Background: Lung transplantation is the only life-saving therapy for end-stage lung disease. However, lung transplantation has the worst survival among all solid organ transplants.1 We applied machine learning to a large standardized electronic health record (EHR) dataset from the United Network for Organ Sharing (UNOS) to test whether pre-transplant and peri-transplant donor and recipient features can predict one-, three- and five-year survival, or favorable long-term outcomes in lung transplant. Methods: We used data from 43,869 first time lung transplant recipients >18 years old from 1987 to November 2022 for whom one-, three-, and five-year survival outcomes were available. We applied XGBoost or a tabular BERT model called EHRFormer to the UNOS EHR dataset. Results: Using pre-transplant features XGBoost predicted one year mortality with a test AUC = 0.6 [0.57, 0.64] 95% CI. Addition of peri-transplant features only modestly improved AUC for one-year mortality prediction (test AUC = 0.63 [0.60, 0.67] 95% CI and 0.64 [0.63, 0.66] 95% CI for XGBoost and EHRFormer, respectively). Top predictive features of one year mortality using peri-transplant features from each model were length of index stay, transplant type, recipient age, ventilation status during the index stay, and creatinine at the time of transplant. Both XGBoost and EHRFormer performed better when predicting lung function at one-year post-transplant (XGBoost test AUC = 0.74; EHRFormer test AUC = 0.76). Both models identified and used features previously associated with transplant outcomes to inform predictions. Conclusions: Despite machine learning approaches identifying known risk factors for transplant outcomes, EHR data collected by UNOS poorly predict one-, three-, and five-year mortality outcomes of lung transplantation. These results suggest caution when using pre-transplant EHR features to predict lung transplant outcomes.

The authors have declared no competing interest.

This study did not receive any funding.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The Institutional Review Board of Northwestern University gave ethical approval for this work (STU00221316).

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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All patient-de-identified data was obtained from the United Network for Organ Sharing (UNOS) Standard Transplant Analysis and Research File (SRTR), which is based on the Organ Procurement and Transplantation Network data as of 11/8/2023.