Effect of Clinical Decision Support with Audit and Feedback for Prevention of AKI in Coronary Angiography and Intervention: Stepped Wedge Cluster Randomized Trial
Bibliographic record
Abstract
Background: Contrast-associated acute kidney injury (CA-AKI) is a common complication of coronary angiography and percuateous coronary intervention (PCI). We evaluated whether the incidence of CA-AKI was reduced with an intervention including clinical decision support with audit and feedback. Methods: In this cluster-randomized, stepped-wedge trial conducted in Alberta, Canada, we randomly assigned all invasive cardiologists to various start dates for an intervention that included education, point-of-care computerized clinical decision support on contrast volume and IV fluid targets, and repeated audit and feedback related to these processes for CA-AKI prevention. The eligible study population included adults ≥18 years of age, not receiving dialysis, with a predicted risk of CA-AKI >5%, who received non-emergency coronary angiography or PCI. The primary outcome was incidence of AKI based on the KDIGO serum creatinine criteria. Analyses were performed according to the intention-to-treat principle, using mixed-effect models to account for clustering in the data. Results: Of 34 physicians randomized, 3 retired prior to randomization, and the remaining 31 received the intervention. There were 7,087 procedures performed in 6,449 eligible patients; mean (SD) age 70.2 (10.7) years, 2,292 (32.3%) female, mean (SD) eGFR 62.7 (22.4) mL/min/1.73m2. The proportion of procedures where the desired contrast volume limit was exceeded was reduced from 41.0% to 29.2% (p<0.01), while the proportion who received hemodynamically guided IV fluids increased from 35.3% to 42.0% (p<0.01) with the intervention. The incidence of CA-AKI was significantly reduced, from 9.2% (280 events/3,036 procedures) before the intervention to 8.2% (334 events/4,051 procedures) with the intervention (time adjusted odds ratio, 0.74; 95% CI, 0.58 to 0.94). There was no statistical evidence of effect modification by age, sex, comorbidity, or baseline CA-AKI risk. Conclusions: An intervention combining education, clinical decision support, and audit and feedback resulted in less contrast dye use, greater intravenous fluid administration, and reduced the incidence of CA-AKI following coronary angiography and PCI. Funding: Government Support - Non-U.S.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".