Scheithauer, Simone

[["dc.bibliographiccitation.firstpage","134"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Der Gynäkologe"],["dc.bibliographiccitation.lastpage","138"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Artelt, Tanja"],["dc.contributor.author","Kaase, Martin"],["dc.contributor.author","Scheithauer, Simone"],["dc.date.accessioned","2020-12-10T14:08:43Z"],["dc.date.available","2020-12-10T14:08:43Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1007/s00129-016-4011-1"],["dc.identifier.eissn","1433-0393"],["dc.identifier.issn","0017-5994"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/70529"],["dc.language.iso","de"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Infektiologische Herausforderungen nach Migration"],["dc.title.alternative","Challenges regarding infectious diseases in migrants. Special aspects in the care of female refugees"],["dc.title.subtitle","Besonderheiten bei der Betreuung weiblicher Flüchtlinge"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","634"],["dc.bibliographiccitation.issue","09"],["dc.bibliographiccitation.journal","Deutsche medizinische Wochenschrift"],["dc.bibliographiccitation.lastpage","642"],["dc.bibliographiccitation.volume","143"],["dc.contributor.author","Scheithauer, Simone"],["dc.contributor.author","Kaase, Martin"],["dc.date.accessioned","2020-12-10T18:12:26Z"],["dc.date.available","2020-12-10T18:12:26Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1055/s-0043-115622"],["dc.identifier.eissn","1439-4413"],["dc.identifier.issn","0012-0472"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/74372"],["dc.language.iso","de"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Prävention und Diagnostik multiresistenter Erreger"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Canadian Journal of Infectious Diseases and Medical Microbiology"],["dc.bibliographiccitation.lastpage","5"],["dc.bibliographiccitation.volume","2018"],["dc.contributor.author","Artelt, Tanja"],["dc.contributor.author","Kaase, Martin"],["dc.contributor.author","Bley, Ivonne"],["dc.contributor.author","Eiffert, Helmut"],["dc.contributor.author","Mellmann, Alexander"],["dc.contributor.author","Küster, Helmut"],["dc.contributor.author","Lange, Martina"],["dc.contributor.author","Scheithauer, Simone"],["dc.date.accessioned","2020-12-10T18:37:41Z"],["dc.date.available","2020-12-10T18:37:41Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1155/2018/1525072"],["dc.identifier.eissn","1918-1493"],["dc.identifier.issn","1712-9532"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77063"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Transmission Risk on a Neonatal Intensive Care Unit: Escherichia coli versus Klebsiella pneumoniae"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2123"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Age and Ageing"],["dc.bibliographiccitation.lastpage","2132"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Dohrendorf, Carla Maria"],["dc.contributor.author","Unkel, Steffen"],["dc.contributor.author","Scheithauer, Simone"],["dc.contributor.author","Kaase, Martin"],["dc.contributor.author","Meier, Volker"],["dc.contributor.author","Fenz, Diana"],["dc.contributor.author","Sasse, Jürgen"],["dc.contributor.author","Wappler, Manfred"],["dc.contributor.author","Schweer-Herzig, Jutta"],["dc.contributor.author","Friede, Tim"],["dc.contributor.author","Seele, Jana"],["dc.date.accessioned","2022-02-01T10:31:20Z"],["dc.date.available","2022-02-01T10:31:20Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract Objectives To reduce infections with Clostridioides difficile (CDI) in geriatric patients by interventions easily implementable in standard clinical care. Methods Prevalence and incidence of CDI between January 2015 and February 2020 were analysed (n = 25,311 patients). Pre-intervention status was assessed from April 2016 to March 2017 (n = 4,922). Between May 2017 and August 2019, a monocentric interventional crossover study (n = 4,655) was conducted including standard care and three interventions: (A) sporicidal cleaning of hospital wards, (B) probiotics and (C) improvement in personal hygiene for CDI patients. This was followed by a multicentric comparison of the interventional bundle (A + B + C) between September 2019 and February 2020 (n = 2,593) with the pre-intervention phase. In 98 CDI cases and matched controls individual risk factors for the development of CDI were compared. Results Time series analyses