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Enregistrement W3032768522 · doi:10.1136/esmoopen-2020-000825

Cancer datasets and the SARS-CoV-2 pandemic: establishing principles for collaboration

2020· letter· en· W3032768522 sur OpenAlexaboutno aff
Carlo Palmieri, D. Palmer, Peter Openshaw, J. Kenneth Baillie, Malcolm G. Semple, Lance Turtle

Notice bibliographique

RevueESMO Open · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensnon disponible
Organismes subventionnairesBiotechnology and Biological Sciences Research CouncilNational Institute for Health and Care ResearchWellcome Trust
Mots-clésPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Cancer2019-20 coronavirus outbreakGeographyVirologyMedicineBiologyGeneticsOutbreak

Résumé

récupéré en direct d'OpenAlex

Dear Editor, The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been a major disruptive event for the global oncology community. It has challenged and compromised the delivery of oncological care as a result of (1) the diversion of resource to support the care of acutely and critically ill patients with COVID-19; (2) a reduction in the number of highly trained staff who deliver such treatments due to sickness, self-isolation or family reasons1.Survey on NHS physician work absence during COVID-19 pandemic. Available: https://www.rcplondon.ac.uk/ news/covid-19-and-its-impactnhs-workforceGoogle Scholar; and (3) concerns related to treating patients with cancer, given issues related to the potential risks of acquiring SARS-CoV-2 and the degree of severity of COVID-19 as a result of either innate or iatrogenic cancer-related immunodeficiency. The current peer-reviewed data regarding the course of COVID-19 in patients with cancer is limited to retrospective cases series of 11–105 patients with variability in the data reported2.Liang W. Guan W. Chen R. et al.Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China.http://www.ncbi.nlm.nih.gov/pubmed/32066541Lancet Oncol. 2020; 21: 335-337doi:10.1016/S1470-2045(20)30096-6Google Scholar, 3.Yu J. Ouyang W. Chua M.L.K. et al.SARS-CoV-2 transmission in patients with cancer at a tertiary care hospital in Wuhan, China.JAMA Oncol. 2020; doi:10.1001/jamaoncol.2020.0980Crossref Scopus (822) Google Scholar, 4.Zhang L. Zhu F. Xie L. et al.Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan, China.http://www.ncbi.nlm.nih.gov/pubmed/32224151Ann Oncol. 2020; ([Epub ahead of print 26 Mar 2020])doi:10.1016/j.annonc.2020.03.296Google Scholar, 5.He W. Chen L. Chen L. et al.COVID-19 in persons with haematological cancers.http://www.ncbi.nlm.nih.gov/pubmed/32332856Leukemia. 2020; ([Epub ahead of print 24 Apr 2020])doi:10.1038/s41375-020-0836-7Google Scholar, 6.Dai M. Liu D. Liu M. et al.Patients with cancer appear more vulnerable to SARS-COV-2: a multicenter study during the COVID-19 outbreak.http://www.ncbi.nlm.nih.gov/pubmed/32345594Cancer Discov. 2020; ([Epub ahead of print 28 Apr 2020])doi:10.1158/2159-8290.CD-20-0422Google Scholar and one study involving aggregate-level data from 334 patients.7.Miyashita H. Mikami T. Chopra N. et al.Do patients with cancer have a poorer prognosis of COVID-19? an experience in New York City.http://www.ncbi.nlm.nih.gov/pubmed/32330541Ann Oncol. 2020; ([Epub ahead of print 21 Apr 2020])doi:10.1016/j.annonc.2020.04.006Google Scholar These datasets do not enable the identification of risk factors that might predispose to symptomatic SARS-CoV-2 infection nor do they identify those factors that predict for serious morbidity and mortality as a result of infection. Such information is urgently needed to inform the development of a robust evidence base approach to risk stratification by tumour and treatment type, as well as development and introduction of appropriate mitigation measures. It is in response to this information vacuum that a number of cancer-specific observational studies and audits have been developed in an organic and parallel manner (table 1). These studies cover surgical, oncological and psychological aspects of COVID-19. They range from the collection of data on all cancers to information on specific cancers; most are retrospective, involving a single specialist group. Some do take a cross specialty approach and enable comparison to non-cancer cohorts, as well as enable translational research from biological samples (table 1). In addition to these, there are audits