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Enregistrement W4205402413 · doi:10.1182/blood-2020-141327

Outcomes of Patients with Hematologic Malignancies and COVID-19 Infection: A Report from the ASH Research Collaborative Data Hub

2020· article· en· W4205402413 sur OpenAlexaff
William A. Wood, Donna Neuberg, John Colton Thompson, Martin S. Tallman, Mikkael A. Sekeres, Laurie H. Sehn, Kenneth C. Anderson, Aaron D. Goldberg, Nathan A. Pennell, Charlotte M. Niemeyer, Emily Tucker, Kathleen Hewitt, Robert M. Plovnick, Lisa K. Hicks

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensSt. Michael's HospitalBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésMedicineDiseaseHematologyCancerInternal medicineIntensive care medicineHematologic diseaseComorbidityHematologic Neoplasms

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,045

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,023
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,010
Études des sciences et des technologies0,0010,000
Communication savante0,0020,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,238
Tête enseignante GPT0,452
Écart entre enseignants0,214 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2020
Routes d'admission1
Résumé présentoui

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