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Tokodi, Márton; Schwertner, Walter Richard; Kovács, Attila; Tősér, Zoltán; Staub, Levente; Sárkány, András; Lakatos, Bálint Károly; Behon, Anett; Boros, András Mihály; Perge, Péter; Kutyifa, Valentina; Széplaki, Gábor; Gellér, László; Merkely, Béla; Kosztin, Annamária

Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score

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  • Medientyp: E-Artikel
  • Titel: Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score
  • Beteiligte: Tokodi, Márton; Schwertner, Walter Richard; Kovács, Attila; Tősér, Zoltán; Staub, Levente; Sárkány, András; Lakatos, Bálint Károly; Behon, Anett; Boros, András Mihály; Perge, Péter; Kutyifa, Valentina; Széplaki, Gábor; Gellér, László; Merkely, Béla; Kosztin, Annamária
  • Erschienen: Oxford University Press (OUP), 2020
  • Erschienen in: European Heart Journal, 41 (2020) 18, Seite 1747-1756
  • Sprache: Englisch
  • DOI: 10.1093/eurheartj/ehz902
  • ISSN: 0195-668X; 1522-9645
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: Abstract Aims Our aim was to develop a machine learning (ML)-based risk stratification system to predict 1-, 2-, 3-, 4-, and 5-year all-cause mortality from pre-implant parameters of patients undergoing cardiac resynchronization therapy (CRT). Methods and results Multiple ML models were trained on a retrospective database of 1510 patients undergoing CRT implantation to predict 1- to 5-year all-cause mortality. Thirty-three pre-implant clinical features were selected to train the models. The best performing model [SEMMELWEIS-CRT score (perSonalizEd assessMent of estiMatEd risk of mortaLity With machinE learnIng in patientS undergoing CRT implantation)], along with pre-existing scores (Seattle Heart Failure Model, VALID-CRT, EAARN, ScREEN, and CRT-score), was tested on an independent cohort of 158 patients. There were 805 (53%) deaths in the training cohort and 80 (51%) deaths in the test cohort during the 5-year follow-up period. Among the trained classifiers, random forest demonstrated the best performance. For the prediction of 1-, 2-, 3-, 4-, and 5-year mortality, the areas under the receiver operating characteristic curves of the SEMMELWEIS-CRT score were 0.768 (95% CI: 0.674–0.861; P < 0.001), 0.793 (95% CI: 0.718–0.867; P < 0.001), 0.785 (95% CI: 0.711–0.859; P < 0.001), 0.776 (95% CI: 0.703–0.849; P < 0.001), and 0.803 (95% CI: 0.733–0.872; P < 0.001), respectively. The discriminative ability of our model was superior to other evaluated scores. Conclusion The SEMMELWEIS-CRT score (available at semmelweiscrtscore.com) exhibited good discriminative capabilities for the prediction of all-cause death in CRT patients and outperformed the already existing risk scores. By capturing the non-linear association of predictors, the utilization of ML approaches may facilitate optimal candidate selection and prognostication of patients undergoing CRT implantation.
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