The machine learning-based PRIME score demonstrated superior performance for predicting all-cause mortality after mitral valve surgery compared to EuroSCORE II (AUC 0.873 vs 0.654, P=0.006).
Observational (n=2,103)
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Does the PRIME score improve the prediction of all-cause mortality in patients undergoing mitral valve surgery compared to EuroSCORE II?
The machine learning-based PRIME score demonstrated superior performance for predicting mortality after mitral valve surgery compared with the traditional EuroSCORE II.
Tasa de eventos absoluta: 0.873% vs 0.654%
valor p: p=0.006
Background Mitral valve surgery (MVS) is an effective treatment for mitral valve diseases. There is a lack of reliable personalized risk prediction models for mortality in patients undergoing mitral valve surgery. Our aim was to develop a risk stratification system to predict all-cause mortality in patients after mitral valve surgery. Methods Different machine learning models for the prediction of all-cause mortality were trained on a derivation cohort of 1,883 patients undergoing mitral valve surgery split into a training cohort (70%) and internal validation cohort (30%) to predict all-cause mortality. Forty-five clinical variables routinely evaluated at discharge were used to train the models. The best performance model (PRIME score) was tested in an externally validated cohort of 220 patients undergoing mitral valve surgery. The model performance was evaluated according to the area under the curve (AUC). Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were compared with existing risk strategies. Results After a median follow-up of 2 years, there were 133 (7.063%) deaths in the derivation cohort and 17 (7.727%) deaths in the validation cohort. The PRIME score showed an AUC of 0.902 (95% confidence interval CI, 0.849–0.956) in the internal validation cohort and 0.873 (95% CI: 0.769–0.977) in the external validation cohort. In the external validation cohort, the performance of the PRIME score was significantly improved compared with that of the existing EuroSCORE II (NRI = 0.550, 95% CI 0.001–1.099, P = 0.049, IDI = 0.485, 95% CI 0.230–0.741, P 0.001). Conclusion Machine learning-based model (the PRIME score) that integrate clinical, demographic, imaging, and laboratory features demonstrated superior performance for the prediction of mortality patients after mitral valve surgery compared with the traditional risk model EuroSCORE II. Clinical Trial Registration http://www.clinicaltrials.gov , identifier NCT05141292.
Zhou et al. (Fri,) conducted a observational in Mitral valve disease undergoing surgery (n=2,103). PRIME score (Machine learning-based risk prediction model) vs. EuroSCORE II was evaluated on Prediction of all-cause mortality (AUC in external validation cohort) (95% CI 0.769-0.977, p=0.006). The machine learning-based PRIME score demonstrated superior performance for predicting all-cause mortality after mitral valve surgery compared to EuroSCORE II (AUC 0.873 vs 0.654, P=0.006).