Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease
Background: Ventricular tachycardia (VT) recurrence after catheter ablation remains a concern, emphasizing the need for precise risk assessment. We aimed to use machine learning (ML) to predict 1-month and 1-year VT recurrence following VT ablation. Methods: For 337 patients undergoing VT ablation,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/12/1386 |
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| author | Ferenc Komlósi Patrik Tóth Gyula Bohus Péter Vámosi Márton Tokodi Nándor Szegedi Zoltán Salló Katalin Piros Péter Perge István Osztheimer Pál Ábrahám Gábor Széplaki Béla Merkely László Gellér Klaudia Vivien Nagy |
| author_facet | Ferenc Komlósi Patrik Tóth Gyula Bohus Péter Vámosi Márton Tokodi Nándor Szegedi Zoltán Salló Katalin Piros Péter Perge István Osztheimer Pál Ábrahám Gábor Széplaki Béla Merkely László Gellér Klaudia Vivien Nagy |
| author_sort | Ferenc Komlósi |
| collection | DOAJ |
| description | Background: Ventricular tachycardia (VT) recurrence after catheter ablation remains a concern, emphasizing the need for precise risk assessment. We aimed to use machine learning (ML) to predict 1-month and 1-year VT recurrence following VT ablation. Methods: For 337 patients undergoing VT ablation, we collected 31 parameters including medical history, echocardiography, and procedural data. 17 relevant features were included in the ML-based feature selection, which yielded six and five optimal features for 1-month and 1-year recurrence, respectively. We trained several supervised machine learning models using 10-fold cross-validation for each endpoint. Results: We observed 1-month VT recurrence was observed in 60 (18%) cases and accurately predicted using our model with an area under the receiver operating curve (AUC) of 0.73. Input features used were hemodynamic instability, incessant VT, ICD shock, left ventricular ejection fraction, TAPSE, and non-inducibility of the clinical VT at the end of the procedure. A separate model was trained for 1-year VT recurrence (observed in 117 (35%) cases) with a mean AUC of 0.71. Selected features were hemodynamic instability, the number of inducible VT morphologies, left ventricular systolic diameter, mitral regurgitation, and ICD shock. For both endpoints, a random forest model displayed the highest performance. Conclusions: Our ML models effectively predict VT recurrence post-ablation, aiding in identifying high-risk patients and tailoring follow-up strategies. |
| first_indexed | 2024-03-08T20:59:16Z |
| format | Article |
| id | doaj.art-01846a6631e9440d99c60211b0a6ff7a |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-08T20:59:16Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-01846a6631e9440d99c60211b0a6ff7a2023-12-22T13:54:06ZengMDPI AGBioengineering2306-53542023-12-011012138610.3390/bioengineering10121386Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart DiseaseFerenc Komlósi0Patrik Tóth1Gyula Bohus2Péter Vámosi3Márton Tokodi4Nándor Szegedi5Zoltán Salló6Katalin Piros7Péter Perge8István Osztheimer9Pál Ábrahám10Gábor Széplaki11Béla Merkely12László Gellér13Klaudia Vivien Nagy14Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryMater Private Hospital, 69 Eccles St., D07 WKW8 Dublin, IrelandHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryHeart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, HungaryBackground: Ventricular tachycardia (VT) recurrence after catheter ablation remains a concern, emphasizing the need for precise risk assessment. We aimed to use machine learning (ML) to predict 1-month and 1-year VT recurrence following VT ablation. Methods: For 337 patients undergoing VT ablation, we collected 31 parameters including medical history, echocardiography, and procedural data. 17 relevant features were included in the ML-based feature selection, which yielded six and five optimal features for 1-month and 1-year recurrence, respectively. We trained several supervised machine learning models using 10-fold cross-validation for each endpoint. Results: We observed 1-month VT recurrence was observed in 60 (18%) cases and accurately predicted using our model with an area under the receiver operating curve (AUC) of 0.73. Input features used were hemodynamic instability, incessant VT, ICD shock, left ventricular ejection fraction, TAPSE, and non-inducibility of the clinical VT at the end of the procedure. A separate model was trained for 1-year VT recurrence (observed in 117 (35%) cases) with a mean AUC of 0.71. Selected features were hemodynamic instability, the number of inducible VT morphologies, left ventricular systolic diameter, mitral regurgitation, and ICD shock. For both endpoints, a random forest model displayed the highest performance. Conclusions: Our ML models effectively predict VT recurrence post-ablation, aiding in identifying high-risk patients and tailoring follow-up strategies.https://www.mdpi.com/2306-5354/10/12/1386ventricular tachycardiacatheter ablationrecurrencemachine learningrandom forest |
| spellingShingle | Ferenc Komlósi Patrik Tóth Gyula Bohus Péter Vámosi Márton Tokodi Nándor Szegedi Zoltán Salló Katalin Piros Péter Perge István Osztheimer Pál Ábrahám Gábor Széplaki Béla Merkely László Gellér Klaudia Vivien Nagy Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease Bioengineering ventricular tachycardia catheter ablation recurrence machine learning random forest |
| title | Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease |
| title_full | Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease |
| title_fullStr | Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease |
| title_full_unstemmed | Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease |
| title_short | Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease |
| title_sort | machine learning based prediction of 1 year arrhythmia recurrence after ventricular tachycardia ablation in patients with structural heart disease |
| topic | ventricular tachycardia catheter ablation recurrence machine learning random forest |
| url | https://www.mdpi.com/2306-5354/10/12/1386 |
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