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","120"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Molecular Medicine"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Begemann, Martin"],["dc.contributor.author","Gross, Oliver"],["dc.contributor.author","Wincewicz, Dominik"],["dc.contributor.author","Hardeland, Rüdiger"],["dc.contributor.author","Daguano Gastaldi, Vinicius"],["dc.contributor.author","Vieta, Eduard"],["dc.contributor.author","Weissenborn, Karin"],["dc.contributor.author","Miskowiak, Kamilla W."],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Ehrenreich, Hannelore"],["dc.date.accessioned","2021-12-01T09:23:59Z"],["dc.date.accessioned","2022-08-16T12:53:14Z"],["dc.date.available","2021-12-01T09:23:59Z"],["dc.date.available","2022-08-16T12:53:14Z"],["dc.date.issued","2021-09-26"],["dc.date.updated","2022-07-29T12:18:56Z"],["dc.description.abstract","Background Since fall 2019, SARS-CoV-2 spread world-wide, causing a major pandemic with estimated ~ 220 million subjects affected as of September 2021. Severe COVID-19 is associated with multiple organ failure, particularly of lung and kidney, but also grave neuropsychiatric manifestations. Overall mortality reaches > 2%. Vaccine development has thrived in thus far unreached dimensions and will be one prerequisite to terminate the pandemic. Despite intensive research, however, few treatment options for modifying COVID-19 course/outcome have emerged since the pandemic outbreak. Additionally, the substantial threat of serious downstream sequelae, called \\‘long COVID\\’ and \\‘neuroCOVID\\’, becomes increasingly evident. Main body of the abstract Among candidates that were suggested but did not yet receive appropriate funding for clinical trials is recombinant human erythropoietin. Based on accumulating experimental and clinical evidence, erythropoietin is expected to (1) improve respiration/organ function, (2) counteract overshooting inflammation, (3) act sustainably neuroprotective/neuroregenerative. Recent counterintuitive findings of decreased serum erythropoietin levels in severe COVID-19 not only support a relative deficiency of erythropoietin in this condition, which can be therapeutically addressed, but also made us coin the term \\‘hypoxia paradox\\’. As we review here, this paradox is likely due to uncoupling of physiological hypoxia signaling circuits, mediated by detrimental gene products of SARS-CoV-2 or unfavorable host responses, including microRNAs or dysfunctional mitochondria. Substitution of erythropoietin might overcome this \\‘hypoxia paradox\\’ caused by deranged signaling and improve survival/functional status of COVID-19 patients and their long-term outcome. As supporting hints, embedded in this review, we present 4 male patients with severe COVID-19 and unfavorable prognosis, including predicted high lethality, who all profoundly improved upon treatment which included erythropoietin analogues. Short conclusion Substitution of EPO may—among other beneficial EPO effects in severe COVID-19—circumvent downstream consequences of the \\‘hypoxia paradox\\’. A double-blind, placebo-controlled, randomized clinical trial for proof-of-concept is warranted."],["dc.identifier.citation","Molecular Medicine. 2021 Sep 26;27(1):120"],["dc.identifier.doi","10.1186/s10020-021-00381-5"],["dc.identifier.pii","381"],["dc.identifier.pmid","34565332"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94813"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112749"],["dc.identifier.url","https://rdp.sfb274.de/literature/publications/44"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation","TRR 274: Checkpoints of Central Nervous System Recovery"],["dc.relation","TRR 274 | C01: Oligodendroglial NMDA receptors and NMDAR1 autoantibodies as determinants of axonal integrity in neuropsychiatric disease"],["dc.relation.eissn","1528-3658"],["dc.relation.issn","1076-1551"],["dc.relation.workinggroup","RG Ehrenreich (Clinical Neuroscience)"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Recombinant human EPO"],["dc.subject","Darbepoetin"],["dc.subject","Neuroprotection"],["dc.subject","Treatment"],["dc.subject","Signaling"],["dc.subject","Critical care"],["dc.subject","Outcome"],["dc.title","Addressing the ‘hypoxia paradox’ in severe COVID-19: literature review and report of four cases treated with erythropoietin analogues"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","overview_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]