Clinical Decision Support to Reduce Contrast-Induced Kidney Injury During Cardiac Catheterization: Design of a Randomized Stepped-Wedge Trial
Bibliographic record
Abstract
BACKGROUND: Contrast-induced acute kidney injury (CI-AKI) is a common and serious complication of invasive cardiac procedures. Quality improvement programs have been associated with a lower incidence of CI-AKI over time, but there is a lack of high-quality evidence on clinical decision support for prevention of CI-AKI and its impact on processes of care and clinical outcomes. METHODS: The Contrast-Reducing Injury Sustained by Kidneys (Contrast RISK) study will implement an evidence-based multifaceted intervention designed to reduce the incidence of CI-AKI, encompassing automated identification of patients at increased risk for CI-AKI, point-of-care information on safe contrast volume targets, personalized recommendations for hemodynamic optimization of intravenous fluids, and follow-up information for patients at risk. Implementation will use cardiologist academic detailing, computerized clinical decision support, and audit and feedback. All 31 physicians practicing in all 3 of Alberta's cardiac catheterization laboratories will participate using a cluster-randomized stepped-wedge design. The order in which they are introduced to this intervention will be randomized within 8 clusters. The primary outcome is CI-AKI incidence, with secondary outcomes of CI-AKI avoidance strategies and downstream adverse major kidney and cardiovascular events. An economic evaluation will accompany the main trial. CONCLUSIONS: The Contrast RISK study leverages information technology systems to identify patient risk combined with evidence-based protocols, audit, and feedback to reduce CI-AKI in cardiac catheterization laboratories across Alberta. If effective, this intervention can be broadly scaled and sustained to improve the safety of cardiac catheterization.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".