Faculty, Staff and Student Publications
Language
English
Publication Date
6-1-2024
Journal
Mayo Clinic Proceedings: Digital Health
DOI
10.1016/j.mcpdig.2024.03.005
PMID
38993485
PMCID
PMC11238640
PubMedCentral® Posted Date
4-15-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Objective: To validate deep learning models' ability to predict post-transplantation major adverse cardiovascular events (MACE) in patients undergoing liver transplantation (LT).
Patients and methods: We used data from Optum's de-identified Clinformatics Data Mart Database to identify liver transplant recipients between January 2007 and March 2020. To predict post-transplantation MACE risk, we considered patients' demographics characteristics, diagnoses, medications, and procedural data recorded back to 3 years before the LT procedure date (index date). MACE is predicted using the bidirectional gated recurrent units (BiGRU) deep learning model in different prediction interval lengths up to 5 years after the index date. In total, 18,304 liver transplant recipients (mean age, 57.4 years [SD, 12.76]; 7158 [39.1%] women) were used to develop and test the deep learning model's performance against other baseline machine learning models. Models were optimized using 5-fold cross-validation on 80% of the cohort, and model performance was evaluated on the remaining 20% using the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR).
Results: Using different prediction intervals after the index date, the top-performing model was the deep learning model, BiGRU, and achieved an AUC-ROC of 0.841 (95% CI, 0.822-0.862) and AUC-PR of 0.578 (95% CI, 0.537-0.621) for a 30-day prediction interval after LT.
Conclusion: Using longitudinal claims data, deep learning models can efficiently predict MACE after LT, assisting clinicians in identifying high-risk candidates for further risk stratification or other management strategies to improve transplant outcomes based on important features identified by the model.
Published Open-Access
yes
Recommended Citation
Abdelhameed, Ahmed; Bhangu, Harpreet; Feng, Jingna; et al., "Deep Learning-Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation" (2024). Faculty, Staff and Student Publications. 6197.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6197
Graphical Abstract
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