Lot-to-Lot Variation for Commercial High-Sensitivity Cardiac Troponin: Can We Realistically Report Down to the Assay’s Limit of Detection?
Bibliographic record
Abstract
The release of high-sensitivity cardiac troponin T (hs-cTnT) and troponin I (hs-cTnI) assays in the US and worldwide has resulted in changes to the triage strategy for patients who present to the emergency department with ischemic symptoms suggestive of acute coronary syndrome including chest pain. For rule-out of acute myocardial infarction (MI), many emergency departments have been able to reduce the frequency of blood collection from the time of presentation plus 3–6 hours down to presentation plus 1–3 hours while maintaining a high negative predictive value ≥99.5%) (1,). Investigators have also shown that if the time from onset of symptoms to acute MI is 1–2 hours, very low cardiac troponin (cTn) concentrations can be used to rule out acute MI with a single admission sample (2–4,). If the patient presents earlier than 1–2 hours, this strategy may not be optimal (5). To fully implement a single-sample rule-out protocol, clinical laboratories must be able to report low cTn concentrations. Although laboratories in countries outside the US are able to report values down to the assay limit of detection (LoD), the US Food and Drug Administration (FDA) allows clinical laboratories to report cTn values down to only the assay’s limit of quantification (LoQ), which is conventionally defined as the lowest concentration with a 20% CV and is usually higher than the LoD. One of the FDA’s major concerns is assay variability. Inadequate performance between reagent lots has been documented for other widely used immunoassays. For cTn specifically, recall notices have been posted by the FDA for the Biosite Triage Assay, Beckman Access, and Roche cTnI (see Supplemental Data for additional references). An editorial from the FDA implies that variability of results between lots at low hs-cTn concentrations may give a false impression of low patient risk (6,). Data have been reported that this could lead to inappropriate baseline rule-out of MI, followed by emergency department discharge (7).
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 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 | 0.347 | 0.442 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".