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 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 | 0.029 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.047 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".