Development and Evaluation of an Audit and Feedback Process for Prevention of Acute Kidney Injury During Coronary Angiography and Intervention
Bibliographic record
Abstract
Background: Contrast-associated acute kidney injury (CA-AKI) is a potentially preventable complication of coronary angiography and intervention. Relatively little research has been done to determine how knowledge on CA-AKI prevention can be translated into clinical practice. Methods: We developed, implemented, and surveyed end-users about the usability, acceptability, and utility of an audit and feedback process for CA-AKI prevention in Alberta, Canada. The audit and feedback reported on amount of radiocontrast dye used, hemodynamic optimization of intravenous fluids, and CA-AKI incidence for each cardiologist practicing coronary angiography or percutaneous coronary intervention, compared with peers at their site and across the province. Reports were developed through an iterative process involving interventional cardiologists throughout the design process and usability testing. Results: Cardiologists participating in usability testing indicated a preference for the visual displays of data and summarizing indicators on the front page, and endorsed the value of peer-to-peer comparisons of performance measures. Of 31 eligible cardiologists from across Alberta, 17 responded to a survey evaluating the audit and feedback process. Fifteen respondents (88.2%) agreed that the data presented in the audit and feedback report were understandable; 17 respondents (100%) agreed or strongly agreed that the presentation of the report helped them better understand their performance compared with that of their peers; and 14 (82.4%) agreed that the audit and feedback process helped them identify ways to reduce the risk of AKI for their patients. Conclusions: Conducting an audit and providing feedback was an understandable and acceptable intervention to help cardiologists identify ways to improve prevention of CA-AKI during coronary angiography or intervention.
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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 | 0.160 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".