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Record W3213564228 · doi:10.1182/blood-2021-146016

Clinical Predictors of Outcome in Adult Patients with Acute Leukemias and Myelodysplastic Syndrome and COVID-19 Infection: Report from the American Society of Hematology Research Collaborative (ASH RC) Data Hub

2021· article· en· W3213564228 on OpenAlexaff
Pinkal Desai, Aaron D. Goldberg, Kenneth C. Anderson, Varun Narendra, Donna Neuberg, Vivek Radhakrishnan, Robert Redd, Gail J. Roboz, Laurie H. Sehn, Mikkael A. Sekeres, Maximilian Stahl, Martin S. Tallman, John Colton Thompson, Emily Tucker, William A. Wood, Lisa K. Hicks

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsSt. Michael's HospitalBC Cancer Agency
Fundersnot available
KeywordsHematologyMedicineInternal medicineMyelodysplastic syndromesCoronavirus disease 2019 (COVID-19)Hematologic NeoplasmsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)OncologyIntensive care medicineImmunologyCancerDiseaseBone marrow

Abstract

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Abstract Background: Predictors of severe infection and outcomes with COVID-19 in patients (pts) with acute myeloid leukemia (AML), acute lymphocytic leukemia (ALL) and myelodysplastic syndromes (MDS) are lacking. Pts with active disease may experience worse outcomes due to overall prognosis and cytopenias. Here we identify risk factors for severe COVID-19 infection and mortality in pts with AML, MDS, and ALL using the ASH RC COVID-19 Registry for Hematology. Methods: The ASH RC COVID-19 Registry for Hematology includes features and outcomes of a laboratory-confirmed or presumptive diagnosis of SARS-CoV-2 infection in adult pts with ongoing or a history of blood disorders. The Registry opened for data collection on April 1, 2020 and is a global effort housed on a secure data platform hosted by Prometheus Research, an IQVIA company. Data are made publicly available and regularly updated on the ASH RC website. Pt characteristics, outcomes, and predictors were analyzed and stratified by disease status (active initial diagnosis and relapsed/refractory vs. remission) and type of hematologic malignancy. Variables included age, comorbidities, type of hematologic malignancy (AML, MDS, ALL), neutrophil and lymphocyte count at time of COVID-19 diagnosis, and active treatment at the time of COVID-19 diagnosis. COVID-19 severity was defined as mild (no hospitalization required), moderate (hospitalization required), or severe (ICU admission required). Categorical pt characteristics for each response group and associations between response groups and characteristics (i.e., alive vs. dead, severity vs. non-severity) were summarized by frequency with differences between response groups evaluated by Fisher's exact test and odds ratios with 95% confidence intervals (CIs) estimated by logistic regression. Multivariable analyses identified independent predictors of outcomes. Results: Analyses were conducted on data from 257 pts with AML (n=135), MDS (n=40), and ALL (n=82); 46% were in remission and 44% had active disease (10% unknown). Overall mortality from COVID-19 infection was 21%. Pts with active disease were significantly more likely to present with moderate and severe COVID-19 compared to those in remission (remission vs. active disease, severe 33% (n=20) vs. 67%(n=40), moderate 45% (n=35) vs.55% (n=42), and mild: 67% (n=56) vs. 33% (n=28), p value <0.001) (Figure 1). This was significant when categorized as severe vs. non severe as well (p=0.002). COVID-19 severity was also associated with AML diagnosis, major comorbidities, and neutropenia and lymphopenia at the time of COVID-19 diagnosis. Univariate analyses of increased mortality after COVID-19 diagnosis were significantly associated with advanced age, male sex, pre-diagnosis survival < 6 months, active disease status, neutropenia, lymphopenia and forgoing ICU care. Multivariable analyses in all pts (Figure 1), revealed that increased COVID-19 related mortality was significantly associated with neutropenia at diagnosis (OR 3.15, 95% C.I. 1.31-8.08, p=0.01), estimated pre-COVID-19 prognosis of < 6 months (OR 8.58, 95% C.I. 3.24-24.46, p<0.001) and forgoing