Predicting Do-Not-Resuscitate Decisions in Critically Ill Patients Through Using Multitask Learning: A Retrospective Study of the MIMIC-IV Database

Timely identification of critically ill patients for do-not-resuscitate (DNR) decisions is crucial to support shared decision-making and ethical end-of-life care. This study developed an explainable multitask learning (MTL) model using the MIMIC-IV database to predict the DNR decision within 24 h. The model was trained on data from 7789 adult patients who were admitted to the intensive care units (ICUs) with a length of stay longer than 3 days, using features including clinical parameters and nursing assessments across a 72-h window. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration plots, and decision curve analysis. Interpretability was illustrated by SHapley Additive exPlanations (SHAP) plots and partial dependence plots (PDP). Error analysis was performed to explore the strengths and limitations of the established model. The MTL model achieved superior performance compared to single-task learning (AUROC: 0.798 vs 0.764). SHAP and PDP plots demonstrated that verbalization ability, ventilatory support, and muscle strength as key features. Error analysis identified a subgroup of patients with extreme ventilatory demand and muscle weakness who contributed to misclassification; excluding this subgroup improved AUROC from 0.792 to 0.827. We developed a DNR prediction model and demonstrated the feasibility of integrating an explainable model into ICU care for a nudge to consider the DNR-relevant issue.