Predicting Time to Death After Withdrawal of Life-Sustaining Treatment in Children

Predicting Time to Death After Withdrawal of Life-Sustaining Treatment in Children

Winter MC; Ledbetter DR

Critical Care Explorations

2022

Decision Support Techniques; Intensive Care Unit; Machine Learning; Pediatric; Terminal Care; Tissue and Organ Procurement

Accurately predicting time to death after withdrawal of life-sustaining treatment is valuable for family counseling and for identifying candidates for organ donation after cardiac death. This topic has been well studied in adults, but literature is scant in pediatrics. The purpose of this report is to assess the performance and clinical utility of the available tools for predicting time to death after treatment withdrawal in children. DATA SOURCES: Terms related to predicting time to death after treatment withdrawal were searched in PubMed and Embase from 1993 to November 2021. STUDY SELECTION: Studies endeavoring to predict time to death or describe factors related to time to death were included. Articles focusing on perceptions or practices of treatment withdrawal were excluded. DATA EXTRACTION: Titles, abstracts, and full text of articles were screened to determine eligibility. Data extraction was performed manually. Two-by-two tables were reconstructed with available data from each article to compare performance metrics head to head. DATA SYNTHESIS: Three hundred eighteen citations were identified from the initial search, resulting in 22 studies that were retained for full-text review. Among the pediatric studies, predictive models were developed using multiple logistic regression, Cox proportional hazards, and an advanced machine learning algorithm. In each of the original model derivation studies, the models demonstrated a classification accuracy ranging from 75% to 91% and positive predictive value ranging from 0.76 to 0.93. CONCLUSIONS: There are few tools to predict time to death after withdrawal of life-sustaining treatment in children. They are limited by small numbers and incomplete validation. Future work includes utilization of advanced machine learning models.

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