Deep Learning-Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation
Ahmed Abdelhameed1, Harpreet Bhangu1, Jingna Feng1
1McWilliams School of Biomedical Informatics (A.A., J.F., F.L., X.H., C.T.), University of Texas Health Science Center at Houston, TX; and Department of Artificial Intelligence and Informatics (A.A., J.F., F.L., X.H., C.T.) and Department of Transplantation (H.B., P.P., L.Y.), Mayo Clinic, Jacksonville, FL.
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.
