Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States
| Autor: | Yeunjung Kim, Sagar Ranka, Jose Wiley, Pedro Cox-Alomar, Istoni da Luz Sant'Ana, Azeem Latib, Abiel Roche-Lima, Karlo Wiley, Harish Ramakrishna, Jovaniel Rodriguez-Maldonado, Maday Gonzalez, Brenda G. Nieves-Rodriguez, Cristina Sanina, Roberto Feliu Maldonado, Dagmar F. Hernandez-Suarez, Duane S. Pinto, Angel López-Candales, Israel J. Rodriguez-Ruiz, Pedro A. Villablanca |
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| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
030204 cardiovascular system & hematology Logistic regression Article Machine Learning Coronary artery disease 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine Internal medicine medicine Humans Hospital Mortality 030212 general & internal medicine Adverse effect business.industry Mitral Valve Insufficiency Bayes Theorem General Medicine medicine.disease United States Random forest Mitral Valve Population study Cardiology and Cardiovascular Medicine business Predictive modelling Kidney disease |
| Zdroj: | Cardiovasc Revasc Med |
| ISSN: | 1553-8389 |
| Popis: | Background Transcatheter mitral valve repair (TMVR) 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 naive 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. |
| Databáze: | OpenAIRE |
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