We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score.
- Authors
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
- 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. Open in new tab Download slide Open in new tab Download slide
- Publication
European Heart Journal, 2020, Vol 41, Issue 18, p1747
- ISSN
0195-668X
- Publication type
Article
- DOI
10.1093/eurheartj/ehz902