Communication tools for end-of-life decision-making in the intensive care unit: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: For many patients admitted to the intensive care unit (ICU), preferences for end-of-life care are unknown, and clinicians and substitute decision-makers are required to make decisions about the goals of care on their behalf. We conducted a systematic review to determine the effect of structured communication tools for end-of-life decision-making, compared to usual care, upon the number of documented goals of care discussions, documented code status, and decisions to withdraw life-sustaining treatments, in adult patients admitted to the ICU. METHODS: We searched multiple databases including MEDLINE, Embase, CINAHL, ERIC, and Cochrane from database inception until July 2014. Two reviewers independently screened articles, assessed eligibility, verified data extraction, and assessed risk of bias using the tool described by the Cochrane Collaboration and the Newcastle Ottawa Scale. Pooled estimates of effect (relative risk, standardized mean difference, or mean difference), were calculated where sufficient data existed. GRADE was used to evaluate the overall quality of evidence for each outcome. RESULTS: We screened 5785 abstracts and reviewed the full text of 424 articles, finding 168 eligible articles, including 19 studies in the ICU setting. The use of communication tools increased documentation of goals-of-care discussions (RR 3.47, 95% CI 1.55, 7.75, p = 0.020, very low-quality evidence), but did not have an effect on code status documentation (RR 1.03, 95% CI 0.96, 1.10, p = 0.540, low-quality evidence) or decisions to withdraw or withhold life-sustaining treatments (RR 0.98, 95% CI 0.89, 1.08, p = 0.70, low-quality evidence). The use of such tools was associated with a decrease in multiple measures of health care resource utilization, including duration of mechanical ventilation (MD -1.9 days, 95% CI -3.26, -0.54, p = 0.006, very low-quality evidence), length of ICU stay (MD -1.11 days, 95% CI -2.18, -0.03, p = 0.04, very low-quality evidence), and health care costs (SMD -0.32, 95% CI -0.5, -0.15, p < 0.001, very low-quality evidence). CONCLUSIONS: Structured communication tools may improve documentation of EOL decision making and may result in lower resource use. The supporting evidence is low to very low in quality. Further high-quality randomized studies of simple communication interventions are needed to determine whether structured, rather than ad hoc, approaches to end-of-life decision-making improve patient-level, family-level, and system-level outcomes. TRIAL REGISTRATION: PROSPERO CRD42014012913.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".