Prolonged elevation of D‐dimer levels in convalescent COVID‐19 patients is independent of the acute phase response
Bibliographic record
Abstract
BACKGROUND: Persistent fatigue, breathlessness, and reduced exercise tolerance have been reported following acute COVID-19 infection. Although immuno-thrombosis has been implicated in acute COVID-19 pathogenesis, the biological mechanisms underpinning long COVID remain unknown. We hypothesized that pulmonary microvascular immuno-thrombosis may be important in this context. METHODS: One hundred fifty COVID-19 patients were reviewed at St James's Hospital Dublin between May and September 2020 at a median of 80.5 (range 44-155) days after initial diagnosis. These included patients hospitalized during initial illness (n = 69) and others managed entirely as out-patients (n = 81). Clinical examination, chest x-ray, and 6-min walk tests were performed. In addition, a range of coagulation and inflammatory markers were assessed. RESULTS: Increased D-dimer levels (>500 ng/ml) were observed in 25.3% patients up to 4 months post-SARS-CoV-2 infection. On univariate analysis, elevated convalescent D-dimers were more common in COVID-19 patients who had required hospital admission and in patients aged more than 50 years (p < .001). Interestingly, we observed that 29% (n = 11) of patients with elevated convalescent D-dimers had been managed exclusively as out-patients during their illness. In contrast, other coagulation (prothrombin time, activated partial thromboplastin time, fibrinogen, platelet count) and inflammation (C-reactive protein, interleukin-6, and sCD25) markers had returned to normal in >90% of convalescent patients. CONCLUSIONS: Elucidating the biological mechanisms responsible for sustained D-dimer increases may be of relevance in long COVID pathogenesis and has implications for clinical management of these patients.
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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) | 0.001 | 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) | 0.002 | 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".