Focus on Artificial Intelligence
Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement

https://doi.org/10.1016/j.jcin.2019.06.013Get rights and content
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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.

Key Words

machine learning
mortality
transcatheter aortic valve replacement

Abbreviations and Acronyms

ACC
American College of Cardiology
AI
artificial intelligence
ANN
artificial neural networks
AUC
area under the receiver-operating curve
CI
confidence interval
ICD-9-CM
International Classification of Diseases-9th Edition-Clinical Modification
LR
logistic regression
ML
machine learning
NB
naive Bayes
NIS
National Inpatient Sample
RF
random forest
SAVR
surgical aortic valve replacement
STS
Society of Thoracic Surgeons
TAVR
transcatheter aortic valve replacement
TVT
transcatheter valve therapy

Cited by (0)

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.