Using machine learning to improve risk prediction in durable left ventricular assist devices

Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist...

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Bibliographic Details
Published in:PloS one 2021-03, Vol.16 (3), p.e0247866-e0247866
Main Authors: Kilic, Arman, Dochtermann, Daniel, Padman, Rema, Miller, James K, Dubrawski, Artur
Format: Article
Language:English
Subjects:
Algorithms
Biology and Life Sciences
Calibration
Computer and Information Sciences
Editing
Engineering and Technology
Fashion models
Health aspects
Health care facilities
Health risks
Heart failure
Heart surgery
Learning algorithms
Liver diseases
Machine learning
Medicine and Health Sciences
Methodology
Mortality
Patients
Performance evaluation
Performance prediction
Regression analysis
Regression models
Research and Analysis Methods
Reviews
Risk
Risk assessment
Social Sciences
Surgery
Technology application
Transplants & implants
Variables
Ventricle
Ventricular assist devices
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Summary:Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist devices reported in the Interagency Registry for Mechanically Assisted Circulatory Support between March 1, 2006 and December 31, 2016 were included. The study cohort was randomly divided 3:1 into training and testing sets. Logistic regression and machine learning models (extreme gradient boosting) were created in the training set for 90-day and 1-year mortality and their performance was evaluated after bootstrapping with 1000 replications in the testing set. Differences in model performance were also evaluated in cases of concordance versus discordance in predicted risk between logistic regression and extreme gradient boosting as defined by equal size patient tertiles. A total of 16,120 patients were included. Calibration metrics were comparable between logistic regression and extreme gradient boosting. C-index was improved with extreme gradient boosting (90-day: 0.707 [0.683-0.730] versus 0.740 [0.717-0.762] and 1-year: 0.691 [0.673-0.710] versus 0.714 [0.695-0.734]; each p
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0247866