TY - JOUR
T1 - Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement
AU - Hernandez-Suarez, Dagmar F.
AU - Kim, Yeunjung
AU - Villablanca, P.
AU - Gupta, Tanush
AU - Wiley, Jose
AU - Nieves-Rodriguez, Brenda G.
AU - Rodriguez-Maldonado, Jovaniel
AU - Feliu Maldonado, Roberto
AU - da Luz Sant'Ana, Istoni
AU - Sanina, Cristina
AU - Cox-Alomar, P.
AU - Ramakrishna, Harish
AU - Lopez-Candales, A.
AU - O'Neill, William W.
AU - Pinto, Duane S.
AU - Latib, A.
AU - Roche-Lima, A.
N1 - Funding Information:
This study was funded by the National Institutes of Health (U54MD007587, U54MD007600, S21MD001830, R25MD007607, and TL1TR001434-3). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. Dr. Pinto serves as a consultant for Medtronic, Abbott Vascular, Abiomed, NuPulse, Siemens, and Boston Scientific. Dr. Latib has served on the advisory boards of Medtronic and Abbott Vascular. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
PY - 2019/7/22
Y1 - 2019/7/22
N2 - 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.
AB - 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.
KW - machine learning
KW - mortality
KW - transcatheter aortic valve replacement
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U2 - 10.1016/j.jcin.2019.06.013
DO - 10.1016/j.jcin.2019.06.013
M3 - Article
C2 - 31320027
AN - SCOPUS:85068443858
VL - 12
SP - 1328
EP - 1338
JO - JACC: Cardiovascular Interventions
JF - JACC: Cardiovascular Interventions
SN - 1936-8798
IS - 14
ER -