Respiratory burden in post-acute COVID-19 sequelae: a longitudinal study of airway and systemic inflammation and clinical outcomes
Bibliographic record
Abstract
Background: It is unclear why patients with post-acute coronavirus disease 2019 sequelae (PACS) often present with persistent respiratory symptoms. We hypothesised that autoimmune inflammatory biomarkers may be associated with the persistence and/or resolution of these symptoms. We performed symptom-based unsupervised cluster analysis to evaluate airway and systemic immune responses in PACS participants over time. Methods: Individuals with confirmed SARS-CoV-2 infection, a persistent range of PACS symptoms for >12 weeks and no previous diagnosis of chronic lung disease were recruited and assessed at a 6-month follow-up. Assessments included St George's Respiratory Questionnaire (SGRQ), pulmonary function testing, 6-min walk test and analysis of blood and sputum inflammatory markers. Results: Unsupervised clustering based on SGRQ domains of 85 PACS individuals revealed four clusters. Cluster 1 (14%) reported no impairment and normal lung function, whereas clusters 2 (24%) and 3 (36%) were moderately symptomatic. Cluster 3 had a greater proportion of reduced lung function. Cluster 4 (26%) reported severe impairment across all SGRQ domains, with significantly lower 6-min walk distance, dyspnoea and fatigue. Clusters 3 and 4 had evidence of systemic inflammation (C-reactive protein and anti-SS-B/La). Sputum analysis showed no evidence of airway inflammation in any cluster. After 6 months, improved symptoms in 43% of individuals correlated with increased forced expiratory volume in 1 s percentage predicted and low serum interleukin-8 (p<0.05) over time. Multivariate regression suggested that a reduction in serum anti-SS-B/La IgG over 6 months was associated with improvement of SGRQ impact (t=3.17, p=0.003) and activity (t=2.04, p=0.005). Conclusions: A subset of previously healthy PACS patients have clinically relevant respiratory burden as identified by unbiased SGRQ domain analysis associated with systemic inflammation and autoantibodies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".