IL-6 and D-Dimer at Admission Predicts Cardiac Injury and Early Mortality during SARS-CoV-2 Infection
Bibliographic record
Abstract
Abstract BACKGROUND We recently described mortality of cardiac injury in COVID-19 patients. Admission activation of immune, thrombotic biomarkers and their ability to predict cardiacinjury and mortality patterns in COVID-19 is unknown. METHODS This retrospective cohort study included 170 COVID-19 patients with cardiac injury at admission to Tongji Hospital in Wuhan from January 29–March 8, 2020. Temporal evolution of inflammatory cytokines, coagulation markers, clinical, treatment and mortality were analyzed. RESULTS Of 170 patients, 60 (35.3%) died early (<21d) and 61 (35.9%) died after prolonged stay. Admission lab work that correlated with early death were elevate levels of interleukin 6 (IL-6) (p<0.0001), Tumor Necrosis Factor-a (TNF-a) (p=0.0025), and C-reactive protein (CRP) (p<0.0001). We observed the trajectory of biomarker changes after admission, and determined that early mortality had a rapidly increasing D-dimer, gradually decreasing platelet and lymphocyte counts. Multivariate and simple linear regression models showed that death risk was determined by immune and thrombotic pathway activation. Increasing cTnI levels were associated with those of increasing IL-6 (p=0.03) and D-dimer (p=0.0021). Exploratory analyses suggested that patients that received heparin has lower early mortality compared to those who did not (p =0.07), despite similar risk profile. CONCLUSIONS In COVID-19 patients with cardiac injury, admission IL-6 and D-dimer predicted subsequent elevation of cTnI and early death, highlighting the need for early inflammatory cytokine-based risk stratification in patients with cardiac injury. Condensed Abstract COVID-19 with cardiac injury is associated with worse survival. Admission activation of immune, thrombotic biomarkers and their ability to predict cardiac injury and mortality patterns in COVID-19 is unknown. This study proved that cardiac injury in these patients is closely related to the activation of immunological and thrombotic pathways and can be predicted by admission biomarkers of these pathways. This study supports the strategy of biomarker-guided, point-of-care therapy that warrants further studies in a randomized manner to develop anti-immune and anti-thrombotic treatment regimens in severe COVID-19 patients with cardiac injury.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".