Impact of Rounding Checklists on the Outcomes of Patients Admitted to ICUs: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effectiveness of ICU rounding checklists on outcomes. DATA SOURCES: Five electronic databases (MEDLINE, Embase, CINAHL, Cochrane Library, and Google Scholar) were searched from inception to May 10, 2024. STUDY SELECTION: Cohort studies, case-control studies, and randomized controlled trials comparing the use of rounding checklists to no checklists were included. Other article types were excluded. DATA EXTRACTION: The primary outcome was in-hospital mortality. Secondary outcomes included ICU and 30-day mortality; hospital and ICU length of stay (LOS); duration of mechanical ventilation; and frequency of catheter-associated urinary tract infections, central line-associated bloodstream infections (CLABSI), and ventilator-associated pneumonia. Additional outcomes included healthcare provider perceptions of checklists. DATA SYNTHESIS: Pooled estimates were obtained using an inverse-variance random-effects meta-analysis model. Certainty of evidence was evaluated using Grading of Recommendations Assessment, Development, and Evaluation. There were 30 included studies (including > 32,000 patients) in the review. Using an ICU rounding checklist was associated with reduced in-hospital mortality (risk ratio [RR] 0.80; 95% CI, 0.70-0.92; 12 observational studies; 17,269 patients; I2 = 48%; very low certainty of evidence). The use of an ICU rounding checklist was also associated with reduced ICU mortality (8 observational studies, p = 0.006), 30-day mortality (2 observational studies, p < 0.001), hospital LOS (11 observational studies, p = 0.02), catheter-associated urinary tract infections (CAUTI) (6 observational studies, p = 0.01), and CLABSI (6 observational studies, p = 0.02). Otherwise, there were no significant differences with using ICU rounding checklists on other patient-related outcomes. Healthcare providers' perceptions of checklists were generally positive. CONCLUSIONS: The use of an ICU rounding checklist may improve in-hospital mortality, as well as other important patient-related outcomes. However, well-designed randomized studies are necessary to increase the certainty of evidence and determine which elements should be included in an ICU rounding checklist.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".