Biomarkers for Predicting Serious Cardiac Outcomes at 72 Hours in Patients Presenting Early after Chest Pain Onset with Symptoms of Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: Most outcome studies of patients presenting early to the emergency department with potential acute coronary syndromes have focused on either the index diagnosis of myocardial infarction (MI) or a composite end point at a later time frame (30 days or 1 year). We investigated the performance of 9 biomarkers for an early serious outcome. METHODS: Patients (n=186) who presented to the emergency department within 6 h of chest pain onset had their presentation serum sample measured for the following analytes: creatine kinase, creatine kinase isoenzyme MB, enhanced AccuTnI troponin I (Beckman Coulter), high-sensitivity cardiac troponin T (hs-cTnT), ischemia-modified albumin, interleukin-6, investigation use only hs-cTnI (Beckman Coulter), N-terminal pro-B-type natriuretic peptide, and cardiac troponin I (Abbott AxSym). We followed patients until 72 h after presentation and determined whether they experienced the following serious cardiac outcomes: MI, heart failure, serious arrhythmia, refractory ischemic cardiac pain, or death. ROC curves were analyzed to determine the area under the ROC curve (AUC) and optimal cutoffs for the biomarkers. RESULTS: The AUCs for the hs-cTnI assay (0.86; 95% CI, 0.76-0.96), the AccuTnI assay (0.86; 95% CI, 0.78-0.95), and the hs-cTnT assay (0.82; 95% CI, 0.71-0.94) assays were significantly higher than those for the other 6 assays (AUC values≤0.71 for the rest of the biomarkers, P<0.05). The ROC curve-derived optimal cutoffs were ≥19 ng/L (diagnostic sensitivity, 80%; specificity, 88%), ≥0.018 μg/L (diagnostic sensitivity, 75%; specificity, 86%), and ≥32 ng/L (diagnostic sensitivity, 68%; specificity, 92%) for the hs-cTnI, AccuTnI, and hs-cTnT assays, respectively. CONCLUSIONS: The optimal cutoffs for predicting serious cardiac outcomes in this low-risk population are different from the published 99th percentiles. Larger studies are required to verify these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".