Factors Associated With an Electronic Health Record–Based Definition of Postacute Sequelae of COVID-19 in Patients With Systemic Autoimmune Rheumatic Disease
Bibliographic record
Abstract
OBJECTIVE: Many individuals with rheumatic disease are at higher risk for severe acute coronavirus disease 2019 (COVID-19). We aimed to evaluate risk factors for postacute sequelae of COVID-19 (PASC) using an electronic health record (EHR)-based definition. METHODS: We identified patients with prevalent rheumatic diseases and COVID-19 within the Mass General Brigham healthcare system. PASC was defined by the International Classification of Diseases, 10th revision (ICD-10) codes, relevant labs, vital signs, and medications at least 30 days following the first COVID-19 infection. Patients were followed until the earliest of incident PASC, repeat COVID-19 infection, 1 year of follow-up, death, or February 19, 2023. We used multivariable Cox regression to estimate the association of baseline characteristics with PASC risk. RESULTS: Among 2459 patients (76.37% female, mean age 57.4 years), the most common incident PASC manifestations were cough (14.56%), dyspnea (12.36%), constipation (11.39%), and fatigue (10.70%). Serious manifestations including acute coronary disease (4.43%), thromboembolism (3.09%), hypoxemia (3.09%), stroke (1.75%), and myocarditis (0.12%) were rare. The Delta wave (adjusted hazard ratio [aHR] 0.63, 95% CI 0.49-0.82) and Omicron era (aHR 0.50, 95% CI 0.41-0.62) were associated with lower risk of PASC than the early pandemic period (March 2020-June 2021). Age, obesity, comorbidity burden, race, and hospitalization for acute COVID-19 infection were associated with greater risk of PASC. Glucocorticoid (GC) use (aHR 1.19, 95% CI 1.05-1.34 compared to no use) was associated with greater risk of PASC. CONCLUSION: Among patients with rheumatic diseases, following their first COVID-19 infection, we found a decreased risk of PASC over calendar time using an EHR-based definition. Aside from GCs, no specific immunomodulatory medications were associated with increased risk, and risk factors were otherwise similar to those seen in the general population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".