Prevalence and Risk Factors of Postacute Sequelae of COVID-19 in Adults With Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
Objective Incidence and manifestations of postacute sequelae of coronavirus disease 2019 (PASC) are poorly defined among immunosuppressed populations. We reported, phenotyped, and assessed risk factors for PASC in adults with systemic autoimmune diseases. Methods Persons aged ≥ 18 years with systemic autoimmune diseases were recruited into a national, prospective observational cohort of SARS-CoV-2 vaccination and infection between December 2020 and April 2021. Serial surveys assessed vaccination status, SARS-CoV-2 infection incidence, and disease flares. Participants reporting SARS-CoV-2 infection received a questionnaire assessing symptom duration, severity, and quality of life (QOL) effect; PASC was defined as ≥ 1 symptom persisting for > 12 weeks. PASC syndromes were mapped by overlapping symptom domains. Characteristics were compared between participants who did vs did not report PASC. Results Among 1615 participants, 590 (36.5%) reported SARS-CoV-2 infection and were sent PASC surveys, 299 (50.7%) of whom responded > 12 weeks following the reported infection. Respondents were 91.6% female, 91.2% White, median (IQR) age was 48 (40-60) years with median (IQR) 3 (2-3) vaccine doses at time of first infection. Common diagnoses included inflammatory arthritis (38.5%) and inflammatory bowel disease (14.4%). Eighty-nine of 299 (29.8%) reported PASC, with the most reported symptom domain being neurological/psychological (83.1%); 84% reported an effect on QOL. Participants with PASC reported lower number of preceding vaccines (median [IQR] 2 [2-3] vs 3 [2-3];P< 0.001) and more reinfections (16.9% vs 5.7%;P= 0.004). Conclusion In a large, real-world cohort, 29.8% of persons with systemic autoimmune disease reported PASC, often affecting QOL. Preceding vaccination may reduce PASC, whereas multiple infections may increase risk, supporting ongoing booster vaccine campaigns and efforts to limit breakthrough infections.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".