M
M. Schons
Researcher at University of Cologne
Publications - 14
Citations - 302
M. Schons is an academic researcher from University of Cologne. The author has contributed to research in topics: Medicine & Cohort. The author has an hindex of 4, co-authored 10 publications receiving 92 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 Article
COVID-19 in cancer patients: Clinical characteristics and outcome - a first analysis of the LEOSS registry
Maria Madeleine Ruethrich,G. Kniele,Lukas Tometten,Stefan Borgmann,J. Schneider,Sebastian Dolff,Frank Hanses,J. Norma,N. Isberner,M. Wettstein,Annika Y. Classen,M. Schons,Jorg-Janne Vehreschild,C. Giessen-Jung,Gernot Beutel,M. von Lilienfeld-Toal,Kai Wille +16 more
TL;DR: Comparing cancer and non-cancer patients, outcome of COVID-19 was comparable after adjusting for age, sex, and comorbidity, but cancer patients as a group are at higher risk due to advanced age and pre-existing conditions.
Journal ArticleDOI
First results of the "Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS)".
Carolin Jakob,Stefan Borgmann,Fazilet Duygu,Uta Behrends,Martin Hower,Uta Merle,Anette K. Friedrichs,Lukas Tometten,Frank Hanses,Norma Jung,Siegbert Rieg,Kai Wille,Beate Grüner,Hartwig Klinker,Nicole Gersbacher-Runge,Kerstin Hellwig,Lukas Eberwein,Sebastian Dolff,Dominic Rauschning,Michael von Bergwelt-Baildon,Julia Lanznaster,Richard Strauß,Janina Trauth,Maria Madeleine Ruethrich,Catherina Lueck,Jacob Nattermann,Lene Tscharntke,Lisa Pilgram,Sandra Fuhrmann,Annika Y. Classen,Melanie Stecher,M. Schons,Christoph D. Spinner,Jörg Janne Vehreschild,Jörg Janne Vehreschild +34 more
TL;DR: The LEOSS cohort identified age, cardiovascular disease, diabetes and male sex as risk factors for complicated disease stages at SARS-CoV-2 diagnosis, thus confirming previous data.
Journal ArticleDOI
Long-term health sequelae and quality of life at least 6 months after infection with SARS-CoV-2: design and rationale of the COVIDOM-study as part of the NAPKON population-based cohort platform (POP).
A. Horn,Lilian Krist,Wolfgang Lieb,Felipe A. Montellano,M. Kohls,Kirsten Haas,G. Gelbrich,S. J. Bolay-Gehrig,Caroline Morbach,J. P. Reese,Stefan Störk,Julia Fricke,Thomas Zoller,Thomas Zoller,Sein Schmidt,P. Triller,Lucie Kretzler,M. Rönnefarth,C. Von Kalle,S.N. Willich,Florian Kurth,Florian Kurth,Fridolin Steinbeis,Martin Witzenrath,Thomas Bahmer,A. Hermes,M. Krawczak,L. Reinke,C. Maetzler,J. Franzenburg,J. Enderle,A. Flinspach,Jorg-Janne Vehreschild,M. Schons,Thomas Illig,G. Anton,Kathrin Ungethüm,B. C. Finkenberg,M. T. Gehrig,N. Savaskan,Peter U. Heuschmann,Thomas Keil,Thomas Keil,Stefan Schreiber +43 more
TL;DR: The COVIDOM-study within the population-based cohort platform (POP) which has been established under the auspices of the NAPKON infrastructure (German National Pandemic Cohort Network) of the national Network University Medicine (NUM) as discussed by the authors aimed to systematically assess the long-term health status of samples of hospitalized and non-hospitalized SARS-CoV-2 infected individuals from three regions in Germany.
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.