Machine learning-based prediction of 1-year all-cause mortality in patients undergoing CRT implantation: Validation of the SEMMELWEIS-CRT score in the European CRT Survey I dataset

Abstract

We aimed to externally validate the SEMMELWEIS-CRT score for predicting 1-year all-cause mortality in the European Cardiac Resynchronization Therapy (CRT) Survey I dataset – a large multi-center cohort of patients undergoing CRT implantation. The SEMMELWEIS-CRT score is a machine learning-based tool trained for predicting all-cause mortality in patients undergoing CRT implantation. This tool demonstrated impressive performance during internal validation but has not yet been validated externally. To this end, we applied it to the data of 1,367 patients from the European CRT Survey I dataset. The SEMMELWEIS-CRT predicted 1-year mortality with an area under the receiver operating characteristic curve (AUC) of 0.729 [0.682–0.776], which concurred with the performance measured during internal validation (AUC: 0.768 [0.674–0.861], p=0.466). Moreover, the SEMMELWEIS-CRT score outperformed multiple conventional statistics-based risk scores, and we demonstrated that a higher predicted probability is not only associated with a higher risk of death (odds ratio [OR]: 1.081 [1.061–1.101], p<0.001) but also with an increased risk of hospitalizations for any cause (OR: 1.013 [1.002–1.025], p=0.020) or for heart failure (OR: 1.033 [1.015–1.052], p<0.001), a less than 5% improvement in left ventricular ejection fraction (OR: 1.033 [1.021–1.047], p<0.001), and lack of improvement in NYHA functional class compared to baseline (OR: 1.018 [1.006–1.029], p=0.003). In the European CRT Survey I dataset, the SEMMELWEIS-CRT score predicted 1-year all-cause mortality with good discriminatory power, which confirms the generalizability and demonstrates the potential clinical utility of this machine learning-based risk stratification tool.