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Updated: Jun 13, 2026
Pre-clinical Model of Cardiac Donation after Circulatory Death
Published on: August 2, 2019
A Novel Model to Predict Progression to Death After Withdrawal of Care in Potential Donation-After-Circulatory-Death
Austin Ayer1, Praneet Mylavarapu1, David Golombeck2
1Division of Cardiovascular Medicine, University of California San Diego, La Jolla, California.
Insights
Predicting heart donation after circulatory death (DCD) is challenging. A new model accurately predicts circulatory death in potential DCD heart donors, improving organ availability.
Area of Science:
- Cardiology
- Transplantation Medicine
- Medical Informatics
Background:
- Predicting successful heart donation after circulatory death (DCD) is a significant clinical challenge.
- Accurate prediction models are needed to optimize organ utilization and improve patient outcomes.
Purpose of the Study:
- To develop and validate a predictive model for circulatory death in potential DCD heart donors.
- To identify key clinical variables associated with progression to circulatory death.
Main Methods:
- Retrospective review of adult DCD heart offers.
- Development and validation of multivariate logistic regression and machine learning models.
- Assessment of predictive performance using stratified cross-validation and external validation.
Main Results:
- 150 out of 234 (64.1%) potential DCD heart donors progressed to circulatory death.
- Key predictors included Glasgow Coma Scale, brainstem reflexes, hemodynamic parameters, and respiratory support variables.
- The top-performing model achieved an AUC of 0.77 in the development cohort and 0.88 in external validation.
Conclusions:
- A novel predictive model for circulatory death in DCD heart donors has been developed.
- This model shows promising performance for identifying suitable organ donors.
- Further implementation may enhance heart transplantation rates.
Background:
Predicting successful heart donation after circulatory death (DCD) remains a challenge. We developed a model to predict progression to circulatory death after withdrawal of care in potential DCD heart donors.
Methods And Results:
Adult DCD heart offers at a single institution over a 6-month period were retrospectively reviewed. Univariate logistic regression was used to assess associations between pre-withdrawal-of-care variables and a binary outcome of circulatory death. The discriminatory performance of multivariate logistic regression and 4 supervised machine-learning models in predicting circulatory death was assessed by stratified cross validation. The top-performing model type was developed using the full dataset and externally validated in a cohort of potential donors at a distant center. Of 234 included offers, 150 (64.1%) progressed to circulatory death. Factors associated with circulatory death included Glasgow Coma Scale, brainstem reflexes; mean arterial pressure, vasoactive inotropic score, ventilator triggering, positive end-expiratory pressure, partial pressure of arterial oxygen/fraction of inspired oxygen ratio, and serum sodium. A multivariate logistic regression model demonstrated sensitivity of 0.85, specificity of 0.52, and area under the receiver operating characteristic curve of 0.77 in the development cohort, and sensitivity of 1.00, specificity of 0.50, and area under the receiver operating characteristic curve of 0.88 in external validation.
Conclusions:
We present a novel model to predict progression to circulatory death among potential DCD heart donors.