ICU care (OR 6.66, 95% C.I. 2.56-18.23, p<0.001). Among hospitalized pts, increased COVID-19 mortality was associated with estimated pre-COVID-19 prognosis of < 6 months (OR 6.77, 95% C.I. 2.34-22.24, p<0.001) and forgoing ICU care (OR 3.98, 95% C.I. 1.45-11.66, p=0.007). Pts who were older, male, smokers, with active disease, or estimated to have pre-COVID-19 survival of < 6 months were more likely to forgo ICU care. Forgoing ICU care (n=37,16%) was associated with a higher COVID-19 mortality in all pts (n=234, OR 15.6, 95% C.I. 6.4-40.9, p<0.001), hospitalized pts (n=143, OR 9.2, 95% C.I., 3.5-26.5, p<0.001) and in pts where ICU admission was indicated and declined (n=61 OR 5.6, 95% C.I. 1.1-56.4, p=0.03)). Neither active disease status nor ongoing cancer treatment were associated with increased mortality among hospitalized patients. Conclusions: These data suggest that patients with active disease experience significantly higher COVID-19 severity but not increased mortality from COVID-19. Patients who had neutropenia and a pre-COVID-19 prognosis of < 6 months had higher mortality from COVID-19 infection and may be more likely to forgo ICU care. If desired by patients, aggressive support for hospitalized patients with COVID-19 is appropriate regardless of remission status. Figure 1 Figure 1. Disclosures Desai: Agios: Consultancy; Janssen R&D: Research Funding; Kura Oncology: Consultancy; Bristol Myers Squibb: Consultancy; Astex: Research Funding; Takeda: Consultancy. Goldberg: AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Aprea: Research Funding; Prelude Therapeutics: Research Funding; Pfizer: Research Funding; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; DAVA Oncology: Honoraria; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees; Arog: Research Funding; Celularity: Research Funding; Aptose: Consultancy, Research Funding. Anderson: Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; Celgene: 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; 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; Millenium-Takeda: 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. Neuberg: Pharmacyclics: Research Funding; Madrigal Pharmaceuticals: Other: Stock ownership. Radhakrishnan: Emcure Pharmaceuticals: Other: payment to institute; Bristol Myers Squibb: Other: payment to institute; Astrazeneca: Consultancy, Honoraria; Cipla Pharmaceuticals: Honoraria, Other: payment to institute; Pfizer: Consultancy, Honoraria; Johnson and Johnson: Honoraria; Novartis: Honoraria; Aurigene: Speakers Bureau; Roche: Honoraria, Other: payment to institute; Intas Pharmaceutical: Other: payment to institute; Dr Reddy's Laboratories: Honoraria, Membership on an entity's Board of Directors or advisory committees; Janssen India: Honoraria; NATCO Pharmaceuticals: Research Funding. Roboz: Novartis: Consultancy; Mesoblast: Consultancy; Amgen: Consultancy; Actinium: Consultancy; AbbVie: Consultancy; Janssen: Consultancy; Blueprint Medicines: Consultancy; Astex: Consultancy; Janssen: Research Funding; Daiichi Sankyo: Consultancy; Jazz: Consultancy; Agios: Consultancy; Glaxo SmithKline: Consultancy; Celgene: Consultancy; Otsuka: Consultancy; Astellas: Consultancy; Helsinn: Consultancy; MEI Pharma - IDMC Chair: Consultancy; Jasper Therapeutics: Consultancy; Bristol Myers Squibb: Consultancy; AstraZeneca: Consultancy; Bayer: Consultancy; Pfizer: Consultancy; Roche/Genentech: Consultancy. Sehn: Novartis: Consultancy; Genmab: Consultancy; Debiopharm: Consultancy. Sekeres: Takeda/Millenium: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees. Tallman: Syros: Membership on an entity's Board of Directors or advisory committees; Kura: Membership on an entity's Board of Directors or advisory committees; NYU Grand Rounds: Honoraria; 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; 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. Wood: Pfizer: Research Funding; Teladoc: Consultancy; Koneksa Health: Consultancy, Current equity holder in publicly-traded company.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.069
GPT teacher head0.407
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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