Diagnostic Utility of Creatine Kinase in Patients Presenting to the Emergency Department with Chest Pain
Bibliographic record
Abstract
Introduction: Creatine Kinase (CK) is routinely performed in some emergency departments (ED) for Non-ST-elevation myocardial infarction (NSTEMI) workup. Its diagnostic utility is not well understood. The objectives of this study were to assess the value of CK in NSTEMI diagnosis in the troponin era and the association between the highest CK/Troponin values and ejection fraction (EF) during NSTEMI follow-up. Methods: A prospective cohort study conducted at the two EDs of The Ottawa Hospital from March 2014 to March 2016 enrolled adults (≥18 years) for whom troponin I (TnI) and CK were ordered for NSTEMI symptoms. We excluded those with ST-Elevation Myocardial Infarction (STEMI). The primary outcome was a NSTEMI within 30 days. We used descriptive statistics and report test diagnostic characteristics with 95% confidence intervals (CI). We compared the highest median CK/Troponin values using Wilcoxon test. Results: Of the 2,153 patients enrolled, 99 (4.6%) suffered a NSTEMI. The sensitivity and specificity were: CK (cutoff >250U/L) 31.3% (95%CI 22.2, 40.5) and 91.1% (95%CI 89.9, 92.4) respectively; TnI (cutoff >0.045µg/L) 98.0% (95%CI 95.2, 100) and 86.2% (95%CI 84.7, 87.7) respectively. The median CK values were not significantly different between those with normal (n=267) and abnormal EF (n=55) on follow-up (107 U/L and 118 U/L respectively; p=0.31), whereas the median TnI values were significantly different (0.02 µg/L and 0.1 µg/L respectively; p<0.0001). Conclusions: CK measurements do not provide any value in the ED work-up of NSTEMI and is not associated with EF on follow-up. Discontinuing routine CK measurements would improve resource utilization.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".