ALARRM: A laboratory approach for rapid risk-assessment for myocardial infarction
Bibliographic record
Abstract
BACKGROUND: Current pathways using high-sensitivity cardiac troponin (hs-cTn) to rule in and rule out myocardial infarction (MI) are assay specific. This requires clinicians and laboratories to use the correct assay-specific cutoffs, deltas, and time between measurements for optimal decision making. To overcome these challenges, we developed a new common laboratory pathway (i.e., ALARRM: a laboratory approach for rapid risk-assessment for MI) to aid in early risk stratification for MI in the emergency department (ED) using the combined validated clinical chemistry score (CCS) and common change criteria (3C) algorithm. The objective of our study was to assess the diagnostic performance (sensitivity and specificity) and effectiveness (combination of rule out/low risk and rule in/high risk) of ALARRM. METHODS: The study cohort (n = 855) consisted of patients presenting to the ED with acute coronary syndrome symptoms and had two blood samples collected three hours apart for measurement of Abbott hs-cTnI, Ortho hs-cTnI, Roche hs-cTnT, glucose, and creatinine (for the estimated glomerular filtration rate (eGFR) calculation). We also assessed the European society of cardiology (ESC) single sample assay-specific cutoffs with both ALARRM and ESC criteria being assessed for index MI in the cohort. RESULTS: The sensitivity for MI using ALARRM was 100 % for all three assays, however this was not the case for the ESC single cutoffs where the Ortho hs-cTnI assay missed 4 MIs. The specificities in patients with serial measurements with ALARRM were > 90 %, with the overall effectiveness for ALARRM being 61 % (95 %CI: 58 to 64) for Roche, 80 % (95 %CI: 77 to 82) for Abbott, and 89 % (95 %CI: 86 to 91) for Ortho. CONCLUSION: The ALARRM pathway represents a highly efficacious and common approach using hs-cTn for early risk stratification for MI.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".