학술논문
Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease.
Document Type
Academic Journal
Author
Komlósi F; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Tóth P; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Bohus G; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Vámosi P; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Tokodi M; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Szegedi N; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Salló Z; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Piros K; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Perge P; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Osztheimer I; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Ábrahám P; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Széplaki G; Mater Private Hospital, 69 Eccles St., D07 WKW8 Dublin, Ireland.; Merkely B; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Gellér L; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.; Nagy KV; Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary.
Source
Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101676056 Publication Model: Electronic Cited Medium: Print ISSN: 2306-5354 (Print) Linking ISSN: 23065354 NLM ISO Abbreviation: Bioengineering (Basel) Subsets: PubMed not MEDLINE
Subject
Language
English
ISSN
2306-5354
Abstract
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.
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.