Abstract S12-01: High mortality among hospital-acquired COVID-19 infection in patients with cancer: An observational cohort study from Quebec and British Columbia
Bibliographic record
Abstract
Abstract Background: Studies suggest that patients with cancer are more likely to experience severe outcomes from COVID-19. Therefore, cancer centers have undertaken efforts to care for patients with cancer in COVID-free zones. Nevertheless, nosocomial transmission of COVID-19 in patients with cancer likely occurs, but the frequency and relevance of these events remain unknown. The goal of this study was to determine the incidence and impact of hospital-acquired COVID-19 in this population and identify prognostic factors for COVID-19 severity in patients with cancer. Methods: Patients with cancer and a laboratory-confirmed or presumed diagnosis of COVID-19 were prospectively identified using provincial registries and hospital databases between March 3rd and May 23rd, 2020, in the provinces of Quebec and British Columbia. Patients’ baseline characteristics including age, sex, comorbidities, cancer type, and type of anticancer treatment were collected. The primary outcome was incidence of hospital-acquired infection defined by diagnosis of SARS-CoV-2 5 days after hospital admission for COVID-unrelated cause. Co-primary outcomes were death or composite outcomes of severe illness from COVID-19 such as hospitalization, supplemental oxygen, intensive-care unit (ICU) admission, and/or mechanical ventilation. Results: A total of 253 patients (N=250 adult and N=3 pediatric) with COVID-19 and cancer were identified, and the majority were residents of Quebec (N=236). Ninety patients (35.6%) received active anticancer treatment in the last 3 months prior to COVID-19 diagnosis. During a median follow-up of 23 days, 209 (82.6%) required hospitalization, 38 (15%) required admission to ICU, and 71 (28%) died. Forty-seven (19%) had a diagnosis of hospital-acquired COVID-19. Median overall survival was shorter in those with hospital-acquired infection, compared to a contemporary community-acquired population (27 days vs. 71 days, HR 2.2, 95% CI 1.2-4.0, p=0.002). Multivariate analysis demonstrated that hospital-acquired COVID-19, age, ECOG status, and advanced stage of cancer were independently associated with death. Conclusion: Our study demonstrates a high rate of nosocomial transmission of COVID-19, associated with increased mortality in both univariate and multivariate analysis in the cancer population, reinforcing the importance of treating patients with cancer in COVID-free zones. We also validated that age, poor ECOG, and advanced cancer were negative prognostic factors for COVID-19 in patients with cancer. Citation Format: Arielle Elkrief, Antoine Desilets, Neha Papneja, Lena Cvetkovic, Catherine Groleau, Yahia Abdelali Lakehal, Layla Shbat, Corentin Richard, Julie Malo, Wiam Belkaid, Erin Cook, Stephane Doucet, Thai Hoa Tran, Patrice Savard, Kevin Jao, Nathalie Daaboul, Eric Bhang, Jonathan Loree, Wilson Miller, Donald Vinh, Nathaniel Bouganim, Gerald Batist, Caroline Letendre, Bertrand Routy. High mortality among hospital-acquired COVID-19 infection in patients with cancer: An observational cohort study from Quebec and British Columbia [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S12-01.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".