Quality of end-of-life communication in 2 high-risk ICU cohorts: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Factors influencing the quality of end-of-life communication are relevant to improving end-of-life care. We assessed the quality of end-of-life communication and influencing factors in 2 intensive care unit (ICU) cohorts at high risk of death: patients living in nursing homes and those on extracorporeal membrane oxygenation (ECMO). METHODS: This retrospective cohort study included admissions to 4 ICUs in Winnipeg, Manitoba, from 2000 to 2017. We identified cohorts and influencing factors from the Winnipeg ICU database and by manual chart review. We assessed quality of end-of-life communication using 18 validated, binary quality indicators to calculate a weighted, scaled, composite score (range 0-100). We used median regression to identify factors associated with the composite score. RESULTS: = 230), with longer hospital stays and higher disease severity. Mean composite scores of end-of-life communication were extremely low in both cohorts (mean 48.5 [standard error of the mean (SEM) 1.7] for the nursing home cohort, 49.1 [SEM 2.5] for the ECMO cohort). Patient characteristics associated with higher median composite scores were older age (5.0 per decade, 95% confidence interval [CI] 2.1-7.8) and lower (worse) Glasgow Coma Scale (GCS) scores (1.8 per GCS point, 95% CI 0.5-3.2). The median composite score rose significantly over time (1.7 per year, 95% CI 0.5-2.8). INTERPRETATION: The quality of end-of-life communication in ICUs is poor, and factors associated with better prognosis are also associated with worse communication. Direct and early communication should occur with all patients in the ICU and their surrogates, not just those who are believed most likely to die.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".