Profile of the multicenter cohort of the German Cancer Consortium’s Clinical Communication Platform
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Published:2023-04-05
Issue:5
Volume:38
Page:573-586
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ISSN:0393-2990
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Container-title:European Journal of Epidemiology
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language:en
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Short-container-title:Eur J Epidemiol
Author:
Maier DanielORCID, Vehreschild Jörg JanneORCID, Uhl BarbaraORCID, Meyer SandraORCID, Berger-Thürmel Karin, Boerries MelanieORCID, Braren RickmerORCID, Grünwald ViktorORCID, Hadaschik BorisORCID, Palm Stefan, Singer SusanneORCID, Stuschke Martin, Juárez David, Delpy Pierre, Lambarki Mohamed, Hummel MichaelORCID, Engels Cäcilia, Andreas StefanieORCID, Gökbuget NicolaORCID, Ihrig Kristina, Burock Susen, Keune Dietmar, Eggert Angelika, Keilholz Ulrich, Schulz Hagen, Büttner Daniel, Löck SteffenORCID, Krause Mechthild, Esins Mirko, Ressing Frank, Schuler MartinORCID, Brandts Christian, Brucker Daniel P.ORCID, Husmann Gabriele, Oellerich Thomas, Metzger PatrickORCID, Voigt Frederik, Illert Anna L., Theobald Matthias, Kindler Thomas, Sudhof Ursula, Reckmann Achim, Schwinghammer Felix, Nasseh Daniel, Weichert Wilko, von Bergwelt-Baildon Michael, Bitzer MichaelORCID, Malek Nisar, Öner Öznur, Schulze-Osthoff KlausORCID, Bartels StefanORCID, Haier JörgORCID, Ammann Raimund, Schmidt Anja Franziska, Guenther Bernd, Janning MelanieORCID, Kasper BerndORCID, Loges SonjaORCID, Stilgenbauer Stephan, Kuhn PeterORCID, Tausch Eugen, Runow Silvana, Kerscher Alexander, Neumann Michael, Breu Martin, Lablans MartinORCID, Serve Hubert
Abstract
AbstractTreatment concepts in oncology are becoming increasingly personalized and diverse. Successively, changes in standards of care mandate continuous monitoring of patient pathways and clinical outcomes based on large, representative real-world data. The German Cancer Consortium’s (DKTK) Clinical Communication Platform (CCP) provides such opportunity. Connecting fourteen university hospital-based cancer centers, the CCP relies on a federated IT-infrastructure sourcing data from facility-based cancer registry units and biobanks. Federated analyses resulted in a cohort of 600,915 patients, out of which 232,991 were incident since 2013 and for which a comprehensive documentation is available. Next to demographic data (i.e., age at diagnosis: 2.0% 0–20 years, 8.3% 21–40 years, 30.9% 41–60 years, 50.1% 61–80 years, 8.8% 81+ years; and gender: 45.2% female, 54.7% male, 0.1% other) and diagnoses (five most frequent tumor origins: 22,523 prostate, 18,409 breast, 15,575 lung, 13,964 skin/malignant melanoma, 9005 brain), the cohort dataset contains information about therapeutic interventions and response assessments and is connected to 287,883 liquid and tissue biosamples. Focusing on diagnoses and therapy-sequences, showcase analyses of diagnosis-specific sub-cohorts (pancreas, larynx, kidney, thyroid gland) demonstrate the analytical opportunities offered by the cohort’s data. Due to its data granularity and size, the cohort is a potential catalyst for translational cancer research. It provides rapid access to comprehensive patient groups and may improve the understanding of the clinical course of various (even rare) malignancies. Therefore, the cohort may serve as a decisions-making tool for clinical trial design and contributes to the evaluation of scientific findings under real-world conditions.
Funder
Johann Wolfgang Goethe-Universität, Frankfurt am Main
Publisher
Springer Science and Business Media LLC
Reference28 articles.
1. Joos S, Nettelbeck DM, Reil-Held A, et al. German Cancer Consortium (DKTK)–A national consortium for translational cancer research. Mol Oncol. 2019. https://doi.org/10.1002/1878-0261.12430. 2. Meropol NJ, Donegan J, Rich AS. Progress in the application of machine learning algorithms to cancer research and care. JAMA Netw Open. 2021. https://doi.org/10.1001/jamanetworkopen.2021.16063. 3. Yuan Q, Cai T, Hong C, et al. Performance of a machine learning algorithm using electronic health record data to identify and estimate survival in a longitudinal cohort of patients with lung cancer. JAMA Netw Open. 2021. https://doi.org/10.1001/jamanetworkopen.2021.14723. 4. Morin O, Vallières M, Braunstein S, et al. An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nat Cancer. 2021. https://doi.org/10.1038/s43018-021-00236-2. 5. Obermeyer Z, Emanuel EJ. Predicting the future—big data, machine learning, and clinical medicine. N Engl J Med. 2016. https://doi.org/10.1056/nejmp1606181.
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