of CDI cases revealed a reduction in the prevalence of CDI in all three participating centres prior to the multicentric intervention phase. In the monocentric phase, no effect of individual interventions on CDI prevalence was identified. However, an aggregated analysis of CDI cases comparing the pre-intervention and the multicentric phase revealed a significant reduction in CDI prevalence. Risk factors for the development of CDI included use of antibiotics, anticoagulants, previous stay in long-term care facilities, prior hospital admissions, cardiac and renal failure, malnutrition and anaemia. Conclusions The observed reduction in CDI may be attributed to heightened awareness of the study objectives and specific staff training. Individual interventions did not appear to reduce CDI prevalence. A further randomised trial would be necessary to confirm whether the bundle of interventions is truly effective."],["dc.description.abstract","Abstract Objectives To reduce infections with Clostridioides difficile (CDI) in geriatric patients by interventions easily implementable in standard clinical care. Methods Prevalence and incidence of CDI between January 2015 and February 2020 were analysed (n = 25,311 patients). Pre-intervention status was assessed from April 2016 to March 2017 (n = 4,922). Between May 2017 and August 2019, a monocentric interventional crossover study (n = 4,655) was conducted including standard care and three interventions: (A) sporicidal cleaning of hospital wards, (B) probiotics and (C) improvement in personal hygiene for CDI patients. This was followed by a multicentric comparison of the interventional bundle (A + B + C) between September 2019 and February 2020 (n = 2,593) with the pre-intervention phase. In 98 CDI cases and matched controls individual risk factors for the development of CDI were compared. Results Time series analyses of CDI cases revealed a reduction in the prevalence of CDI in all three participating centres prior to the multicentric intervention phase. In the monocentric phase, no effect of individual interventions on CDI prevalence was identified. However, an aggregated analysis of CDI cases comparing the pre-intervention and the multicentric phase revealed a significant reduction in CDI prevalence. Risk factors for the development of CDI included use of antibiotics, anticoagulants, previous stay in long-term care facilities, prior hospital admissions, cardiac and renal failure, malnutrition and anaemia. Conclusions The observed reduction in CDI may be attributed to heightened awareness of the study objectives and specific staff training. Individual interventions did not appear to reduce CDI prevalence. A further randomised trial would be necessary to confirm whether the bundle of interventions is truly effective."],["dc.identifier.doi","10.1093/ageing/afab169"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/98833"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-517"],["dc.relation.eissn","1468-2834"],["dc.relation.issn","0002-0729"],["dc.title","Reduced Clostridioides difficile infections in hospitalised older people through multiple quality improvement strategies"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","10556"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Wulff, Antje"],["dc.contributor.author","Baier, Claas"],["dc.contributor.author","Ballout, Sarah"],["dc.contributor.author","Tute, Erik"],["dc.contributor.author","Sommer, Kim Katrin"],["dc.contributor.author","Kaase, Martin"],["dc.contributor.author","Sargeant, Anneka"],["dc.contributor.author","Drenkhahn, Cora"],["dc.contributor.author","Schlüter, Dirk"],["dc.contributor.author","Scheithauer, Simone"],["dc.contributor.authorgroup","Infection Control Study Group"],["dc.date.accessioned","2021-07-05T15:00:33Z"],["dc.date.available","2021-07-05T15:00:33Z"],["dc.date.issued","2021"],["dc.description.abstract","Abstract The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems."],["dc.description.abstract","Abstract The spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems."],["dc.identifier.doi","10.1038/s41598-021-89796-y"],["dc.identifier.pii","89796"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87850"],["dc.language.iso","en"],["dc.notes.intern","DOI Import DOI-Import GROB-441"],["dc.relation.eissn","2045-2322"],["dc.title","Transformation of microbiology data into a standardised data representation using OpenEHR"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]