Coinfections in Patients With Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Study
Bibliographic record
Abstract
BACKGROUND: The frequency of coinfections and their association with outcomes have not been adequately studied among patients with cancer and coronavirus disease 2019 (COVID-19), a high-risk group for coinfection. METHODS: We included adult (≥18 years) patients with active or prior hematologic or invasive solid malignancies and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection, using data from the COVID-19 and Cancer Consortium (CCC19, NCT04354701). We captured coinfections within ±2 weeks from diagnosis of COVID-19, identified factors cross-sectionally associated with risk of coinfection, and quantified the association of coinfections with 30-day mortality. RESULTS: Among 8765 patients (hospitalized or not; median age, 65 years; 47.4% male), 16.6% developed coinfections: 12.1% bacterial, 2.1% viral, 0.9% fungal. An additional 6.4% only had clinical diagnosis of a coinfection. The adjusted risk of any coinfection was positively associated with age >50 years, male sex, cardiovascular, pulmonary, and renal comorbidities, diabetes, hematologic malignancy, multiple malignancies, Eastern Cooperative Oncology Group Performance Status, progressing cancer, recent cytotoxic chemotherapy, and baseline corticosteroids; the adjusted risk of superinfection was positively associated with tocilizumab administration. Among hospitalized patients, high neutrophil count and C-reactive protein were positively associated with bacterial coinfection risk, and high or low neutrophil count with fungal coinfection risk. Adjusted mortality rates were significantly higher among patients with bacterial (odds ratio [OR], 1.61; 95% CI, 1.33-1.95) and fungal (OR, 2.20; 95% CI, 1.28-3.76) coinfections. CONCLUSIONS: Viral and fungal coinfections are infrequent among patients with cancer and COVID-19, with the latter associated with very high mortality rates. Clinical and laboratory parameters can be used to guide early empiric antimicrobial therapy, which may improve clinical outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".