Factors Associated With Withdrawal of Life-Sustaining Therapy After Out-of-Hospital Cardiac Arrest
Bibliographic record
Abstract
Background: Out-of-hospital cardiac arrest (OHCA) is a leading cause of global mortality. Most patients get hypoxic brain injury, which often leads to the withdrawal of life-sustaining therapy (WLST) because of concerns of poor neurologic prognosis. This study describes the rates and reasons for WLST and identifies factors associated with early WLST, defined as occurring within 72 hours of admission. Methods: We conducted a multicentered, retrospective cohort study of adult OHCA patients admitted to 3 large academic hospitals in Toronto from January 2012 to December 2019. Data were extracted from medical records and analyzed using descriptive statistics and cause-specific hazards regression models to identify factors associated with WLST and documented goals of care (GOC) discussions. Results: Among 264 patients (median age 66 years, 76.5% male), the in-hospital mortality rate was 62.1%. Of the nonsurvivors, 67.1% died following WLST (90% of cases because of concern of poor neurologic prognosis), with 50% of WLST occurring <72 hours from admission. Formal declaration of brain death only occurred 9.8% of the time. Older age significantly increased the risk of early WLST. GOC discussions were documented only 56.4% of the time in the overall cohort and significantly associated with WLST across all time periods. Conclusions: This study highlights the high incidence of WLST, and specifically early WLST, in OHCA patients. GOC discussions are routinely undocumented and is associated with a higher likelihood of WLST. These findings underscore heterogeneity of practice, and the influence of GOC discussions in education and shared decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".