Cold Agglutinin Disease and COVID-19: A Scoping Review of Treatments and Outcomes
Bibliographic record
Abstract
Background: Reports suggest that patients with both acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and cold agglutinin disease (CAD) may experience poorer survival when treated with rituximab. We conducted a scoping review to evaluate severe outcomes, including intensive care unit (ICU) admission and mortality, in coronavirus disease 2019 (COVID-19) patients with CAD on various treatments, including rituximab. Methods: This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Four literature databases were searched on December 19, 2023, for studies reporting lab-confirmed SARS-CoV-2 and CAD, excluding rheumatological conditions. Results: Of the 741 screened articles, 19 were included. Studies, predominantly case reports (17/19) or case series (2/19), were mainly from the USA (8/19) and India (3/19), with others across Europe and Asia. Among 23 patients (61% female, median age 61 years), 21/23 had a new CAD diagnosis; only two had pre-existing CAD. Overall, 74% recovered, 21% died, and outcomes for one were unreported. Nine (39%) were ICU-admitted. Of rituximab-treated patients (n = 4), 25% were ICU-admitted, none died. Non-rituximab treatments (n = 19) saw 42% ICU admissions and 26% mortality. Conclusions: This review found no increased risk of severe outcomes in CAD and COVID-19 patients treated with rituximab.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".