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","405"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Critical Care"],["dc.bibliographiccitation.volume","25"],["dc.contributor.author","Schmidt, Stefan"],["dc.contributor.author","Dieks, Jana-Katharina"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Moerer, Onnen"],["dc.date.accessioned","2021-12-01T09:23:03Z"],["dc.date.accessioned","2022-08-16T12:51:41Z"],["dc.date.available","2021-12-01T09:23:03Z"],["dc.date.available","2022-08-16T12:51:41Z"],["dc.date.issued","2021-11-24"],["dc.date.updated","2022-07-29T12:18:17Z"],["dc.description.abstract","Background The use of ultrasonography in the intensive care unit (ICU) is steadily increasing but is usually restricted to examinations of single organs or organ systems. In this study, we combine the ultrasound approaches the most relevant to ICU to design a whole-body ultrasound (WBU) protocol. Recommendations and training schemes for WBU are sparse and lack conclusive evidence. Our aim was therefore to define the range and prevalence of abnormalities detectable by WBU to develop a simple and fast bedside examination protocol, and to evaluate the value of routine surveillance WBU in ICU patients. Methods A protocol for focused assessments of sonographic abnormalities of the ocular, vascular, pulmonary, cardiac and abdominal systems was developed to evaluate 99 predefined sonographic entities on the day of admission and on days 3, 6, 10 and 15 of the ICU admission. The study was a clinical prospective single-center trial in 111 consecutive patients admitted to the surgical ICUs of a tertiary university hospital. Results A total of 3003 abnormalities demonstrable by sonography were detected in 1275 individual scans of organ systems and 4395 individual single-organ examinations. The rate of previously undetected abnormalities ranged from 6.4 ± 4.2 on the day of admission to 2.9 ± 1.8 on day 15. Based on the sonographic findings, intensive care therapy was altered following 45.1% of examinations. Mean examination time was 18.7 ± 3.2 min, or 1.6 invested minutes per detected abnormality. Conclusions Performing the WBU protocol led to therapy changes in 45.1% of the time. Detected sonographic abnormalities showed a high rate of change in the course of the serial assessments, underlining the value of routine ultrasound examinations in the ICU. Trial registration The study was registered in the German Clinical Trials Register (DRKS, 7 April 2017; retrospectively registered) under the identifier DRKS00010428."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.citation","Critical Care. 2021 Nov 24;25(1):405"],["dc.identifier.doi","10.1186/s13054-021-03811-2"],["dc.identifier.pii","3811"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94547"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112747"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation.eissn","1364-8535"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","Sonography"],["dc.subject","Ultrasound"],["dc.subject","General critical care ultrasound"],["dc.subject","Whole-body ultrasound"],["dc.subject","Critical care echocardiography"],["dc.subject","Intensive care medicine"],["dc.subject","Critical care"],["dc.title","Development and evaluation of the focused assessment of sonographic pathologies in the intensive care unit (FASP-ICU) protocol"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["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"]]