led by specialist societies, such as Intensive Care National Audit and Research Centre, which provide some information on cancer cases with the potential to be analysed in great detail and to allow comparison with patients without cancer.8.Intensive care national audit and research centre (ICNARC) report on COVID-19 in critical care.2020https://www.icnarc.org/Google Scholar The International Severe Acute Respiratory and Emerging Infections Consortium WHO Clinical Characterisation Protocol has collected detailed clinical information and outcomes for over 30 000 people of all ages admitted to hospitals with COVID-19 and has recorded major comorbidities and concomitant medications that identify those people affected by cancer.9.International severe acute respiratory and emerging infections Consortium (ISARIC), COVID-19 report.2020https://isaric.tghn.org/Google ScholarTable 1Summary of the current cancer observational and translational studies related to the SARS-CoV-2/COVID-19 pandemicName of study/locationBrief descriptionCovidSurg–Cancer/globalObservationalEvaluate the 30-day COVID-19 infection rates in elective cancer surgery during the COVID-19 pandemic (https://globalsurg.org/cancercovidsurg/)The COVID-19 and Cancer ConsortiumThe USA, the European Union, Argentina, Canada and the UK are eligible to participate. Currently, there are 100 USA centres.ObservationalAim is to collect data about patients with cancer who have been infected with COVID-19 (https://ccc19.org/)American Society of Haematology Research CollaborativeCOVID-19 Registry for Hematologic Malignancy/globalObservationalCaptures data on people who test positive for COVID-19 and have been or are currently being treated for hematological malignancy (https://www.ashresearchcollaborative.org/covid-19-registry)Thoracic Cancers International COVID-19 Collaboration/globalObservationalA global consortium designed to gather information on patients with thoracic cancer infected with COVID-19 regardless of therapies administered (http://www.etop-eu.org/index.php?option=com_content&view=article&id=115644&catid=13&Itemid=557)Clinical Characterisation Protocol–Cancer UK/UKProspective observational and biological samplesThe study will characterise the presentation, management and outcome of patients with solid and haematological malignancies recruited into the prospective Clinical Characterisation Protocol for Severe Emerging Infections in the UK. It will also compare patients with cancer to those without cancer. The biology of SARS-CoV-2 in the context of cancer-associated or iatrogenic immunosuppression will also be investigated (https://isaric.tghn.org/UK-CCP/)UK Coronavirus Cancer Monitoring Project/UKObservationalThe UK Coronavirus Cancer Monitoring scheme is a clinician-led reporting project recoding data related to patients with cancer who have tested positive for COVID-19 across the UK (https://ukcoronaviruscancermonitoring.com/).Paediatrics (https://ukcoronaviruscancermonitoring.com/paediatrics/)ONCOVID/UK, Italy and SpainObservationalTo describe the features of COVID-19 infection in patients with cancer, investigate its severity in this population and evaluate long-term outcomes (https://www.oncovid.net/)UK COVID and Gynaecological Cancer Study/UKObservationalRecords and assesses changes and outcomes in patients across the whole patient pathway and within the multidisciplinary team [email protected]Patients with AML and COVID-19 Epidemiology/UKObservationalAims to understand the incidence, presentation and severity of COVID-19 during treatment of AML As well as to develop informed recommendations for the care of patients with AML, including those who develop COVID-19 infection during treatment or have recovered from prior COVID-19 infectionCOVID-RTClinicaland Translational Radiotherapy (CT-RAD) Research Working Group, UKObservationalAimsto capture changes in radiotherapy pathways and understand their impact onradiotherapy services and patient outcomes across the UK. The initiative willnot only focus on patients with COVID-19, but all radiotherapy patients (https://www.ncri.org.uk/news/covid19-radiotherapy-initiative/)The American Society of Clinical Oncology Survey on COVID-19 in Oncology Registry/USAObservationalCaptures baseline and follow-up data on how the impact of SARS-CoV-2 on cancer care and cancer patient outcomes during the COVID-19 pandemic and into 