Improved risk stratification of patients with acute coronary syndromes using a combination of hsTnT, NT-proBNP and hsCRP with the GRACE score
Bibliographic record
Abstract
BACKGROUND: Clinical scores and biomarkers improve risk stratification of patients with acute coronary syndromes. However, little is known about their value in patients referred for coronary angiography. METHODS: Consecutive patients admitted at four Swiss university hospitals with a diagnosis of acute coronary syndrome were enrolled into the SPUM-ACS Biomarker Cohort between 2009 and 2012. Patients were followed at 30 days and 1 year with assessment of adjudicated events including all-cause mortality and the composite of all-cause mortality or non-fatal recurrent myocardial infarction. RESULTS: Events and biomarkers were analysed in 1892 patients (52.4% with ST-segment elevation myocardial infarction, 43.3% with non-ST-segment elevation myocardial infarction and 4.3% with unstable angina). Death at 30 days occurred in 35 patients (1.9%) and at 1 year in 80 patients (4.3%). The choice of troponin assay (conventional versus high sensitivity) to calculate the Global Registry of Acute Coronary Events (GRACE) score did not affect risk prediction. The prognostic accuracy of the GRACE score was improved when combined with three individual biomarkers including high sensitivity troponin T (hsTnT), N-terminal-pro B-type natriuretic peptide (NT-proBNP) and high sensitivity C-reactive protein (hsCRP) to yield a 9% increment (C-statistic 0.73->0.82) for the discrimination of short-term risk for all-cause mortality. In contrast, the novel biomarkers placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1) and the ratio sFlt-1/PlGF did not improve risk stratification. CONCLUSIONS: In patients with acute coronary syndrome referred for coronary angiography, combinations of biomarkers including hsTnT, NT-proBNP and hsCRP with the GRACE score enhanced risk discrimination. CLINICAL TRIALS REGISTRATION: NCT01000701.
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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 | 0.000 |
| 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".