Authors' objectives: Identify and compare effectiveness of prediction scales and other tools for identifying terminally ill patients who will die within a short period of time following withdrawal of life-sustaining treatment.

Authors' results and conclusions: EVIDENCE SUMMARY ▪ A variety of tools have been developed to predict how soon patients with irreversible brain injury and other terminal conditions will die following withdrawal of life-supporting therapy. These tools are used to select patients who may be good candidates for organ donation after circulatory death. Effective prediction of imminent death is important for making best use of organ donor care and organ recovery resources. ▪ The evidence on these tools is marked by considerable heterogeneity of study designs, prediction tools, and patient population, precluding quantitative data synthesis. Findings may not be generalizable from one population to another. ▪ There is some evidence that the C-DCD nomogram has better predictive value than the points-based Wisconsin and UNOS scales. The GRADE of this evidence is low because of imprecision and indirectness (the reported differences in area under the ROC [receiver operating characteristic] may be driven by differences in performance at clinically irrelevant thresholds). The C-DCD nomogram is a precursor to more recent linear prediction models, which are similar in performance to each other and to the C-DCD. ▪ Artificial intelligence models have recently been introduced for this indication: there is presently too little evidence to evaluate their comparative effectiveness. ▪ As differences in effectiveness across different prediction models are small, factors such as ease of use, local experience, and integration into the electronic medical record may be relevant to decisions of which tool to use. There is no quantitative evidence on the comparative usability of different tools.

Project Status: Completed

Year Published: 2025

URL for published report: https://www.med.upenn.edu/CEP/external-request-form.html

English language abstract: An English language summary is available

Publication Type: Rapid Review

Country: United States

Organisation Name: Penn Medicine Center for Evidence-based Practice

Contact Address: Penn Medicine Center for Evidence-based Practice, University of Pennsylvania Health System, 3600 Civic Center Blvd, 3rd Floor West, Philadelphia PA 19104

Contact Name: Nikhil Mull

Contact Email: cep@pennmedicine.upenn.edu

Copyright: <p>Center for Evidence-based Practice (CEP)</p>