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Utilizing Machine Learning to Identify Predictors of Corticosteroid Discontinuation 1-Year After Adult Heart
Caroline Chen1, Zeina Jedeon2, Abhishek Jaiswal2,3
1Department of Pharmacy Practice, University of Connecticut School of Pharmacy, Storrs, Connecticut, USA.
Insights
Machine learning identified key factors for discontinuing corticosteroid (CS) therapy one year after heart transplantation (HT). Lower transplant center volume, shorter donor ischemia time, and LVAD use predicted CS cessation.
Area of Science:
- Cardiology
- Immunology
- Data Science
Background:
- Corticosteroids (CS) are crucial post-heart transplantation (HT) but associated with adverse effects.
- Identifying predictors of CS discontinuation is vital for optimizing long-term outcomes.
Purpose of the Study:
- To leverage machine learning (ML) to identify factors associated with CS discontinuation 1-year post-adult HT.
- To develop a predictive model for CS cessation using recipient, donor, and transplant variables.
Main Methods:
- Retrospective analysis of the United Network for Organ Sharing (UNOS) database.
- Utilized eXtreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP) for predictive modeling and feature importance.
- Included 72,730 adult first-time HT recipients discharged on CS.
Main Results:
- 17.9% of recipients discontinued CS within 1 year.
- Predictors of CS discontinuation included lower transplant center volume, shorter donor ischemic time, and LVAD use at transplant.
- The XGBoost model demonstrated strong performance (AUC=0.854).
Conclusions:
- Machine learning effectively identified key predictors of 1-year CS discontinuation post-heart transplant.
- Transplant center volume, donor ischemia time, and LVAD use are significant factors influencing CS cessation.
Objective:
To use machine learning methods to identify factors associated with corticosteroid (CS) discontinuation 1 year after adult heart transplantation (HT).
Design:
Retrospective, observational, cohort study.
Data Source:
This study used data from the United Network for Organ Sharing (UNOS) database.
Patients:
We included adults (age ≥ 18 years) who underwent their first HT between January 2000 and December 2023 in the UNOS database with follow-up through December 2024, who were discharged on a CS.
Measurements:
We divided the cohort into those with or without CS at 1-year post-transplant follow-up. We used the eXtreme Gradient Boosting (XGBoost) algorithm to build a model predicting CS discontinuation at 1 year. Relevant recipient, donor, and transplant variables were included to train the model, with Shapley Additive Explanations (SHAP) used to identify and interpret the most important and predictive features.
Results:
We identified 72,730 HT recipients; 13,017 (17.9%) had CS discontinued within the first year. Compared with the CS cessation group, those who continued CS were more likely to have had a lower BMI, lower ischemic etiology of cardiomyopathy, lower intra-aortic balloon pump (IABP) and left ventricular assist device (LVAD) use before HT, better renal function, and sustained longer donor ischemic time. Model performance was strong, with an area under the curve of 0.854 (95% confidence interval: 0.848-0.861). Lower average transplant center volume (number of transplants per year), shorter donor ischemic time, and LVAD use at transplant predicted CS discontinuation.
Conclusions:
In a large national database, utilizing novel ML modeling techniques, we identified annual transplant center volume, donor ischemia time, and LVAD use at the time of HT as the best predictors of CS discontinuation 1 year after HT.
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