A Novel Model to Predict Progression to Death After Withdrawal of Care in Potential Donation-After-Circulatory-Death Heart Donors

The authors aimed to develop a predictive model for determining the likelihood of progression to circulatory death in potential heart donors after the withdrawal of care. By analyzing various clinical factors and employing logistic regression and machine-learning techniques, they identified key predictors and achieved a high sensitivity and area under the curve (AUC) in both the development and external validation cohorts. This model could enhance decision-making in the context of donation after circulatory death (DCD).