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Maria Rüthrich
Researcher at Leibniz Association
Publications - 19
Citations - 232
Maria Rüthrich is an academic researcher from Leibniz Association. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 4, co-authored 10 publications receiving 66 citations.
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Journal ArticleDOI
COVID-19 in cancer patients: clinical characteristics and outcome-an analysis of the LEOSS registry
Maria Rüthrich,C. Giessen-Jung,Stefan Borgmann,Annika Y. Classen,Sebastian Dolff,Beate Grüner,Frank Hanses,Nora Isberner,Philipp Köhler,Julia Lanznaster,Uta Merle,Silvio Nadalin,Christiane Piepel,J. Schneider,J. Schneider,M. Schons,R. Strauss,Lukas Tometten,Jorg-Janne Vehreschild,Jorg-Janne Vehreschild,M. von Lilienfeld-Toal,Gernot Beutel,Kai Wille +22 more
TL;DR: In this paper, the authors present an analysis of cancer patients from the LEOSS (Lean European Open Survey on SARS-CoV-2 Infected Patients) registry to determine whether cancer patients are at higher risk.
Journal ArticleDOI
Evidence-based management of COVID-19 in cancer patients: Guideline by the Infectious Diseases Working Party (AGIHO) of the German Society for Haematology and Medical Oncology (DGHO).
Nicola Giesen,Rosanne Sprute,Maria Rüthrich,Yascha Khodamoradi,Sibylle C. Mellinghoff,Gernot Beutel,Catherina Lueck,Michael Koldehoff,Marcus Hentrich,Michael Sandherr,Michael von Bergwelt-Baildon,H.-H. Wolf,Hans H. Hirsch,Bernhard Wörmann,Oliver A. Cornely,Philipp Köhler,Enrico Schalk,Marie von Lilienfeld-Toal +17 more
TL;DR: This guideline provides evidence-based recommendations regarding prevention, diagnostics and treatment of SARS-CoV-2 infection and COVID-19 as well as strategies towards safe antineoplastic care during the CO VID-19 pandemic.
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Neurological symptoms and complications in predominantly hospitalized COVID-19 patients: Results of the European multinational Lean European Open Survey on SARS-Infected Patients (LEOSS).
Nina N. Kleineberg,Nina N. Kleineberg,Samuel Knauss,Eileen Gülke,Hans O. Pinnschmidt,Carolin Jakob,Paul Lingor,Kerstin Hellwig,Achim Berthele,Günter U. Höglinger,Günter U. Höglinger,Gereon R. Fink,Gereon R. Fink,Matthias Endres,Christian Gerloff,Christine Klein,Melanie Stecher,Annika Y. Classen,Siegbert Rieg,Stefan Borgmann,Frank Hanses,Maria Rüthrich,Martin Hower,Lukas Tometten,Martina Haselberger,Christiane Piepel,Uta Merle,Sebastian Dolff,Christian Degenhardt,Björn-Erik Ole Jensen,Maria J G T Vehreschild,Johanna Erber,Christiana Franke,Clemens Warnke +33 more
TL;DR: In this paper, the effects of prior neurological diseases and the effect of neurological symptoms on outcome were studied using multivariate logistic regression using real-world data from a multinational registry.
Journal ArticleDOI
Obesity and Impaired Metabolic Health Increase Risk of COVID-19-Related Mortality in Young and Middle-Aged Adults to the Level Observed in Older People: The LEOSS Registry
Norbert Stefan,Katrin Sippel,Martin Heni,Andreas Fritsche,Robert Wagner,Carolin Jakob,Hubert Preissl,Alexander von Werder,Yascha Khodamoradi,Stefan Borgmann,Maria Rüthrich,Frank Hanses,Martina Haselberger,C. Piepel,Martin Hower,Jürgen vom Dahl,Kai Wille,Christoph Römmele,Janne Vehreschild,Melanie Stecher,Michele Solimena,Michael Roden,Annette Schürmann,Baptist Gallwitz,Martin Hrabé de Angelis,David S. Ludwig,Matthias B. Schulze,Bjoern Jensen,Andreas L. Birkenfeld +28 more
TL;DR: The modifiable risk factors obesity, diabetes and hypertension increase the risk of COVID-19-related mortality in young and middle-aged patients to the level of risk observed in advanced age, specifically in the absence of obesity and impaired metabolic health.
Journal ArticleDOI
Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning.
Carolin Jakob,Ujjwal M. Mahajan,Marcus Oswald,Melanie Stecher,M. Schons,Julia Mayerle,Siegbert Rieg,Mathias W. Pletz,Uta Merle,Kai Wille,Stefan Borgmann,Christoph D. Spinner,Sebastian Dolff,Clemens Scherer,Lisa Pilgram,Maria Rüthrich,Frank Hanses,Martin Hower,Richard Strauß,Steffen Massberg,Ahmet Görkem Er,Norma Jung,Jörg Janne Vehreschild,Jörg Janne Vehreschild,Hans Stubbe,Lukas Tometten,Rainer König +26 more
TL;DR: A machine learning-based predictor model and a clinical score are presented for identifying patients at risk of developing advanced COVID-19 and better prioritizing patients in need for hospitalization.