Mörer, Onnen

[["dc.bibliographiccitation.artnumber","295"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Critical Care"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Magunia, Harry"],["dc.contributor.author","Lederer, Simone"],["dc.contributor.author","Verbuecheln, Raphael"],["dc.contributor.author","Gilot, Bryant J."],["dc.contributor.author","Koeppen, Michael"],["dc.contributor.author","Haeberle, Helene A."],["dc.contributor.author","Mirakaj, Valbona"],["dc.contributor.author","Hofmann, Pascal"],["dc.contributor.author","Marx, Gernot"],["dc.contributor.author","Bickenbach, Johannes"],["dc.contributor.author","Nohe, Boris"],["dc.contributor.author","Lay, Michael"],["dc.contributor.author","Spies, Claudia"],["dc.contributor.author","Edel, Andreas"],["dc.contributor.author","Schiefenhövel, Fridtjof"],["dc.contributor.author","Rahmel, Tim"],["dc.contributor.author","Putensen, Christian"],["dc.contributor.author","Sellmann, Timur"],["dc.contributor.author","Koch, Thea"],["dc.contributor.author","Brandenburger, Timo"],["dc.contributor.author","Kindgen-Milles, Detlef"],["dc.contributor.author","Brenner, Thorsten"],["dc.contributor.author","Berger, Marc"],["dc.contributor.author","Zacharowski, Kai"],["dc.contributor.author","Adam, Elisabeth"],["dc.contributor.author","Posch, Matthias"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Scheer, Christian S."],["dc.contributor.author","Sedding, Daniel"],["dc.contributor.author","Weigand, Markus A."],["dc.contributor.author","Fichtner, Falk"],["dc.contributor.author","Nau, Carla"],["dc.contributor.author","Prätsch, Florian"],["dc.contributor.author","Wiesmann, Thomas"],["dc.contributor.author","Koch, Christian"],["dc.contributor.author","Schneider, Gerhard"],["dc.contributor.author","Lahmer, Tobias"],["dc.contributor.author","Straub, Andreas"],["dc.contributor.author","Meiser, Andreas"],["dc.contributor.author","Weiss, Manfred"],["dc.contributor.author","Jungwirth, Bettina"],["dc.contributor.author","Wappler, Frank"],["dc.contributor.author","Meybohm, Patrick"],["dc.contributor.author","Herrmann, Johannes"],["dc.contributor.author","Malek, Nisar"],["dc.contributor.author","Kohlbacher, Oliver"],["dc.contributor.author","Biergans, Stephanie"],["dc.contributor.author","Rosenberger, Peter"],["dc.date.accessioned","2021-11-25T11:13:27Z"],["dc.date.accessioned","2022-08-18T12:40:20Z"],["dc.date.available","2021-11-25T11:13:27Z"],["dc.date.available","2022-08-18T12:40:20Z"],["dc.date.issued","2021-08-17"],["dc.date.updated","2022-07-29T12:18:15Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.\r\n \r\n \r\n Methods\r\n A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported.\r\n \r\n \r\n Results\r\n 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy.\r\n \r\n \r\n Conclusions\r\n Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models.\r\n Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451."],["dc.identifier.citation","Critical Care. 2021 Aug 17;25(1):295"],["dc.identifier.doi","10.1186/s13054-021-03720-4"],["dc.identifier.pii","3720"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/93542"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112980"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-455"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1364-8535"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","COVID-19"],["dc.subject","Critical care"],["dc.subject","ARDS"],["dc.subject","Outcome"],["dc.subject","Prognostic models"],["dc.title","Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","736"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Infectious Diseases"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Meyer, Eva C."],["dc.contributor.author","Alt-Epping, Sabine"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Büttner, Benedikt"],["dc.date.accessioned","2021-11-25T10:47:25Z"],["dc.date.accessioned","2022-08-16T12:43:01Z"],["dc.date.available","2021-11-25T10:47:25Z"],["dc.date.available","2022-08-16T12:43:01Z"],["dc.date.issued","2021-08-03"],["dc.date.updated","2022-07-29T12:00:23Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Capnocytophaga canimorsus (C. canimorsus) infections are rare and usually present with unspecific symptoms, which can eventually end in fatal septic shock and multiorgan failure. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) related coronavirus disease 2019 (COVID-19), on the other hand, is predominantly characterized by acute respiratory failure, although other organ complications can occur. Both infectious diseases have in common that hyperinflammation with a cytokine storm can occur. While microbial detection of C. canimorsus in blood cultures can take over 48 h, diagnosis of SARS-CoV-2 is facilitated by a widely available rapid antigen diagnostic test (Ag-RDT) the results of which are available within half an hour. These Ag-RDT results are commonly verified by a nucleic acid amplification test (NAAT), whose results are only available after a further 24 h.\r\n \r\n \r\n Case presentation\r\n A 68-year-old male patient with the diagnosis of COVID-19 pneumonia was referred to our Intensive Care Unit (ICU) from another hospital after testing positive on an Ag-RDT. While the initial therapy was focused on COVID-19, the patient developed a fulminant septic shock within a few hours after admission to the ICU, unresponsive to maximum treatment. SARS-CoV-2 NAATs were negative, but bacteremia of C. canimorsus was diagnosed post-mortem. Further anamnestic information suggest that a small skin injury caused by a dog leash or the subsequent contact of this injury with the patient’s dog could be the possible point of entry for these bacteria.\r\n \r\n \r\n Conclusion\r\n During the acute phase of hyperinflammation and cytokine storm, laboratory results can resemble both, sepsis of bacterial origin or SARS-CoV-2. This means that even in the light of a global SARS-CoV-2 pandemic, where this diagnosis provides the most salient train of thoughts, differential diagnoses must be considered. Ag-RDT can contribute to early detection of a SARS-CoV-2 infection, but false-positive results may cause fixation errors with severe consequences for patient outcome."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.citation","BMC Infectious Diseases. 2021 Aug 03;21(1):736"],["dc.identifier.doi","10.1186/s12879-021-06422-y"],["dc.identifier.pii","6422"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/93512"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112741"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-455"],["dc.relation.eissn","1471-2334"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","SARS-CoV-2"],["dc.subject","Rapid antigen diagnostic test"],["dc.subject","Capnocytophaga canimorsus"],["dc.subject","Septic shock"],["dc.subject","COVID-19"],["dc.subject","Case report"],["dc.title","Fatal septic shock due to Capnocytophaga canimorsus bacteremia masquerading as COVID-19 pneumonia - a case report"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]