The Impact of Palliative Care Consultation in the ICU on Length of Stay
Bibliographic record
Abstract
INTRODUCTION: The intensive care unit (ICU) consumes 20% of hospital expenditures and 1% of gross domestic product. Many strategies have been attempted to reduce ICU costs. A systematic review was conducted to evaluate the effect of palliative care (PC) consultations in the ICU on length of stay (LOS) and costs. METHODS: A literature search was performed using PubMed, MEDLINE, EMBASE, and the Cochrane Library. Randomized controlled trials (RCTs), prospective, and retrospective cohort studies looking at PC consultations in adult ICUs published between January 2000 and February 2016 were selected. Independent reviewers assessed the eligibility of studies, extracted data on ICU, hospital LOS, and mortality, and rated each study's quality. The cost was derived from an existing model in the literature; the primary outcome was ICU LOS and the secondary outcomes were direct variable costs, mortality, and hospital LOS. RESULTS: We reviewed 814 abstracts, but only 8 studies met inclusion criteria and were included. The patients with a PC consultation in the ICU, when compared to those who did not, showed a trend toward reduced LOS. This reduction was statistically significant in the higher quality studies. Mortality was similar in both groups. Palliative care consultations also lead to a reduction in costs in 5 of the 8 eligible trials. On average, ICU costs were USD7533 and USD6406 (control vs PC, P < .05) and hospital direct variable costs were USD9518 and USD8971 ( P < .05) per admission. Due to interstudy heterogeneity, all outcomes were described narratively. CONCLUSION: This review demonstrates a trend that PC consultations reduce LOS and costs without impacting mortality. However, due to the small sample sizes and varying degrees of quality of evidence, many questions remain. A large multicenter RCT and formal economic evaluation would be needed for more definitive results.
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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 | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".