Analytical Error of Cardiac Troponin and Impact on the Performance of Accelerated Diagnostic Protocols in Patients with Acute Chest Pain
Bibliographic record
Abstract
BACKGROUND: This study simulates how assay-specific bias influences the diagnostic performance of 0/1-h accelerated diagnostic protocols (ADPs) for 3 different high-sensitivity cardiac troponin (hs-cTn) assays. METHODS: We included 1493 patients presenting with chest pain. hs-cTnT (Roche Diagnostics), hs-cTnI from Abbott Diagnostics (hs-cTnI-A), and Siemens Healthineers (hs-cTnI-S) were measured at admission. The absolute total error observed in a state-of-the-art EQA study were added to the admission concentrations, producing 6 new variables being adjusted for maximum possible bias (if analytical variation is 0) (+biasmean, +biasmax95%CI, +biasmin95%CI, -biasmean, -biasmax95%CI, -biasmin95%CI). The influence of this "worst-case scenario" bias was compared after calculating sensitivity, specificity, negative and positive predictive values, and rule-out proportion for 30-day myocardial infarction or death for the observed and bias-adjusted hs-cTn concentrations. RESULTS: For 0-h rule-out, hs-cTnI-S and hs-cTnT had a sensitivity of >99.0%, compared to 97.7% for hs-cTnI-A. After adding the bias, sensitivity was unchanged for hs-cTnI-S (99.5%), but lower for hs-cTnT (95.5%), and hs-cTnI-A (96.2%). For the 0-/1-h algorithm, adding bias reduced sensitivity to 95.5% for hs-cTnT, while both hs-cTnI algorithms were unchanged (100.0%). Rule-out proportions for 0 h ranged from 0% to 60.0% for hs-cTnT, 28.2%-62.7% for hs-cTnI-A, and 3.5%-35.5% for hs-cTnI-S. For the 0-/1-h algorithm, ranges were 57.7%-75.8% (hs-cTnT), 52.8%-67.5% (hs-cTnI-A), and 45.7%-61.2% (hs-cTnI-S). CONCLUSION: Analytical bias of hs-cTn assays affects the clinical rule-out rate of the 0/1-h ADPs more than the diagnostic sensitivity. Bias may have a greater influence on the proportion of patients requiring hospital admission and may contribute to the heterogeneity of the reported rule-out rates of current ADPs. ClinicalTrials.gov Registration Number: NCT02620202.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".