Cardiac Troponin-I and COVID-19: A Prognostic Tool for In-Hospital Mortality
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Bibliographic record
Abstract
BACKGROUND: The number of fatalities due to coronavirus disease 2019 (COVID-19) is escalating with more than 800,000 deaths globally. The scientific community remains in urgent need of prognostic tools to determine the probability of survival in patients with COVID-19 and to determine the need for hospitalization. METHODS: This is a retrospective cohort study of patients with a diagnosis of COVID-19 admitted to a tertiary center between March 2020 and July 2020. Patients age 18 years and older were stratified into two groups based on their troponin-I level in the first 24 h of admission (groups: elevated vs. normal). The aim of the study is to explore the utility of cardiac troponin-I level for early prognostication of patients with COVID-19. RESULTS: This cohort of 257 patients included 122/257 (47%) women with a mean age of 63 ± 17 years. Patients with an elevated troponin-I level were more likely to be older (77 ± 13 vs. 58 ± 16 years, P < 0.0001), have a history of hypertension (P < 0.0001), diabetes mellitus (P = 0.0019), atrial fibrillation or flutter (P = 0.0009), coronary artery disease (P < 0.0001), and chronic heart failure (P = 0.0011). Patients with an elevated troponin-I level in the first 24 h of admission were more likely to have higher in-hospital mortality (52% vs. 10%, P < 0.0001). Troponin-I level in the first 24 h of admission had a negative predictive value of 89.7% and a positive predictive value of 51.9% for all-cause in-hospital mortality. CONCLUSIONS: Troponin-I elevation is commonly seen in patients with COVID-19 and is significantly associated with fatal outcomes. However, a normal troponin-I level in the first 24 h of admission had a high negative predictive value for all-cause in-hospital mortality, thereby predicting favorable survival at the time of discharge.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it