ReviewCritical care explorations2022
Predicting Time to Death After Withdrawal of Life-Sustaining Treatment in Children.
Review in Critical care explorations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Education and Ethical Guidance for Organ Donation at the End-of-Life: An Ethical Guidance Statement from the Neurocritical Care Society.Neurocritical care · 2026Review
- Practice of pediatric palliative extubation in Brazil: a case series.Critical care science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
conclusionsThere 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.