Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States

Dagmar F. Hernandez-Suarez, Sagar Ranka, Yeunjung Kim, Azeem Latib, Jose Wiley, Angel Lopez-Candales, Duane S. Pinto, Maday C. Gonzalez, Harish Ramakrishna, Cristina Sanina, Brenda G. Nieves-Rodriguez, Jovaniel Rodriguez-Maldonado, Roberto Feliu Maldonado, Israel J. Rodriguez-Ruiz, Istoni da Luz Sant'Ana, Karlo A. Wiley, Pedro Cox-Alomar, Pedro A. Villablanca, Abiel Roche-Lima

Research output: Contribution to journalArticle

Abstract

Background: Transcatheter mitral valve repair utilization has increased significantly in the United States over the last years. Yet, a risk-prediction tool for adverse events has not been developed. We aimed to generate a machine-learning-based algorithm to predict in-hospital mortality after TMVR. Methods: Patients who underwent TMVR from 2012 through 2015 were identified using the National Inpatient Sample database. The study population was randomly divided into a training set (n = 636) and a testing set (n = 213). Prediction models for in-hospital mortality were obtained using five supervised machine-learning classifiers. Results: A total of 849 TMVRs were analyzed in our study. The overall in-hospital mortality was 3.1%. A naïve Bayes (NB) model had the best discrimination for fifteen variables, with an area under the receiver-operating curve (AUC) of 0.83 (95% CI, 0.80–0.87), compared to 0.77 for logistic regression (95% CI, 0.58–0.95), 0.73 for an artificial neural network (95% CI, 0.55–0.91), and 0.67 for both a random forest and a support-vector machine (95% CI, 0.47–0.87). History of coronary artery disease, of chronic kidney disease, and smoking were the three most significant predictors of in-hospital mortality. Conclusions: We developed a robust machine-learning-derived model to predict in-hospital mortality in patients undergoing TMVR. This model is promising for decision-making and deserves further clinical validation.

Original languageEnglish (US)
JournalCardiovascular Revascularization Medicine
DOIs
StateAccepted/In press - 2020

Keywords

  • Machine learning
  • Mortality
  • Transcatheter mitral valve repair

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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    Hernandez-Suarez, D. F., Ranka, S., Kim, Y., Latib, A., Wiley, J., Lopez-Candales, A., Pinto, D. S., Gonzalez, M. C., Ramakrishna, H., Sanina, C., Nieves-Rodriguez, B. G., Rodriguez-Maldonado, J., Feliu Maldonado, R., Rodriguez-Ruiz, I. J., da Luz Sant'Ana, I., Wiley, K. A., Cox-Alomar, P., Villablanca, P. A., & Roche-Lima, A. (Accepted/In press). Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States. Cardiovascular Revascularization Medicine. https://doi.org/10.1016/j.carrev.2020.06.017