IL-6 and D-dimer Levels at Admission Predict Cardiac Injury and Early Mortality during SARS-CoV-2 Infection
Bibliographic record
Abstract
Background: We previously described the mortality associated with cardiac injury in patients with coronavirus disease 2019 (COVID-19). The activation of immune and thrombotic biomarkers at admission, and their ability to predict cardiac injury and mortality patterns in COVID-19, remains unclear. Methods: This retrospective cohort study included 170 patients with COVID-19 with cardiac injury at the time of admission to Tongji Hospital in Wuhan between January 29, 2020, and March 8, 2020. The temporal evolution of inflammatory cytokines, coagulation markers, clinical treatment, and mortality were analyzed. Continuous variables are expressed as median (interquartile range). The Mann-Whitney test was used for two-group comparisons, whereas the Kruskal-Wallis test was used for comparisons among three groups. Categorical variables are expressed as proportions and percentages, and Fisher’s exact test was used to compare differences. A multivariate regression model was used to predict in-hospital death. A simple linear regression analysis was applied to examine the correlation between baseline biomarkers and peak cTnI levels. Results: Of the 170 patients, 60 (35.3%) died early (<21 d), and 61 (35.9%) died after a prolonged stay. The admission laboratory findings correlating with early death were elevated interleukin 6 (IL-6) (P < 0.0001), tumor necrosis factor-α (P = 0.0025), and C-reactive protein (P < 0.0001). We observed the trajectory of biomarker changes in patients after admission hospitalization, and determined that early mortality was associated with a rapidly increasing D-dimer level, and gradually decreasing platelet and lymphocyte counts. Multivariate and simple linear regression models indicated that the risk of death was associated with immune and thrombotic pathway activation. Elevated admission cTnI levels were associated with elevated IL-6 (P = 0.03) and D-dimer (P = 0.0021) levels. Conclusion: In patients with COVID-19 with cardiac injury, IL-6 and D-dimer levels at admission predicted subsequently elevated cTnI levels and early death, thus highlighting the need for early inflammatory cytokine-based risk stratification in patients with cardiac injury.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".