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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".