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

Meredith C Winter1,2, David R Ledbetter3

  • 1Department of Anesthesiology Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA.

Critical Care Explorations
|September 14, 2022
PubMed

Insights

Predicting time to death after withdrawing life-sustaining treatment in children is crucial. Current pediatric tools are limited, necessitating further development, especially using advanced machine learning models.

Area of Science:

  • Pediatric critical care medicine
  • Biostatistics
  • Palliative care

Background:

  • Accurate prediction of time to death after withdrawal of life-sustaining treatment (WLST) is vital for pediatric patient care, family counseling, and organ donation after cardiac death (DCD) identification.
  • While extensively studied in adults, predictive tools for WLST in children remain scarce.

Purpose of the Study:

  • To evaluate the performance and clinical utility of existing tools for predicting time to death in pediatric patients following WLST.
  • To identify gaps in current pediatric research on predicting post-WLST mortality.

Main Methods:

  • A systematic literature search was conducted in PubMed and Embase (1993-November 2021) for studies predicting time to death after WLST in children.
  • Included studies focused on prediction or factors influencing time to death; excluded studies on perceptions or practices of WLST.
  • Data extraction involved manual screening of titles, abstracts, and full texts, with performance metrics compared using two-by-two tables.

Main Results:

  • The search yielded 22 studies for full-text review from 318 initial citations.
  • Pediatric studies utilized various predictive models, including logistic regression, Cox proportional hazards, and machine learning.
  • Models demonstrated classification accuracy between 75%-91% and positive predictive values of 0.76-0.93 in their derivation studies.

Conclusions:

  • Few validated tools exist for predicting time to death in children after WLST.
  • Current pediatric predictive models are limited by small sample sizes and incomplete validation.
  • Future research should focus on developing and validating advanced predictive models, particularly those employing machine learning.

Keywords:
decision support techniquesintensive care unitsmachine learningpediatricterminal caretissue and organ procurement

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