2021Psychology study/ChinaObservationalThe effects of prevention and control measures on treatment and psychological status of patients with cancer during the COVID-19 outbreak (http://www.chictr.org.cn/showproj.aspx?proj=50714)Clinically related study/ChinaObservational/retrospectiveClinical characteristics and prognosis of patients with cancer with COVID-19 based on bioinformatics analysis(http://www.chictr.org.cn/showproj.aspx?proj=51019)Perioperative immune prediction and intervention of patients with tumour undergoing surgery during the COVID-19 outbreak period/ChinaInterventional/prospectiveTo understand the influence of the pandemic on the prognosis of patients undergoing cancer surgery and to understand the influence of different interventions on outcomes (http://www.chictr.org.cn/showproj.aspx?proj=50984).AML, acute myeloid leukemia; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. Open table in a new tab AML, acute myeloid leukemia; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. It is key that all these important efforts culminate in a robust evidence which can enable1.Survey on NHS physician work absence during COVID-19 pandemic. Available: https://www.rcplondon.ac.uk/ news/covid-19-and-its-impactnhs-workforceGoogle Scholar governments and policy makers to provide clear advice regarding the need or otherwise for patients with cancer to self-isolate/cocoon, as well as to identify groups to prioritise for vaccination or other evidence-based interventions which might reduce the severity of infection2.Liang W. Guan W. Chen R. et al.Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China.http://www.ncbi.nlm.nih.gov/pubmed/32066541Lancet Oncol. 2020; 21: 335-337doi:10.1016/S1470-2045(20)30096-6Google Scholar; oncologists to provide clear advice regarding the risks of specific treatment modalities and systemic anticancer therapy for specific cancers in the era of SARS-CoV-2 and3.Yu J. Ouyang W. Chua M.L.K. et al.SARS-CoV-2 transmission in patients with cancer at a tertiary care hospital in Wuhan, China.JAMA Oncol. 2020; doi:10.1001/jamaoncol.2020.0980Crossref Scopus (822) Google Scholar patients to make more informed decisions regarding their cancer care and the degree they choose interact and mix at societal and family levels. The latter is particularly important for patients with life-limiting diagnoses, as well as for addressing the mental health effects of self-isolation.10.Holmes E.A. O'Connor R.C. Perry V.H. et al.Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science.http://www.ncbi.nlm.nih.gov/pubmed/32304649Lancet Psychiatry. 2020; ([Epub ahead of print 15 Apr 2020])doi:10.1016/S2215-0366(20)30168-1Google Scholar To enable this and to ensure we harness the true potential of all these data, we wish to suggest the adoption of what we have named ‘principles for collaboration in the field of cancer and COVID-19’. These principles are (1) the establishment of a searchable database of all non-IMP (Investigational medicinal products) COVID-19 cancer studies with all protocols and documents being made available; (2) enabling patients with cancer to register and contribute their own data and biological material if they so wish; (3) establishment of an agreed core cancer COVID-19 dataset with accepted common definitions, such as defining events and severity of infection; (4) the involvement of experts in infectious disease, microbiology, infection control and critical care in all projects, given the cross-cutting nature of COVID-19 and the need to capture relevant data across theses specialities; (5) agreement to bring all datasets together for a meta-analysis; and (6) the creation of a public facing open-access repository of all data for future research and policymaking. We hope that the oncology–COVID research community that has developed since the inception of the pandemic can cooperate and coordinate using these principles for the benefit of our patients and society.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,988

Scores Codex et Gemma par catégorie

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

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 Opus0,218
Tête enseignante GPT0,465
Écart entre enseignants0,247 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations7
Publié2020
Routes d'admission1
Résumé présentoui

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