Predicting time to asystole following withdrawal of life‐sustaining treatment: a systematic review
Bibliographic record
Abstract
The planned withdrawal of life-sustaining treatment is a common practice in the intensive care unit for patients where ongoing organ support is recognised to be futile. Predicting the time to asystole following withdrawal of life-sustaining treatment is crucial for setting expectations, resource utilisation and identifying patients suitable for organ donation after circulatory death. This systematic review evaluates the literature for variables associated with, and predictive models for, time to asystole in patients managed on intensive care units. We conducted a comprehensive structured search of the MEDLINE and Embase databases. Studies evaluating patients managed on adult intensive care units undergoing withdrawal of life-sustaining treatment with recorded time to asystole were included. Data extraction and PROBAST quality assessment were performed and a narrative summary of the literature was provided. Twenty-three studies (7387 patients) met the inclusion criteria. Variables associated with imminent asystole (<60 min) included: deteriorating oxygenation; absence of corneal reflexes; absence of a cough reflex; blood pressure; use of vasopressors; and use of comfort medications. We identified a total of 20 unique predictive models using a wide range of variables and techniques. Many of these models also underwent secondary validation in further studies or were adapted to develop new models. This review identifies variables associated with time to asystole following withdrawal of life-sustaining treatment and summarises existing predictive models. Although several predictive models have been developed, their generalisability and performance varied. Further research and validation are needed to improve the accuracy and widespread adoption of predictive models for patients managed in intensive care units who may be eligible to donate organs following their diagnosis of death by circulatory criteria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".