Invasive mould disease in fatal COVID-19: a systematic review of autopsies
Bibliographic record
Abstract
Invasive mould disease (IMD) might affect up to a third of critically ill patients with COVID-19. COVID-19-associated pulmonary aspergillosis (CAPA) is typically diagnosed on the basis of a combination of non-specific clinical, radiographical, and mycological findings, but whether most cases represent invasive disease is unresolved. We systematically reviewed autopsy series of three or more decedents with COVID-19 for evidence of IMD. We searched PubMed, Web of Science, OVID (Embase), and medRxiv for studies in English or French published from Jan 1, 2019, to Sept 26, 2020. We identified 1070 references, of which 50 studies met the criteria. These studies described autopsies from 677 decedents, with individual-level data for 443 decedents. The median age was 70·0 years (IQR 57·0-79·0). Of decedents with individual-level data, 133 (30%) had diabetes, 97 (22%) had pre-existing lung disease, and 27 (6%) had immunocompromising conditions. Of 548 decedents with such data, 320 (58%) received invasive mechanical ventilation; among 140 decedents for whom this was known, ventilation was for a median of 9·0 days (IQR 5·0-20·0). Treatment included immunomodulation in 60 decedents and antifungals in 50 decedents. Autopsy-proven IMD occurred in 11 (2%) of 677 decedents, including eight CAPA, two unspecified IMD, and one disseminated mucormycosis. Among 320 decedents who received mechanical ventilation, six (2%) had IMD. We conclude that IMD, including CAPA, is an uncommon autopsy finding in COVID-19.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".