Anti-cardiolipin and other anti-phospholipid antibodies in critically ill COVID-19 positive and negative patients
Bibliographic record
Abstract
ABSTRACT Background Reports of severe COVID-19 being associated with thrombosis, anti-phospholipid antibodies (APLA), anti-phospholipid syndrome (APS) have yielded disparate conclusions. Studies comparing COVID-19 patients with contemporaneous controls of similar severity are lacking. Methods 22 COVID + and 20 COVID − patients with respiratory failure admitted to intensive care were studied longitudinally. Demographic and clinical data were obtained from the day of admission. APLA testing included anti-cardiolipin (aCL), anti-β2glycoprotien 1 (β2GP1), anti-domain 1 beta2 glycoprotein 1 (β2GP1) and anti-phosphatidyl serine/prothrombin complex (PS/PT). Anti-nuclear antibodies (ANA) were detected by immunofluorescence and antibodies to cytokines by a commercially available multiplexed array. ANOVA was used for continuous variables and Fisher’s exact test was used for categorical variables with α=0.05 and the false discovery rate at q=0.05. Results APLA were predominantly IgG aCL (48%) followed by IgM (21%) in all patients, with a tendency toward higher frequency among the COVID + . aCL was not associated with surrogate markers of thrombosis but IgG aCL was strongly associated with worse disease severity and higher ANA titers regardless of COVID-19 status. An association between aCL and anti-cytokine autoantibodies tended to be higher among the COVID + . Conclusions Positive APLA serology was associated with more severe disease regardless of COVID-19 status. KEY MESSAGES What is already known about this subject? COVID-19 is associated with coagulopathy and high morbidity and mortality. COVID-19 shares some of these clinical features with anti-phospholipid syndrome. Reports of an association of anti-phospholipid antibodies with high risk COVID-19 have yielded disparate conclusions, but they lacked longitudinal follow up and control groups of similar severity. What does this study add? Anti-phospholipid syndrome serology assessed longitudinally was predominantly anticardiolipin IgG autoantibodies, in 48% of patients. Anticardiolipin serology was associated with worse disease severity in both COVID-19 positive and negative patients. How might this impact on clinical practice or future developments? The use of anti-phospholipid antibodies tests in the COVID-19 clinical setting needs to be taken in context; whereas they are associated with more serve disease, they do not discriminate between COVID-19 positive and negative 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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".