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How to Improve Cancer Patients ENrollment in Clinical Trials From rEal-Life Databases Using the Observational Medical Outcomes Partnership Oncology Extension: Results of the PENELOPE Initiative in Urologic Cancers

Authors :
Emmanuelle Kempf
Morgan Vaterkowski
Damien Leprovost
Nicolas Griffon
David Ouagne
Stéphane Breant
Patricia Serre
Alexandre Mouchet
Bastien Rance
Gilles Chatellier
Ali Bellamine
Marie Frank
Julien Guerin
Xavier Tannier
Alain Livartowski
Martin Hilka
Christel Daniel
Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS)
Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord
Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)
Université Paris Cité (UPCité)
Hôpital Européen Georges Pompidou [APHP] (HEGP)
Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)
Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138))
École Pratique des Hautes Études (EPHE)
Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)
Health data- and model- driven Knowledge Acquisition (HeKA)
Inria de Paris
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138))
Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE)
Institut Curie [Paris]
Source :
JCO Clinical Cancer Informatics, JCO Clinical Cancer Informatics, 2023, 7, ⟨10.1200/CCI.22.00179⟩
Publication Year :
2023
Publisher :
American Society of Clinical Oncology (ASCO), 2023.

Abstract

PURPOSE To compare the computability of Observational Medical Outcomes Partnership (OMOP)–based queries related to prescreening of patients using two versions of the OMOP common data model (CDM; v5.3 and v5.4) and to assess the performance of the Greater Paris University Hospital (APHP) prescreening tool. MATERIALS AND METHODS We identified the prescreening information items being relevant for prescreening of patients with cancer. We randomly selected 15 academic and industry-sponsored urology phase I-IV clinical trials (CTs) launched at APHP between 2016 and 2021. The computability of the related prescreening criteria (PC) was defined by their translation rate in OMOP-compliant queries and by their execution rate on the APHP clinical data warehouse (CDW) containing data of 205,977 patients with cancer. The overall performance of the prescreening tool was assessed by the rate of true- and false-positive cases of three randomly selected CTs. RESULTS We defined a list of 15 minimal information items being relevant for patients' prescreening. We identified 83 PC of the 534 eligibility criteria from the 15 CTs. We translated 33 and 62 PC in queries on the basis of OMOP CDM v5.3 and v5.4, respectively (translation rates of 40% and 75%, respectively). Of the 33 PC translated in the v5.3 of the OMOP CDM, 19 could be executed on the APHP CDW (execution rate of 58%). Of 83 PC, the computability rate on the APHP CDW reached 23%. On the basis of three CTs, we identified 17, 32, and 63 patients as being potentially eligible for inclusion in those CTs, resulting in positive predictive values of 53%, 41%, and 21%, respectively. CONCLUSION We showed that PC could be formalized according to the OMOP CDM and that the oncology extension increased their translation rate through better representation of cancer natural history.

Details

ISSN :
24734276
Database :
OpenAIRE
Journal :
JCO Clinical Cancer Informatics
Accession number :
edsair.doi.dedup.....ec71e8d8193de6d2ea29d663665b5875