Risks for Hospitalization and Death Among Patients with Blood Disorders from the ASH RC COVID-19 Registry for Hematology
Bibliographic record
Abstract
Abstract INTRODUCTION: Patients (pts) with blood disorders are at particular risk for severe infection and death from COVID-19. Factors that contribute to this risk, including cancer treatment, have not been clearly delineated. The ASH RC COVID-19 Registry for Hematology is a public-facing, volunteer registry reporting outcomes of COVID-19 infection in pts with underlying blood disorders. We report a multivariable analysis of the impact of cancer treatment and other key variables on COVID-19 mortality and hospitalization among pts with blood cancer. METHODS: Data were collected between April 1, 2020, and July 2, 2021. All analyses were performed using R version 4.0.2. Multivariable logistic regression explored associations between mortality and seven patient/disease factors previously reported as important to COVID-19 outcome. Independent variables included: age (>60); sex; presence of a major comorbidity (defined as any of heart disease, hypertension, pulmonary disease and/or diabetes); type of hematologic malignancy; estimated prognosis of < 6 months prior to COVID-19; deferral of ICU care; and administration of cancer treatment in the previous year (excluding single agent hydroxyurea). A secondary multivariable logistic regression explored associations between the same variables and hospitalization with COVID-19. RESULTS: We included all pts in the registry with a malignant diagnosis except for 3 patients excluded based on a data sharing agreement (N=1029). Median age category was 50-59y (range <5y to > 90y). The sample was 42% female and 28% had major comorbidities. Types of hematologic malignancies were 354 (34%) acute leukemia/MDS, 255 (25%) lymphoma, 206 (20%) plasma cell dyscrasia (myeloma/amyloid/POEMS), 116 (11%) CLL, 98 (10%) myeloproliferative neoplasm (MPN). Most pts (73%) received cancer treatment during the previous year, 9% had a pre-COVID-19 prognosis of <6months, and 10% deferred ICU care. COVID-19 mortality in the entire cohort was 17%. In multivariable analyses, age > 60 (OR 2.03, 1.31-3.18), male sex (OR 1.69, 1.11 - 2.61), estimated pre-COVID-19 prognosis of less than 6 months (OR 6.16, 3.26 - 11.70) and ICU deferral (OR 10.87, 6.36 - 18.96) were all independently associated with an increased risk of death. Receiving cancer treatment in the year prior to COVID-19 diagnosis and type of hematologic malignancy were not significantly associated with death. In multivariable analyses, age > 60 (OR 2.46, 1.83 - 3.31), male sex (OR 1.34, 1.02 - 1.76), estimated pre-COVID-19 prognosis of < 6 months (OR 4.81, 2.45 - 10.50), presence of a major comorbidity (OR 1.57, 1.15 - 2.16), and cancer treatment in the previous year (OR 1.50, 1.10 - 2.06) were all independently associated with an increased risk of a severe COVID-19 requiring hospitalization. Pts with a MPN or plasma cell dyscrasia and COVID-19 were less likely to require hospitalization for COVID-19 compared to patients with CLL, leukemia/MDS, or lymphoma. CONCLUSIONS: These analyses confirm the negative impact of age > 60, male sex, pre-COVID-19 prognosis of < 6 months, and deferral of ICU care on mortality among patients with hematologic malignancy and COVID-19. We did not observe an increased risk of COVID-19 mortality among pts with COVID-19 who received blood cancer treatment in the previous year, although rate of hospitalization was higher. Pts with some hematologic malignancies (MPN, plasma cell dyscrasias), may experience less severe COVID-19 infections than others. Disclosures Anderson: Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium-Takeda: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Scientific Founder of Oncopep and C4 Therapeutics: Current equity holder in publicly-traded company, Current holder of individual stocks in a privately-held company; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Mana Therapeutics: Membership on an entity's Board of Directors or advisory committees. Desai: Janssen R&D: Research Funding; Astex: Research Funding; Kura Oncology: Consultancy; Agios: Consultancy; Bristol Myers Squibb: Consultancy; Takeda: Consultancy. Goldberg: Celularity: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees; Aptose: Consultancy, Research Funding; Prelude Therapeutics: Research Funding; DAVA Oncology: Honoraria; Pfizer: Research Funding; Arog: Research Funding; Aprea: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Neuberg: Madrigal Pharmaceuticals: Other: Stock ownership; Pharmacyclics: Research Funding. Radhakrishnan: Janssen India: Honoraria; Dr Reddy's Laboratories: Honoraria, Membership on an entity's Board of Directors or advisory committees; Aurigene: Speakers Bureau; Novartis: Honoraria; Johnson and Johnson: Honoraria; Pfizer: Consultancy, Honoraria; Astrazeneca: Consultancy, Honoraria; Emcure Pharmaceuticals: Other: payment to institute; Cipla Pharmaceuticals: Honoraria, Other: payment to institute; Bristol Myers Squibb: Other: payment to institute; Roche: Honoraria, Other: payment to institute; Intas Pharmaceutical: Other: payment to institute; NATCO Pharmaceuticals: Research Funding. Sehn: Genmab: Consultancy; Debiopharm: Consultancy; Novartis: Consultancy. Sekeres: Novartis: Membership on an entity's Board of Directors or advisory committees; Takeda/Millenium: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Tallman: Kura: Membership on an entity's Board of Directors or advisory committees; Syros: Membership on an entity's Board of Directors or advisory committees; Innate Pharma: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Biosight: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Oncolyze: Membership on an entity's Board of Directors or advisory committees; KAHR: Membership on an entity's Board of Directors or advisory committees; Orsenix: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Rafael Pharmaceuticals: Research Funding; Glycomimetics: Research Funding; Biosight: Research Funding; Orsenix: Research Funding; Abbvie: Research Funding; NYU Grand Rounds: Honoraria; Mayo Clinic: Honoraria; UC DAVIS: Honoraria; Northwell Grand Rounds: Honoraria; NYU Grand Rounds: Honoraria; Danbury Hospital Tumor Board: Honoraria; Acute Leukemia Forum: Honoraria; Miami Leukemia Symposium: Honoraria; New Orleans Cancer Symposium: Honoraria; ASH: Honoraria; NCCN: Honoraria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".