25gmds202 10.3205/25gmds202 urn:nbn:de:0183-25gmds2021 Meeting Abstract Oswald Oswald Marcus M

Universitätsklinikum Jena, Jena, Germany

author Pletz Pletz Mathias M

Institute for Infectious Diseases and Infection Control, Jena University Hospital, Jena, Germany

author König König Rainer R

Universitätsklinikum Jena, Jena, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 pneumonia fluoroquinolones machine learning 20260401 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 202 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) PS 3: Epidemiologie 1 Jena 20250907 20250911 Abstr. 284 TextIntroduction: Selecting the appropriate antibiotics treatment against moderate pneumonia is still a matter of debate. To develop a treatment rule for the decision of a beta-lactam or a fluoroquinolone antibiotic, we develop machine learning (ML) methods based on scoring models, trained on ex ante available etiological and clinical parameters. For propensity balancing, we compute an expected mortality for each patient by logistic regression and take the difference to the real mortality (90 days).Methods: We consider seasonal effects and display time-lapse characteristics of more than 10,000 hospitalised patients with moderate severity (non-intensive care unit patients) from the observational, prospective, multinational CAPNETZ study . Incidence data of Influenza infection is derived from publicly available data of the Robert Koch Institute (https://survstat.rki.de/). During the years more and more numbers of cases are reported. This is corrected by computing the linear regression slope of the logarithm of the case numbers and subtracting it from the data. Now the Influenza seasons of all years are comparable. In addition we construct daily case numbers from the weekly published RKI data by interpolation.Results: We observe seasonal effects linked to seasonal Influenza incidences. We observe an increasing severity during and directly after the Influenza peaks, and, at the same periods, rather low mortality of patients treated with fluoroquinolones.Conclusion: The seasonal effects on Fluorquinolone treatment are interesting and need elaboration on explaining these effects.Members of the CAPNETZ study group are: M. Dreher, C. Cornelissen (Aachen); W. Knüppel (Bad Arolsen); D. Stolz (Basel); N. Suttorp, M. Witzenrath, P. Creutz, A. Mikolajewska (Berlin, Charité); T. Bauer, D. Krieger (Berlin); W. Pankow, D. Thiemig (Berlin-Neukölln); B. Hauptmeier, S. Ewig, D. Wehde (Bochum); M. Prediger, S. Schmager (Cottbus); M. Kolditz, B. Schulte-Hubbert, S. Langner (Dresden); W. Albrich (St Gallen); T. Welte, J. Freise, G. Barten, O. Arenas Toro, M. Nawrocki, J. Naim, M. Witte, W. Kröner, T. Illig, N. Klopp (Hannover); M. Kreuter, F. Herth, S. Hummler (Heidelberg); P. Ravn, A. Vestergaard-Jensen, G. Baunbaek-Knudsen (Hillerød); M. Pletz, C. Kroegel, J. Frosinski, J. Winning, B. Schleenvoigt ( Jena); K. Dalhoff, J. Rupp, R. Hörster, D. Drömann (Lübeck); G. Rohde, J. Drijkoningen, D. Braeken (Maastricht); H. Buschmann (Paderborn); T. Schaberg, I. Hering (Rotenburg/Wümme); M. Panning (Freiburg); M. Wallner (Ulm).The authors declare that they have no competing interests.The authors declare that an ethics committee vote is not required. Suttorp N Welte T Marre R Stenger S Pletz M Rupp J Das Kompetenzzentrum für ambulant erworbene Pneumonie 2016 Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 475-81 Suttorp N, Welte T, Marre R, Stenger S, Pletz M, Rupp J, et al. Das Kompetenzzentrum für ambulant erworbene Pneumonie [CAPNETZ. The competence network for community-acquired pneumonia (CAP)]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2016;59(4):475-81. 0 0 0 0