Reducing redundant creatine kinase testing in cardiac injury
Bibliographic record
Abstract
BACKGROUND: Creatine kinase (CK) testing in the setting of suspected cardiac injury is commonly performed yet rarely provides clinical value beyond troponin testing. We sought to evaluate and reduce CK testing coupled with troponin testing by 50% or greater. METHODS: We performed root cause analysis to study prevailing processes and patterns of CK testing. We developed new institutional guidelines, removed CK from high-volume paper and electronic order bundles and conducted academic detailing for departments with highest ordering frequency. We evaluated consecutive patients at Sunnybrook Health Sciences Centre between 1 January 2018 and 31 March 2020 who had either a CK or troponin level measured. We prespecified successful implementation as a reduction of 50% in total CK orders and a decrease in the ratio of CK-to-troponin tests to one-third or less. We retained additional data beyond our study period to assess for sustained reductions in testing. RESULTS: Total CK tests decreased over the study period from 3963 to 2111 per month, amounting to a 46.7% reduction (95% CI 33.2 to 60.2; p<0.001) equalling 61 fewer tests per hospital day. Troponin testing did not significantly change during the intervention. Ratio of CK-to-troponin tests decreased from 0.91 to 0.49 (p<0.001). The reduction coincided with changes to order-sets, was observed across all clinical units and was sustained during additional months beyond the study period. These reductions in testing resulted in a projected annual cost savings of C$28 446. CONCLUSIONS: We demonstrate that a low-cost and feasible quality improvement initiative may lead to significant reduction in unnecessary CK testing and substantial savings in healthcare costs for patients with suspected cardiac injury.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".