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Komlósi F, Tóth P, Bohus G, Vámosi P, Tokodi M, Szegedi N, Salló Z, Piros K, Perge P, Osztheimer I, Ábrahám P, Széplaki G, Merkely B, Gellér L, Nagy KV. Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease. Bioengineering (Basel) 2023; 10:1386. [PMID: 38135977 PMCID: PMC10740977 DOI: 10.3390/bioengineering10121386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
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.
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Affiliation(s)
- Ferenc Komlósi
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Patrik Tóth
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Gyula Bohus
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Péter Vámosi
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Márton Tokodi
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Nándor Szegedi
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Zoltán Salló
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Katalin Piros
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Péter Perge
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - István Osztheimer
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Pál Ábrahám
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Gábor Széplaki
- Mater Private Hospital, 69 Eccles St., D07 WKW8 Dublin, Ireland;
| | - Béla Merkely
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - László Gellér
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Klaudia Vivien Nagy
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
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