The Prognostic Value of Serial Troponin Measurements in Patients Admitted for COVID-19
Bibliographic record
Abstract
AIMS: Myocardial injury (MI) in coronavirus disease-19 (COVID-19) is quite prevalent at admission and affects prognosis. Little is known about troponin trajectories and their prognostic role. We aimed to describe the early in-hospital evolution of MI and its prognostic impact. METHODS AND RESULTS: We performed an analysis from an Italian multicentre study enrolling COVID-19 patients, hospitalized from 1 March to 9 April 2020. MI was defined as increased troponin level. The first troponin was tested within 24 h from admission, the second one between 24 and 48 h. Elevated troponin was defined as values above the 99th percentile of normal values. Patients were divided in four groups: normal, normal then elevated, elevated then normal, and elevated. The outcome was in-hospital death. The study population included 197 patients; 41% had normal troponin at both evaluations, 44% had elevated troponin at both assessments, 8% had normal then elevated troponin, and 7% had elevated then normal troponin. During hospitalization, 49 (25%) patients died. Patients with incident MI, with persistent MI, and with MI only at admission had a higher risk of death compared with those with normal troponin at both evaluations (P < 0.001). At multivariable analysis, patients with normal troponin at admission and MI injury on Day 2 had the highest mortality risk (hazard ratio 3.78, 95% confidence interval 1.10-13.09, P = 0.035). CONCLUSIONS: In patients admitted for COVID-19, re-test MI on Day 2 provides a prognostic value. A non-negligible proportion of patients with incident MI on Day 2 is identified at high risk of death only by the second measurement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".