Effect of a Quality-Improvement Intervention on End-of-Life Care in the Intensive Care Unit: A Randomized Trial
Bibliographic record
Abstract
RATIONALE: Because of high mortality, end-of-life care is an important component of intensive care. OBJECTIVES: We evaluated the effectiveness of a quality-improvement intervention to improve intensive care unit (ICU) end-of-life care. METHODS: We conducted a cluster-randomized trial randomizing 12 hospitals. The intervention targeted clinicians with five components: clinician education, local champions, academic detailing, clinician feedback of quality data, and system supports. Outcomes were assessed for patients dying in the ICU or within 30 hours of ICU discharge using surveys and medical record review. Families completed Quality of Dying and Death (QODD) and satisfaction surveys. Nurses completed the QODD. Data were collected during baseline and follow-up at each hospital (May 2004 to February 2008). We used robust regression models to test for intervention effects, controlling for site, patient, family, and nurse characteristics. MEASUREMENTS AND MAIN RESULTS: All hospitals completed the trial with 2,318 eligible patients and target sample sizes obtained for family and nurse surveys. The primary outcome, family-QODD, showed no change with the intervention (P = 0.33). There was no change in family satisfaction (P = 0.66) or nurse-QODD (P = 0.81). There was a nonsignificant increase in ICU days before death after the intervention (hazard ratio = 0.9; P = 0.07). Among patients undergoing withdrawal of mechanical ventilation, there was no change in time from admission to withdrawal (hazard ratio = 1.0; P = 0.81). CONCLUSIONS: We found this intervention was associated with no improvement in quality of dying and no change in ICU length of stay before death or time from ICU admission to withdrawal of life-sustaining measures. Improving ICU end-of-life care will require interventions with more direct contact with patients and families. Clinical trial registered with www.clinicaltrials.gov (NCT00685893).
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".