Hospital re‐admission after critical care survival: a systematic review and meta‐analysis
Why this work is in the frame
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Bibliographic record
Abstract
Survivors of critical illness frequently require increased healthcare resources after hospital discharge. We undertook a systematic review and meta-analysis to assess hospital re-admission rates following critical care admission and to explore potential re-admission risk factors. We searched the MEDLINE, Embase and CINAHL databases on 05 March 2020. Our search strategy incorporated controlled vocabulary and text words for hospital re-admission and critical illness, limited to the English language. Two reviewers independently applied eligibility criteria and assessed quality using the Newcastle Ottawa Score checklist and extracted data. The primary outcome was acute hospital re-admission in the year after critical care discharge. Of the 8851 studies screened, 87 met inclusion criteria and 41 were used within the meta-analysis. The analysis incorporated data from 3,897,597 patients and 741,664 re-admission episodes. Pooled estimates for hospital re-admission after critical illness were 16.9% (95%CI: 13.3-21.2%) at 30 days; 31.0% (95%CI: 24.3-38.6%) at 90 days; 29.6% (95%CI: 24.5-35.2%) at six months; and 53.3% (95%CI: 44.4-62.0%) at 12 months. Significant heterogeneity was observed across included studies. Three risk factors were associated with excess acute care rehospitalisation one year after discharge: the presence of comorbidities; events during initial hospitalisation (e.g. the presence of delirium and duration of mechanical ventilation); and subsequent infection after hospital discharge. Hospital re-admission is common in survivors of critical illness. Careful attention to the management of pre-existing comorbidities during transitions of care may help reduce healthcare utilisation after critical care discharge. Future research should determine if targeted interventions for at-risk critical care survivors can reduce the risk of subsequent rehospitalisation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| 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 | 0.001 |
| 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 it