Outcomes of Patients with Hematologic Malignancies and COVID-19 Infection: A Report from the ASH Research Collaborative Data Hub
Bibliographic record
Abstract
Introduction: The coronavirus disease 2019 (COVID-19) is an illness resulting from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019. Many patients with blood cancer have underlying immune dysfunction, and many are treated with chemotherapies and immunotherapies that are themselves profoundly immunosuppressive. Additionally, patients with blood cancer are often older, may have comorbid illness including hypertension and diabetes, and may be especially susceptible to complications of COVID-19 include hypercoagulability and thrombosis. For patients with hematologic malignancies, overall risk of morbidity and mortality from COVID-19 infection, and how this risk varies as a function of age, disease status, type of malignancy, and cancer therapy, has not yet been well defined. Methods: The ASH Research Collaborative COVID-19 Registry for Hematology was developed to study features and outcomes of COVID-19 infection in patients with underlying blood disorders, such as hematologic malignancies. The Registry opened for data collection on April 1, 2020. The Registry is a global effort and is housed on a secure data platform hosted by Prometheus Research, an IQVIA company. The Registry collects data from patients of all ages with a current or history of hematological disease, and either a laboratory-confirmed or presumptive diagnosis of SARS-CoV-2 infection. Data are made available and regularly updated on the ASH Research Collaborative website to guide the provider and patient communities. Data presented here are limited to malignant hematologic diseases only. Contributors are individual providers or designees submitting data on behalf of providers. Results: At the time of this analysis, data from 250 patients with blood cancers from 74 sites around the world had been entered into the Registry. The most commonly represented malignancies were acute leukemia (33%), non-Hodgkin lymphoma (27%), and myeloma or amyloidosis (16%). Patients presented with a myriad of symptoms, most frequently fever (73%), cough (67%), dyspnea (50%), and fatigue (40%). Use of COVID-19-directed therapies such as hydroxychloroquine (N=76) or azithromycin (N=59) was common. Overall mortality was 28%. Patients with a physician-estimated prognosis from the underlying hematologic malignancy of less than 12 months at the time of COVID-19 diagnosis and those with relapsed/refractory disease experienced a higher proportion of moderate/severe COVID-19 disease and death. In some instances, death occurred after a decision was made to forego ICU admission in favor of a palliative approach. Conclusions: Taken together, these data support the emerging consensus that patients with hematologic malignancies experience significant morbidity and mortality from COVID-19 infection. However, we see no reason, based on our data, to withhold intensive therapies from patients with underlying hematologic malignancies and favorable prognoses, if aggressive supportive care is consistent with patient preferences. Batch submissions from sites with high incidence of COVID-19 infection are ongoing. The Registry has been expanded to include non-malignant hematologic diseases, and the Registry will continue to accumulate data as a resource for the hematology community. Figure Disclosures Wood: Pfizer: Research Funding; Teladoc/Best Doctors: Consultancy; ASH Research Collaborative: Honoraria. Neuberg:Celgene: Research Funding; Madrigak Pharmaceuticals: Current equity holder in publicly-traded company; Pharmacyclics: Research Funding. Tallman:Amgen: Research Funding; UpToDate: Patents & Royalties; Bioline rx: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees; KAHR: Membership on an entity's Board of Directors or advisory committees; Rigel: Membership on an entity's Board of Directors or advisory committees; Delta Fly Pharma: Membership on an entity's Board of Directors or advisory committees; Oncolyze: Membership on an entity's Board of Directors or advisory committees; BioSight: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cellerant: Research Funding; Orsenix: Research Funding; ADC Therapeutics: Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Rafael: Research Funding; Glycomimetics: Research Funding; Abbvie: Research Funding. Sekeres:BMS: Consultancy; Takeda/Millenium: Consultancy; Pfizer: Consultancy. Sehn:Karyopharm: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Kite: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Apobiologix: Consultancy, Honoraria; AstraZeneca: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; Acerta: Consultancy, Honoraria; TG therapeutics: Consultancy, Honoraria; Chugai: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Teva: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding; MorphoSys: Consultancy, Honoraria; Merck: Consultancy, Honoraria; Lundbeck: Consultancy, Honoraria; Genentech, Inc.: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Verastem Oncology: Consultancy, Honoraria. Anderson: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; Oncopep and C4 Therapeutics.: Other: Scientific Founder of Oncopep and C4 Therapeutics.; Gilead: Membership on an entity's Board of Directors or advisory committees; 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. Goldberg:Dava Oncology: Honoraria; ADC Therapeutics: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy; Aptose: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Celularity: Research Funding; AROG: Research Funding; Aprea: Research Funding. Pennell:Astrazeneca: Consultancy; BMS: Consultancy; Eli Lilly: Consultancy; Amgen: Consultancy; Genentech: Consultancy; Cota: Consultancy; Merck: Consultancy; Inivata: Consultancy; G1 Therapeutics: Consultancy. Niemeyer:Celgene: Consultancy; Novartis: Consultancy. Hicks:Gilead Sciences: Research Funding.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".