Complications and severity of COVID-19 in patients with head and neck cancer (HNC): A COVID-19 and Cancer Consortium (CCC19) registry analysis.
Bibliographic record
Abstract
6031 Background: Patients with cancer have worse outcomes from COVID-19 infection. However, the specific impact of COVID-19 on patients with head and neck cancer (HNC) is largely unknown. The COVID-19 and Cancer Consortium (CCC19) maintains an international registry (NCT04354701) aimed to investigate the clinical course and complications of COVID-19 in patients with cancer. Here, we report severity of COVID-19 and its complications among HNC patients. Methods: The CCC19 registry was queried for patients with HNC and laboratory confirmed SARS-CoV-2 infection. The co-primary outcomes were severity of COVID-19 illness on an ordinal scale (0: no complications; 1: hospitalized, no oxygen (O2); 2: hospitalized, required O2; 3: ICU admission; 4: mechanical ventilation (MV); 5: death), and severity of complications (mild, moderate, serious). The outcomes were further stratified by demographics, recent treatment (systemic vs local; surgery, radiation (RT) vs systemic), treatment intent (palliative vs curative), and cancer status (remission, responding, stable, progressing). Results: From March 2020 to December 2021, 356 HNC patients were identified. Median age was 65 (interquartile range 58-74), 29% were female, 56% were white, 67% were former or current smokers, 20% had a BMI >30, 15% had an ECOG performance status >2, and 57% had >2 comorbidities. 154 (43%) had no complications, 61 (17%) were hospitalized without O2, 135 (38%) were hospitalized with O2, 50 (14%) required ICU, 32 (9%) required MV, and 74 (21%) died. 88 (25%) had mild, 59 (17%) had moderate, and 132 (37%) had serious complications. 33% of patients who received systemic therapy and 30% who received RT within 3 mo prior to COVID-19 diagnosis died. Mortality was higher in patients receiving palliative when compared to curative intent treatment (44% vs 16%). In addition, 50% of patients with actively progressing cancer, and 45% who had serious complications died. Importantly, 37 (n=12 palliative systemic therapy and n=25 local therapy) patients had a treatment delay due to COVID-19 diagnosis. Conclusions: Our study is the largest cohort to date describing COVID-19 outcomes in HNC patients and suggest a high rate of mortality even in those receiving local and curative intent treatment. Variables stratified by COVID-19 severity. Note: Ordinal levels 3 and 4 not shown due to small case numbers. Characteristics Worst severity of COVID-19 illness 0(N=150)% 1(N=51)% 2(N=60)% 5(N=74)% Complication severityMildModerateSerious 8425<3 1037<10 <8278 <7845 ECOG>2 15 <10 19 52 Obesity (BMI >30) 49 10 25 10 SmokingNeverFormer/current 4938 1216 1419 1921 Treatment intentCurativePalliative 4825 1615 1915 1644 Treatment modalitySystemic therapyLocal therapy 3430 1910 1128 3327 Cancer statusRemission/NEDResponding/stableProgressing 503925 121814 1824<8 121650 Note: Ordinal levels 3 and 4 not shown due to small case number
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".