C-reactive protein and clinical outcomes in patients with COVID-19
Bibliographic record
Abstract
BACKGROUND: A systemic inflammatory response is observed in coronavirus disease 2019 (COVID-19). Elevated serum levels of C-reactive protein (CRP), a marker of systemic inflammation, are associated with severe disease in bacterial or viral infections. We aimed to explore associations between CRP concentration at initial hospital presentation and clinical outcomes in patients with COVID-19. METHODS AND RESULTS: Consecutive adults aged ≥18 years with COVID-19 admitted to a large New York healthcare system between 1 March and 8 April 2020 were identified. Patients with measurement of CRP were included. Venous thrombo-embolism (VTE), acute kidney injury (AKI), critical illness, and in-hospital mortality were determined for all patients. Among 2782 patients hospitalized with COVID-19, 2601 (93.5%) had a CRP measurement [median 108 mg/L, interquartile range (IQR) 53-169]. CRP concentrations above the median value were associated with VTE [8.3% vs. 3.4%; adjusted odds ratio (aOR) 2.33, 95% confidence interval (CI) 1.61-3.36], AKI (43.0% vs. 28.4%; aOR 2.11, 95% CI 1.76-2.52), critical illness (47.6% vs. 25.9%; aOR 2.83, 95% CI 2.37-3.37), and mortality (32.2% vs. 17.8%; aOR 2.59, 95% CI 2.11-3.18), compared with CRP below the median. A dose response was observed between CRP concentration and adverse outcomes. While the associations between CRP and adverse outcomes were consistent among patients with low and high D-dimer levels, patients with high D-dimer and high CRP have the greatest risk of adverse outcomes. CONCLUSIONS: Systemic inflammation, as measured by CRP, is strongly associated with VTE, AKI, critical illness, and mortality in COVID-19. CRP-based approaches to risk stratification and treatment should be tested.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".