Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increased their success rate, a substantial challenge in increasing the number of DCD donations resides in the uncertainty regarding the timing of cardiac death after terminal extubation, impacting the risk of prolonged ischemic organ injury, and negatively affecting post-transplant outcomes. In this study, we trained and externally validated an ODE-RNN model, which combines recurrent neural network with neural ordinary equations and excels in processing irregularly-sampled time series data. The model is designed to predict time-to-death following terminal extubation in the intensive care unit (ICU) using the last 24 hours of clinical observations. Our model was trained on a cohort of 3,238 patients from Yale New Haven Hospital, and validated on an external cohort of 1,908 patients from six hospitals across Connecticut. The model achieved accuracies of 95.3 ± 1.0% and 95.4 ± 0.7% for predicting whether death would occur in the first 30 and 60 minutes, respectively, with a calibration error of 0.024 ± 0.009. Heart rate, respiratory rate, mean arterial blood pressure (MAP), oxygen saturation (SpO2), and Glasgow Coma Scale (GCS) scores were identified as the most important predictors. Surpassing existing clinical scores, our model sets the stage for reduced organ acquisition costs and improved post-transplant outcomes.

The authors have declared no competing interest.

This study was partly funded by: HHS | National Institutes of Health (NIH) Valid 1F30AI157270-01: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy HHS | National Institutes of Health (NIH) Valid R01HD100035: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy HHS | National Institutes of Health (NIH) Valid R01GM130847: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy HHS | National Institutes of Health (NIH) Valid R01GM135929: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy Yale Innovation Grant : Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy, Ramesh Batra NSF Career grant 2047856: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy Chan-Zuckerberg Initiative CZF2019-182702: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy Chan-Zuckerberg Initiative CZF2019-002440: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy Sloan Fellowship FG-2021-15883: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy Novo Nordisk (Novo Nordisk Global) Valid GR112933: Xingzhi Sun, Edward De Brouwer, Chen Liu, Smita Krishnaswamy

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