Outcomes of patients with hematologic malignancies and COVID-19: a report from the ASH Research Collaborative Data Hub
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is an illness resulting from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019. Patients with cancer, and especially those with hematologic malignancies, may be at especially high risk of adverse outcomes, including mortality resulting from COVID-19 infection. 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. At the time of this report, 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 <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 forgo intensive care unit admission in favor of a palliative approach. Taken together, these data support the emerging consensus that patients with hematologic malignancies experience significant morbidity and mortality resulting from COVID-19 infection. Batch submissions from sites with high incidence of COVID-19 infection are planned to support future analyses.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".