"Department of General Cardiology, Public Healthcare Institution\u2014Institute of Cardiology, MD-2025 Chisinau, Moldova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tatiana","family":"Abras","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Public Medico-Sanitary Institution Republican Clinical Hospital \u201cTimofei Mosneaga\u201d, MD-2025 Chisinau, Moldova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucia","family":"Mazur-Nicorici","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Nicolae Testemitanu State University of Medicine and Pharmacy, MD-2004 Chisinau, Moldova"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5483-8672","authenticated-orcid":false,"given":"Oleg","family":"Arnaut","sequence":"additional","affiliation":[{"name":"Department of Human Physiology and Biophysics, Nicolae Testemitanu State University of Medicine and Pharmacy, MD-2004 Chisinau, Moldova"},{"name":"Bioinformatics and Computational Medicine Laboratory, Nicolae Testemitanu State University of Medicine and Pharmacy, MD-2004 Chisinau, Moldova"},{"name":"National Cancer Registry, Public Healthcare Institution\u2014Oncological Institute, MD-2025 Chisinau, Moldova"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rosseel, L., Mylotte, D., Cosyns, B., Vanhaverbeke, M., Zweiker, D., Teles, R.C., Anger\u00e5s, O., Neylon, A., Rudolph, T.K., and Wykrzykowska, J.J. (2023). Contemporary European practice in transcatheter aortic valve implantation: Results from the 2022 European TAVI Pathway Registry. Front. Cardiovasc. Med., 10.","DOI":"10.3389\/fcvm.2023.1227217"},{"key":"ref_2","first-page":"58","article-title":"Transcatheter aortic valve implantation. First experience of minimally invasive treatment in the Republic of Moldova","volume":"63","author":"Abras","year":"2020","journal-title":"Mold. Med. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/j.jacc.2024.01.025","article-title":"Prevalence and Outcomes of Patients with Discordant High-Gradient Aortic Stenosis","volume":"83","author":"Unger","year":"2024","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_4","first-page":"7","article-title":"The efficacy of transcatheter treatment of aortic valve stenosis based on the type of implanted valve","volume":"81","author":"Abras","year":"2025","journal-title":"Bull. Acad. Sci. Mold. Med. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2771","DOI":"10.1093\/eurheartj\/ehz924","article-title":"Clinical impact of conduction disturbances in transcatheter aortic valve replacement recipients: A systematic review and meta-analysis","volume":"41","author":"Faroux","year":"2020","journal-title":"Eur. Heart J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1016\/j.jcin.2016.07.026","article-title":"Incidence, Predictors, and Outcomes of Permanent Pacemaker Implantation Following Transcatheter Aortic Valve Replacement","volume":"9","author":"Fadahunsi","year":"2016","journal-title":"JACC Cardiovasc. Interv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1016\/j.jacc.2020.12.062","article-title":"Ambulatory Electrocardiogram Monitoring in Patients Undergoing Transcatheter Aortic Valve Replacement: JACC State-of-the-Art Review","volume":"77","author":"Philippon","year":"2021","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.jacc.2014.04.033","article-title":"Predictors of permanent pacemaker implantation in patients with severe aortic stenosis undergoing TAVR: A meta-analysis","volume":"64","author":"Siontis","year":"2014","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1093\/eurheartj\/ehz227","article-title":"New-onset left bundle branch block after transcatheter aortic valve replacement is associated with adverse long-term clinical outcomes in intermediate-risk patients: An analysis from the PARTNER II trial","volume":"40","author":"Nazif","year":"2019","journal-title":"Eur. Heart J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.carrev.2022.10.004","article-title":"Site-Level Variation and Predictors of Permanent Pacemaker Implantation Following TAVR in the Evolut Low-Risk Trial","volume":"47","author":"Gada","year":"2023","journal-title":"Cardiovasc. Revascularization Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e036179","DOI":"10.1161\/JAHA.124.036179","article-title":"Trends in Transcatheter Versus Surgical Aortic Valve Replacement Outcomes in Patients with Low-Surgical Risk: A Systematic Review and Meta-Analysis of Randomized Trials","volume":"13","author":"Kazemian","year":"2024","journal-title":"J. Am. Heart Assoc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1016\/j.jcin.2023.05.020","article-title":"Incidence, Predictors, and Prognostic Impact of New Permanent Pacemaker Implantation After TAVR with Self-Expanding Valves","volume":"16","author":"Pagnesi","year":"2023","journal-title":"JACC Cardiovasc. Interv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, Y.T., Tsao, T.P., Lee, K.C., Lin, H.C., Liu, C.T., Hsiung, M.C., Yin, W.H., and Wei, J. (2025). Predictors of permanent pacemaker requirement in aortic stenosis patients undergoing self-expanding valve transcatheter aortic valve replacement using the cusp overlap technique. Front. Cardiovasc. Med., 12.","DOI":"10.3389\/fcvm.2025.1486375"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2481175","DOI":"10.1080\/14017431.2025.2481175","article-title":"Predictors of permanent pacemaker implantation after transcatheter aortic valve implantation","volume":"59","author":"Forsdahl","year":"2025","journal-title":"Scand. Cardiovasc. