COVID-19 Associated Pulmonary Aspergillosis: Systematic Review and Patient-Level Meta-analysis
Bibliographic record
Abstract
Abstract Rationale Pulmonary aspergillosis may complicate COVID-19 and contribute to excess mortality in intensive care unit (ICU) patients. The incidence is unclear because of discordant definitions across studies. Objective We sought to review the incidence, diagnosis, treatment, and outcomes of COVID-19-associated pulmonary aspergillosis (CAPA), and compare research definitions. Methods We systematically reviewed the literature for ICU cohort studies and case series including ≥ patients with CAPA. We calculated pooled incidence. Patients with sufficient clinical details were reclassified according to 4 standardized definitions (Verweij, White, Koehler, and Bassetti). Measurements Correlations between definitions were assessed with Spearman’s rank test. Associations between antifungals and outcome were assessed with Fisher’s Exact test. Main Results 38 studies (35 cohort studies and 3 case series) were included. Among 3,297 COVID-19 patients in ICU cohort studies, 313 were diagnosed with CAPA (pooled incidence 9.5%). 197 patients had patient-level data allowing reclassification. Definitions had limited correlation with one another (ρ=0.330 to 0.621, p<0.001). 38.6% of patients reported to have CAPA did not fulfil any research definitions. Patients were diagnosed after a median of 9 days (interquartile range 5-14) in ICUs. Tracheobronchitis occured in 5.3% of patients examined with bronchoscopy. The mortality rate (50.0%) was high, irrespective of antifungal use (p=0.28); this remained true even when the analysis was restricted to patients meeting standardized definitions for CAPA. Conclusions The reported incidence of CAPA is exaggerated by use of non-standard definitions. Further research should focus on identifying patients likely to benefit from antifungals.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".