Do Intensivist Staffing Patterns Influence Hospital Mortality Following ICU Admission? A Systematic Review and Meta-Analyses*
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of different intensivist staffing models on clinical outcomes for critically ill patients. DATA SOURCES: A sensitive search of electronic databases and hand-search of major critical care journals and conference proceedings was completed in October 2012. STUDY SELECTION: Comparative observational studies examining intensivist staffing patterns and reporting hospital or ICU mortality were included. DATA EXTRACTION: Of 16,774 citations, 52 studies met the inclusion criteria. We used random-effects meta-analytic models unadjusted for case-mix or cluster effects and quantified between-study heterogeneity using I. Study quality was assessed using the Newcastle-Ottawa Score for cohort studies. DATA SYNTHESIS: High-intensity staffing (i.e., transfer of care to an intensivist-led team or mandatory consultation of an intensivist), compared to low-intensity staffing, was associated with lower hospital mortality (risk ratio, 0.83; 95% CI, 0.70-0.99) and ICU mortality (pooled risk ratio, 0.81; 95% CI, 0.68-0.96). Significant reductions in hospital and ICU length of stay were seen (-0.17 d, 95% CI, -0.31 to -0.03 d and -0.38 d, 95% CI, -0.55 to -0.20 d, respectively). Within high-intensity staffing models, 24-hour in-hospital intensivist coverage, compared to daytime only coverage, did not improved hospital or ICU mortality (risk ratio, 0.97; 95% CI, 0.89-1.1 and risk ratio, 0.88; 95% CI, 0.70-1.1). The benefit of high-intensity staffing was concentrated in surgical (risk ratio, 0.84; 95% CI, 0.44-1.6) and combined medical-surgical (risk ratio, 0.76; 95% CI, 0.66-0.83) ICUs, as compared to medical (risk ratio, 1.1; 95% CI, 0.83-1.5) ICUs. The effect on hospital mortality varied throughout different decades; pooled risk ratios were 0.74 (95% CI, 0.63-0.87) from 1980 to 1989, 0.96 (95% CI, 0.69-1.3) from 1990 to 1999, 0.70 (95% CI, 0.54-0.90) from 2000 to 2009, and 1.2 (95% CI, 0.84-1.8) from 2010 to 2012. These findings were similar for ICU mortality. CONCLUSIONS: High-intensity staffing is associated with reduced ICU and hospital mortality. Within a high-intensity model, 24-hour in-hospital intensivist coverage did not reduce hospital, or ICU, mortality. Benefits seen in mortality were dependent on the type of ICU and decade of publication.
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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 | 0.025 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".