As mitral valve transcatheter edge-to-edge repair (M-TEER) is evolving as an effective treatment for high-risk surgical patients with mitral regurgitation, there is a pressing need for cardiologists to optimize resources through risk stratification of in-hospital mortality for this patient population. Although current risk-prediction models have been shown to predict adverse outcomes with reasonable accuracy, models trained using the US nationwide population are lacking. This study aimed to identify clinical, demographic, and procedural features that predict in-hospital mortality, and to derive and validate an in-hospital mortality risk-prediction tool in patients who have undergone M-TEER. A total of 9,373 admissions from the Nationwide Readmissions Database of patients who underwent M-TEER between 2015 and 2018 were used to develop and validate the model. We first performed least absolute shrinkage and selection operator (LASSO) regularization of Cox regression (Coxnet) that is 10-fold cross-validated. The non-zero coefficients were multiplied with the respective values of each observation of the predictors to build the scoring formula. Out of 9,373 admissions, 196 patients (2%) died in-hospital during index admission. In descending order, the top variables that were most predictive of in-hospital mortality were higher age, presence of fluid and electrolyte disturbance, and large metropolitan location of the hospital. The validation C-statistic of the MitraCox score was 0.82. Using X-tile software (Yale School of Medicine, New Haven, Connecticut), 2 cutoffs of the score were determined on the basis of in-hospital mortality and length of stay, and the survival of the population was classified into 3 risk groups: low, intermediate, and high. The scoring system deployed online as a web-based calculator can be accessed at https://kathavs.shinyapps.io/Mitracox_Kapadia/. In conclusion, MitraCox score is easy to calculate and predicts in-hospital mortality depending on length of stay in a dynamic manner.
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