High-Sensitivity Cardiac Troponin I vs a Clinical Chemistry Score for Predicting All-Cause Mortality in an Emergency Department Population
Bibliographic record
Abstract
Background For patients investigated for suspected acute coronary syndrome, there is uncertainty if a single measurement of high-sensitivity cardiac troponin I (hs-cTnI) at emergency department (ED) presentation can identify patients at both low and high risk for mortality. Methods We included consecutive adult patients in the ED who had a Clinical Chemistry Score (CCS) taken at presentation (ie, combination of glucose, creatinine for estimated glomerular filtration rate determination, and hs-cTnI assay) in a Canadian city between 2012 and 2013. Outcomes were 3-month, 1-year, and 5-year all-cause mortality using the provincial death registry. Mortality rates and test performance (eg, sensitivity and specificity) with 95% confidence intervals (CIs) were obtained for the CCS or hs-cTnI assay alone using established cutoffs for these tests. Results Our cohort included 5974 patients with a 1-year mortality rate of 17.2% (95% CI, 16.2-18.3). A CCS ≥ 1 yielded a sensitivity of 99.2% (95% CI, 98.4-99.6) compared with the hs-cTnI ≥ 5 ng/L cutoff sensitivity of 88.4% (95% CI, 86.3-90.3), with the mortality rate being significantly lower for patients with CCS < 1 (2.0%; 95% CI, 0.9-4.0) vs patients with hs-cTnI < 5 ng/L (5.0%; 95% CI, 4.2-6.0) at 1 year ( P = 0.01). A CCS of 5 also yielded a higher specificity (88.5%; 95% CI, 87.5-89.3) compared with hs-cTnI > 26 ng/L (83.9%; 95% CI, 82.9-84.9), with no difference in mortality rates (37.4% vs 36.3%; P = 0.66). This trend was consistent at 3-month and 5-year mortality. Conclusion For patients in the ED with a potential cardiac issue, using the CCS cutoffs can better identify patients at low and high risk for mortality than using published cutoffs for hs-cTnI alone.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".