Post-COVID dyspnea: prevalence, predictors, and outcomes in a longitudinal, prospective cohort
Bibliographic record
Abstract
BACKGROUND: The pathophysiology, evolution, and associated outcomes of post-COVID dyspnea remain unknown. The aim of this study was to determine the prevalence, severity, and predictors of dyspnea 12 months following hospitalization for COVID-19, and to describe the respiratory, cardiac, and patient-reported outcomes in patients with post-COVID dyspnea. METHODS: We enrolled a prospective cohort of all adult patients admitted to 2 academic hospitals in Vancouver, Canada with PCR-confirmed SARS-CoV-2 during the first wave of COVID between March and June 2020. Dyspnea was measured 3, 6, and 12 months after initial symptom onset using the University of California San Diego Shortness of Breath Questionnaire. RESULTS: A total of 76 patients were included. Clinically meaningful dyspnea (baseline score > 10 points) was present in 49% of patients at 3 months and 46% at 12 months following COVID-19. Between 3 and 12 months post-COVID-19, 24% patients had a clinically meaningful worsening in their dyspnea, 49% had no meaningful change, and 28% had a clinically meaningful improvement in their dyspnea. There was worse sleep, mood, quality of life, and frailty in patients with clinically meaningful dyspnea at 12 months post-COVID infection compared to patients without dyspnea. There was no difference in PFT findings, troponin, or BNP comparing patients with and without clinically meaningful dyspnea at 12 months. Severity of dyspnea and depressive symptoms at 3 months predicted severity of dyspnea at 12 months. CONCLUSIONS: Post-COVID dyspnea is common, persistent, and negatively impacts quality of life. Mood abnormalities may play a causative role in post-COVID dyspnea in addition to potential cardiorespiratory abnormalities. Dyspnea and depression at initial follow-up predict longer-term post-COVID dyspnea, emphasizing that standardized dyspnea and mood assessment following COVID-19 may identify patients at high risk of post-COVID dyspnea and facilitating early and effective management.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".