Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement

Dagmar F. Hernandez-Suarez, Yeunjung Kim, P. Villablanca, Tanush Gupta, Jose M. Wiley, Brenda G. Nieves-Rodriguez, Jovaniel Rodriguez-Maldonado, Roberto Feliu Maldonado, Istoni da Luz Sant'Ana, Cristina Sanina, P. Cox-Alomar, Harish Ramakrishna, A. Lopez-Candales, William W. O'Neill, Duane S. Pinto, A. Latib, A. Roche-Lima

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Objectives: This study sought to develop and compare an array of machine learning methods to predict in-hospital mortality after transcatheter aortic valve replacement (TAVR) in the United States. Background: Existing risk prediction tools for in-hospital complications in patients undergoing TAVR have been designed using statistical modeling approaches and have certain limitations. Methods: Patient data were obtained from the National Inpatient Sample database from 2012 to 2015. The data were randomly divided into a development cohort (n = 7,615) and a validation cohort (n = 3,268). Logistic regression, artificial neural network, naive Bayes, and random forest machine learning algorithms were applied to obtain in-hospital mortality prediction models. Results: A total of 10,883 TAVRs were analyzed in our study. The overall in-hospital mortality was 3.6%. Overall, prediction models’ performance measured by area under the curve were good (>0.80). The best model was obtained by logistic regression (area under the curve: 0.92; 95% confidence interval: 0.89 to 0.95). Most obtained models plateaued after introducing 10 variables. Acute kidney injury was the main predictor of in-hospital mortality ranked with the highest mean importance in all the models. The National Inpatient Sample TAVR score showed the best discrimination among available TAVR prediction scores. Conclusions: Machine learning methods can generate robust models to predict in-hospital mortality for TAVR. The National Inpatient Sample TAVR score should be considered for prognosis and shared decision making in TAVR patients.

Original languageEnglish (US)
Pages (from-to)1328-1338
Number of pages11
JournalJACC: Cardiovascular Interventions
Volume12
Issue number14
DOIs
StatePublished - Jul 22 2019

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Keywords

  • machine learning
  • mortality
  • transcatheter aortic valve replacement

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

Hernandez-Suarez, D. F., Kim, Y., Villablanca, P., Gupta, T., Wiley, J. M., Nieves-Rodriguez, B. G., Rodriguez-Maldonado, J., Feliu Maldonado, R., da Luz Sant'Ana, I., Sanina, C., Cox-Alomar, P., Ramakrishna, H., Lopez-Candales, A., O'Neill, W. W., Pinto, D. S., Latib, A., & Roche-Lima, A. (2019). Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement. JACC: Cardiovascular Interventions, 12(14), 1328-1338. https://doi.org/10.1016/j.jcin.2019.06.013