Critical Care Transition Programs and the Risk of Readmission or Death After Discharge From an ICU
Bibliographic record
Abstract
OBJECTIVE: To determine whether critical care transition programs reduce the risk of ICU readmission or death, when compared with standard care among adults who survived their incident ICU admission. DATA SOURCES: MEDLINE, EMBASE, CENTRAL, CINAHL, and two clinical trial registries were searched from inception to October 2012. STUDY SELECTION: Studies that examined the effects of critical care transition programs on the risk of ICU readmission or death among patients discharged from ICU were selected for review. A critical care transition program included any rapid response team, medical emergency team, critical care outreach team, or ICU nurse liaison program that provided follow-up for patients discharged from ICU. DATA EXTRACTION: Two reviewers independently extracted data on study characteristics, transition program characteristics, and outcomes (number of ICU readmissions and in-hospital deaths following discharge from ICU). DATA SYNTHESIS: From 3,120 citations, nine before-and-after studies were included. The studies examined medical-surgical populations and described transition programs that were a component of a hospital's outreach team (n = 6) or nurse liaison program (n = 3). Meta-analysis using a fixed-effect model demonstrated a reduced risk of ICU readmission (risk ratio, 0.87 [95% CI, 0.76-0.99]; p = 0.03; I2 = 0%) but no significant reduction in hospital mortality (risk ratio, 0.84 [95% CI, 0.66-1.05]; p = 0.1; I2 = 16%) associated with a critical care transition program. The risk of ICU readmission was similar whether the transition program was included within an outreach team or a nurse liaison program and did not depend on the presence of an intensivist. CONCLUSIONS: Critical care transition programs appear to reduce the risk of ICU readmission in patients discharged from ICU to a general hospital ward. Given methodological limitations of the included before-and-after studies, additional research should confirm these observations and explore the ideal model for these programs before recommending implementation.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".