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 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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".