Evidence map›Paper›PMID 25944573›Full record

ReviewIntensive care medicine2015

Predicting time to death after withdrawal of life-sustaining therapy.

Laveena Munshi, Sonny Dhanani, Sam D Shemie, Laura Hornby, Genevieve Gore, Jason Shahin

Abstract readReview
PubMed Publisher
In one paragraph

Review in Intensive care medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  13. Focus on palliative care in the ICU.Intensive care medicine · 2017
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Laveena MunshiInterdepartmental Division of Critical Care Medicine, and Department of Medicine, University of Toronto, University Health Network and Mount Sinai Hospital, Toronto, Canada.
Sonny Dhanani
Sam D Shemie
Laura Hornby
Genevieve Gore
Jason Shahin

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePredicting time to death following the withdrawal of life-sustaining therapy is difficult. Accurate predictions may better prepare families and improve the process of donation after circulatory death.

methodsWe systematically reviewed any predictive factors for time to death after withdrawal of life support therapy.

resultsFifteen observational studies met our inclusion criteria. The primary outcome was time to death, which was evaluated to be within 60 min in the majority of studies (13/15). Additional time endpoints evaluated included time to death within 30, 120 min, and 10 h, respectively. While most studies evaluated risk factors associated with time to death, a few derived or validated prediction tools. Consistent predictors of time to death that were identified in five or more studies included the following risk factors: controlled ventilation, oxygenation, vasopressor use, Glasgow Coma Scale/Score, and brain stem reflexes. Seven unique prediction tools were derived, validated, or both across some of the studies. These tools, at best, had only moderate sensitivity to predicting the time to death. Simultaneous withdrawal of all support and physician opinion were only evaluated in more recent studies and demonstrated promising predictor capabilities.

conclusionsWhile the risk factors controlled ventilation, oxygenation, vasopressors, level of consciousness, and brainstem reflexes have been most consistently found to be associated with time to death, the addition of novel predictors, such as physician opinion and simultaneous withdrawal of all support, warrant further investigation. The currently existing prediction tools are not highly sensitive. A more accurate and generalizable tool is needed to inform end-of-life care and enhance the predictions of donation after circulatory death eligibility.

Indexed as

DeathLife Support CareWithholding TreatmentForecastingHumansTime Factors

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.