A Multicenter Assessment of the Sensitivity and Specificity for a Single High-Sensitivity Cardiac Troponin Test at Emergency Department Presentation for Hospital Admission
Bibliographic record
Abstract
BACKGROUND: Studies have illustrated how a low or undetectable high-sensitivity cardiac troponin (hs-cTn) concentration at emergency department (ED) presentation can rule out myocardial infarction (MI). A problem with using an undetectable hs-cTn cutoff is that this value may be defined differently among hospitals and is also difficult to monitor. In the present study, we assess the diagnostic performance of a clinical chemistry score (CCS) vs hs-cTn alone in the presentation blood sample in the ED for patient hospital admission in a multicenter setting. METHODS: From January 1 to June 30, 2018, consecutive patients with random glucose, creatinine (for an estimated glomerular filtration rate calculation), and hs-cTnI (Abbott, 2 hospitals, Hamilton, Ontario, n = 10496) or hs-cTnT (Roche, 4 hospitals, Calgary, Alberta, n = 25177) were assessed for hospital admission with the CCS (range of scores, 0-5) or hs-cTn alone. Sensitivity, specificity, predicative values, and likelihood ratios were calculated for a CCS of 0 and 5 and for hs-cTn alone (hs-cTnI cutoffs, 5 and 26 ng/L; hs-cTnT cutoffs, 6 and 14 ng/L). RESULTS: The CCS of 0 (CCS <1) identified approximately 10% of all patients as low risk and had a sensitivity for hospital admission of nearly 98% as compared to <93% when hs-cTnT (<6 ng/L) or hs-cTnI (<5 ng/L) cutoffs alone were used. A CCS ≥5 had a specificity for hospital admission >95%, with approximately 14% of patients at high risk. CONCLUSIONS: An ED disposition (admit or send home) using the presentation blood sample could occur in nearly 25% of all patients by use of the CCS.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".