Secondary infections in critically ill patients with viral pneumonia due to COVID-19 and influenza: a historical cohort study
Bibliographic record
Abstract
PURPOSE: To compare the incidence and nature of secondary infections (SI) between critically ill patients with viral pneumonia due to COVID-19 and seasonal influenza and explore the association between SI and clinical outcomes. METHODS: We conducted a historical cohort study of patients admitted to the intensive care unit (ICU) at two tertiary care centers during the first wave of the COVID-19 pandemic and patients admitted with influenza during the 2018-2019 season. The primary outcome was the rate of SI. Secondary outcomes included rates of ICU and in-hospital mortality, organ-support-dependent disease, and length of ICU and hospital stay. RESULTS: Secondary infections developed in 55% of 95 COVID-19 patients and 51% of 47 influenza patients (unadjusted odds ratio [OR], 1.16; 95% confidence interval [CI], 0.57 to 2.33). After adjusting for baseline differences between cohorts, there were no significant differences between the COVID-19 cohort and the influenza cohort (adjusted OR, 1.00; 95% CI, 0.41 to 2.44). COVID-19 patients with SI had longer ICU and hospital stays and duration of mechanical ventilation. The SI incidence was higher in COVID-19 patients treated with steroids than in those not treated with steroids (15/20, 75% vs 37/75, 49%). CONCLUSION: Secondary infections were common among critically ill patients with viral pneumonia including COVID-19. We found no difference in the incidence of SI between COVID-19 and influenza in our cohort study, but SI in patients with COVID-19 were associated with worse clinical outcomes and increased healthcare resource use. The small cohort size precludes any causal inferences but may provide a basis for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 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".