COVID-19 prevalence, risk factors and outcomes in COPD
Bibliographic record
Abstract
Background: Due to pre-existing lung abnormalities and abnormal immune responses, the possible impact of COVID-19 in COPD is of real concern. Aim: To investigate the prevalence of COVID-19 in a cohort of properly diagnosed and precisely characterized COPD patients and to evaluate the possible risk and prognostic factors predicting the clinical outcome. Methods: Study cohort:370 subjects followed in outpatient COPD clinics. The characteristics of COPD patients with COVID-19 (COPD/COVID+) were compared to a sex and age-matched COPD/COVID- group randomly selected from our cohort. The characteristics of COPD/COVID+ patients needing high (HighIC) or low (LowIC) intensity care were compared. Results: From Feb to Nov2020, 22(5.9%) patients had molecular-confirmed diagnosis of COVID-19. Hypertension [100 vs 68%;p=0.008] and dyslipidemia [59 vs 27%;p=0.03] were more prevalent in COPD/COVID+ than in COPD/COVID-. Pulmonary function was similar in the 2 groups. The 10 of 22 (45%) COPD/COVID+ patients requiring HighIC had a higher prevalence of dyslipidemia [90 vs 41%;p=0.03] and metabolic syndrome [70 vs 16%; p=0.02] than LowIC, obesity and type 2 diabetes were similar. Degree of airflow obstruction was similar in the 2 groups, but low DLCO [32 vs 88%pred;p=0.02] and CT emphysema [89 vs 36%;p=0.028] were more prevalent in HighIC than in LowIC. Conclusions: The COVID-19 prevalence in our COPD cohort was 5.9%. Cardiometabolic, but not respiratory parameters, were risk factors for the infection while cardiometabolic comorbidities and lung parenchyma damage (emphysema and low DLCO) were prognostic factors for worse outcomes in COPD/COVID+ patients. Identification of these factors is essential to plan better strategies to protect fragile COPD patients.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".