Concomitant infections in patients with cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) study.
Bibliographic record
Abstract
6561 Background: COVID-19 has been associated with immune modulation that may predispose infected patients to bacterial, viral, or fungal co-infections. Due to critical illness, > 70% of patients with severe COVID-19 receive empiric antibacterial or antifungal therapy, along with standard anti-COVID-19 treatments. However, the frequency of proven or probable secondary infections is < 10%. To our knowledge, there are no studies evaluating co-infections in patients with cancer and COVID-19, a vulnerable group with multiple risk factors for co-infections. We aim to describe the prevalence of bacterial, viral, and fungal co-infections, identify risk factors for coinfection, and investigate the potential impact of co-infections on mortality, in patients with a history of cancer and COVID-19. Methods: The CCC19 registry (NCT04354701) includes patients with active or prior hematologic or invasive solid malignancies reported across academic and community sites. We captured bacterial, fungal, or viral co-infections diagnosed within ±2 weeks from diagnosis of COVID-19, identified factors associated with an increased risk of having a co-infection, and evaluated the association of co-infections with 30-day all-cause mortality. Results: We examined 6732 patients with a history of cancer and a laboratory-confirmed diagnosis of SARS-CoV-2 reported to CCC19 by 82 sites between March 17, 2020 and February 3, 2021, with complete data on coinfection status. Median age was 65 (interquartile range: 55-75) years with 48% male, 52% non-Hispanic white, 19% non-Hispanic black, and 16% Hispanic. 5448 (81%) had solid tumors and 1466 (22%) had hematologic malignancies. Bacterial infections were reported in 823 patients (12%), including 296 Gram+ and 245 Gram- bacterial events. Documented viral (176 patients, 3%) and fungal (59 patients, 0.9%) co-infections were rare. The risk for co-infections increased with age, and they were more frequent among men, older patients, and those with diabetes, pulmonary or renal comorbid conditions, active progressive cancer, or hematologic malignancies (unadjusted P< 0.01). The frequency of reported co-infections decreased over the study period (divided into quartiles, Mantel-Haenszel P< 0.01). All-cause mortality rates were higher among those with bacterial (24% vs. 10%), viral (22% vs. 12%), and fungal (37% vs. 12%) coinfections compared to those without (unadjusted P< 0.01). Conclusions: The frequency of bacterial infections in patients with cancer and COVID-19 is relatively low. Viral and fungal co-infections are uncommon. Co-infections are associated with higher mortality rates. Several patient and tumor factors can be used for risk stratification and guide early empiric antimicrobial agent selection, which may improve clinical outcomes. These data could inform antimicrobial stewardship interventions in this tenuous patient population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".