An update on the overall epidemiology, clinical characteristics, and outcomes from the COVID-19 and Cancer Consortium (CCC19).
Bibliographic record
Abstract
10565 Background: Despite mitigation and treatment strategies, COVID-19 continues to negatively impact patients (pts) with cancer. Identifying factors that remain consistently associated with morbidity and mortality is critical for risk identification and care delivery. Methods: Using CCC19 registry data through 12/31/2021 we report clinical outcomes (30-day case fatality rate [CFR], mechanical ventilation use (MV), intensive care unit admission (ICU), and hospitalization) in adult pts with cancer and laboratory confirmed SARS-CoV-2, stratified by patient, cancer, and treatment-related factors. Results: In this cohort of 11,417 pts (with 4% reported vaccination prior to COVID-19), 55% required hospitalization, 15% ICU, 9% MV, and 12% died. Overall outcome rates remained similar for 2020 and 2021 (Table). Hydroxychloroquine was utilized in 11% and other anti-COVID-19 drugs (remdesivir, tocilizumab, convalescent plasma, and/or steroids) in 30%. Higher CFRs were observed in older age, males, Black race, smoking (14%), comorbidities (pulmonary [17%], diabetes mellitus [16%], cardiovascular [19%], renal [21%]), ECOG performance status 2+ (31%), co-infection (25%), especially fungal (35%), and initial presentation with severe COVID-19 (48%). Pts with hematologic malignancy, active/progressing cancer status, or receiving systemic anti-cancer therapy within 1-3 months prior to COVID-19 also had worse CFRs. CFRs were similar across anti-cancer modalities. Other outcomes (ICU, MV, hospitalization) followed similar distributions by pt characteristics. Conclusions: Unfavorable outcome rates continue to remain high over 2 years, despite fewer case reports in 2021 owing to multiple factors (e.g., pandemic dynamics, respondent fatigue, overwhelmed healthcare systems). Pts with specific socio-demographics, performance status, comorbidities, type and status of cancer, immunosuppressive therapies, and COVID-19 severity at presentation experienced worse COVID-19 severity; and these factors should be further examined through multivariable modeling. Understanding epidemiological features, patient and cancer-related factors, and impact of anti-COVID-19 interventions can help inform risk stratification and interpretation of results from clinical trials. [Table: see text]
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".