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.ipej.2024.03.003","article-title":"Depth of implantation in relation to membranous septum as a predictor of conduction disturbances after transcatheter aortic valve implantation","volume":"24","author":"Baraka","year":"2024","journal-title":"Indian Pacing Electrophysiol. J."},{"key":"ref_16","first-page":"27","article-title":"Diastolic dysfunction and myocardial ischemia in TAVI patients","volume":"12","author":"Abras","year":"2025","journal-title":"Mold. J. Health Sci."},{"key":"ref_17","first-page":"1071","article-title":"Predictors of permanent pacemaker implantation for transcatheter self-expandable aortic valve implant in the cusp overlap era","volume":"104","author":"Mendiz","year":"2024","journal-title":"Catheter. Cardiovasc. Interv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e039020","DOI":"10.1161\/JAHA.124.039020","article-title":"Anatomical and Functional Predictors of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Implantation","volume":"14","author":"Jung","year":"2025","journal-title":"J. Am. Heart Assoc."},{"key":"ref_19","first-page":"18","article-title":"Transcatheter aortic valve implantation with self-expandable prosthesis in the Republic of Moldova. The one year follow-up of the first ten patients","volume":"72","author":"Abras","year":"2022","journal-title":"Bull. Acad. Sci. Mold. Med. Sci."},{"key":"ref_20","first-page":"e11","article-title":"Intraventricular conduction disturbances after transcatheter aortic valve implantation","volume":"15","author":"Lin","year":"2020","journal-title":"Interv. Cardiol. Rev. Res. Resour."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.amjcard.2023.05.025","article-title":"Conduction Disturbance After Transcatheter Aortic Valve Implantation with Self- or Balloon-Expandable Valve According to the Implantation Depth","volume":"203","author":"Miyashita","year":"2023","journal-title":"Am. J. Cardiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"532","DOI":"10.5152\/AnatolJCardiol.2022.987","article-title":"Structural Changes of the Right Fibrous Trigone as a Risk Factor for Conduction Disturbance After Transcatheter Aortic Valve Implantation","volume":"26","author":"Aslan","year":"2022","journal-title":"Anatol. J. Cardiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.1093\/eurheartj\/ehab364","article-title":"2021 ESC Guidelines on cardiac pacing and cardiac resynchronization therapy","volume":"42","author":"Glikson","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"95","DOI":"10.4330\/wjc.v15.i3.95","article-title":"Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement: The role of machine learning","volume":"15","author":"Agasthi","year":"2023","journal-title":"World J. Cardiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25008","DOI":"10.1038\/s41598-024-76128-z","article-title":"Machine learning for pacemaker implantation prediction after TAVI using multimodal imaging data","volume":"14","author":"Nicol","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_26","first-page":"1357","article-title":"PRIME score for prediction of permanent pacemaker implantation after transcatheter aortic valve replacement","volume":"102","author":"Barrett","year":"2023","journal-title":"Catheter. Cardiovasc. Interv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100726","DOI":"10.1016\/j.shj.2025.100726","article-title":"Preprocedural CT and ECG Markers for Predicting Post-TAVR Pacemaker Requirement in High-Risk Patients","volume":"9","author":"Tretter","year":"2025","journal-title":"Struct. Heart"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1186\/s40001-023-01237-w","article-title":"A prediction model for permanent pacemaker implantation after transcatheter aortic valve replacement","volume":"28","author":"Qi","year":"2023","journal-title":"Eur. J. Med. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"32","DOI":"10.21037\/jmai-23-162","article-title":"Machine learning versus logistic regression for permanent pacemaker implantation prediction after transcatheter aortic valve replacement\u2014A systematic review and meta-analysis","volume":"7","author":"Rossi","year":"2024","journal-title":"J. Med. Artif. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1080\/24748706.2018.1467067","article-title":"Risk Prediction Model for Permanent Pacemaker Implantation after Transcatheter Aortic Valve Replacement","volume":"2","author":"Vejpongsa","year":"2018","journal-title":"Struct. Heart"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1161\/CIRCEP.120.008941","article-title":"Machine Learning Algorithms for Prediction of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement","volume":"14","author":"Tsushima","year":"2021","journal-title":"Circ. Arrhythmia Electrophysiol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/11\/1720\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:52:39Z","timestamp":1780480359000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/11\/1720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,3]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["diagnostics16111720"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16111720","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,3]]}}}