Multicentre implementation of a quality improvement initiative to reduce delirium in adult intensive care units: An interrupted time series analysis
Bibliographic record
Abstract
PURPOSE: The ABCDEF bundle may improve delirium outcomes among intensive care unit (ICU) patients, however population-based studies are lacking. In this study we evaluated effects of a quality improvement initiative based on the ABCDEF bundle in adult ICUs in Alberta, Canada. MATERIAL AND METHODS: We conducted a pre-post, registry-based clinical trial, analysed using interrupted time series methodology. Outcomes were examined via segmented linear regression using mixed effects models. The main data source was a population-based electronic health record. RESULTS: 44,405 consecutive admissions (38,400 unique patients) admitted to 15 general medical/surgical and/or neurologic adult ICUs between 2014 and 2019 were included. The proportion of delirium days per ICU increased from 30.24% to 35.31% during the pre-intervention period. After intervention implementation it decreased significantly (bimonthly decrease of 0.34%, 95%CI 0.18-0.50%, p < 0.01) from 33.48% (95%CI 29.64-37.31%) in 2017 to 28.74% (95%CI 25.22-32.26%) in 2019. The proportion of sedation days using midazolam demonstrated an immediate decrease of 7.58% (95%CI 4.00-11.16%). There were no significant changes in duration of invasive ventilation, proportion of partial coma days, ICU mortality, or potential adverse events. CONCLUSIONS: An ABCDEF delirium initiative was implemented on a population-basis within adult ICUs and was successful at reducing the prevalence of delirium.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".