Meissner, Konrad

[["dc.bibliographiccitation.firstpage","2187"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Intensive Care Medicine"],["dc.bibliographiccitation.lastpage","2196"],["dc.bibliographiccitation.volume","46"],["dc.contributor.author","Chiumello, Davide"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Coppola, Silvia"],["dc.contributor.author","Romitti, Federica"],["dc.contributor.author","Formenti, Paolo"],["dc.contributor.author","Bonifazi, Matteo"],["dc.contributor.author","Pozzi, Tommaso"],["dc.contributor.author","Palumbo, Maria Michela"],["dc.contributor.author","Cressoni, Massimo"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Camporota, Luigi"],["dc.contributor.author","Marini, John J."],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2021-04-14T08:32:14Z"],["dc.date.available","2021-04-14T08:32:14Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1007/s00134-020-06281-2"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83854"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1432-1238"],["dc.relation.issn","0342-4642"],["dc.title","Physiological and quantitative CT-scan characterization of COVID-19 and typical ARDS: a matched cohort study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","46"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of Clinical Medicine"],["dc.bibliographiccitation.volume","9"],["dc.contributor.affiliation","Mewes, Caspar; \t\t \r\n\t\t Department of Anesthesiology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, caspar.mewes@med.uni-goettingen.de"],["dc.contributor.affiliation","Böhnke, Carolin; \t\t \r\n\t\t Department of Anesthesiology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, boehnke.carolin@web.de"],["dc.contributor.affiliation","Alexander, Tessa; \t\t \r\n\t\t Department of Anesthesiology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, tessa.alexander@med.uni-goettingen.de"],["dc.contributor.affiliation","Büttner, Benedikt; \t\t \r\n\t\t Department of Anesthesiology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, benedikt.buettner@med.uni-goettingen.de"],["dc.contributor.affiliation","Hinz, José; \t\t \r\n\t\t Department of Anesthesiology and Intensive Care Medicine, Klinikum Region Hannover, D-30459 Hannover, Germany, jose.hinz@krh.eu"],["dc.contributor.affiliation","Popov, Aron-Frederik; \t\t \r\n\t\t Department of Thoracic and Cardiovascular Surgery, University Medical Center, Eberhard Karls University, D-72076 Tuebingen, Germany, aronf.popov@gmail.com"],["dc.contributor.affiliation","Ghadimi, Michael; \t\t \r\n\t\t Department of General and Visceral Surgery, University Medical Center, Georg August University, D-37075 Goettingen, Germany, mghadim@uni-goettingen.de"],["dc.contributor.affiliation","Beißbarth, Tim; \t\t \r\n\t\t Institute of Medical Bioinformatics, University Medical Center, Georg August University, D-37077 Goettingen, Germany, tim.beissbarth@ams.med.uni-goettingen.de"],["dc.contributor.affiliation","Raddatz, Dirk; \t\t \r\n\t\t Department of Gastroenterology and Gastrointestinal Oncology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, draddat@gwdg.de"],["dc.contributor.affiliation","Meissner, Konrad; \t\t \r\n\t\t Department of Anesthesiology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, konrad.meissner@med.uni-goettingen.de"],["dc.contributor.affiliation","Quintel, Michael; \t\t \r\n\t\t Department of Anesthesiology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, mquintel@med.uni-goettingen.de"],["dc.contributor.affiliation","Bergmann, Ingo; \t\t \r\n\t\t Department of Anesthesiology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, ingo.bergmann@med.uni-goettingen.de"],["dc.contributor.affiliation","Mansur, Ashham; \t\t \r\n\t\t Department of Anesthesiology, University Medical Center, Georg August University, D-37075 Goettingen, Germany, ashham.mansur@med.uni-goettingen.de"],["dc.contributor.author","Mewes, Caspar"],["dc.contributor.author","Böhnke, Carolin"],["dc.contributor.author","Alexander, Tessa"],["dc.contributor.author","Popov, Aron-Frederik"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Büttner, Benedikt"],["dc.contributor.author","Hinz, José"],["dc.contributor.author","Ghadimi, Michael"],["dc.contributor.author","Raddatz, Dirk"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Bergmann, Ingo"],["dc.contributor.author","Mansur, Ashham"],["dc.date.accessioned","2020-04-02T10:35:27Z"],["dc.date.available","2020-04-02T10:35:27Z"],["dc.date.issued","2019-12-24"],["dc.date.updated","2022-02-09T13:22:24Z"],["dc.description.abstract","Septic shock is a frequent life-threatening condition and a leading cause of mortality in intensive care units (ICUs). Previous investigations have reported a potentially protective effect of obesity in septic shock patients. However, prior results have been inconsistent, focused on short-term in-hospital mortality and inadequately adjusted for confounders, and they have rarely applied the currently valid Sepsis-3 definition criteria for septic shock. This investigation examined the effect of obesity on 90-day mortality in patients with septic shock selected from a prospectively enrolled cohort of septic patients. A total of 352 patients who met the Sepsis-3 criteria for septic shock were enrolled in this study. Body-mass index (BMI) was used to divide the cohort into 24% obese (BMI ≥ 30 kg/m2) and 76% non-obese (BMI < 30 kg/m2) patients. Kaplan-Meier survival analysis revealed a significantly lower 90-day mortality (31% vs. 43%; p = 0.0436) in obese patients compared to non-obese patients. Additional analyses of baseline characteristics, disease severity, and microbiological findings outlined further statistically significant differences among the groups. Multivariate Cox regression analysis estimated a significant protective effect of obesity on 90-day mortality after adjustment for confounders. An understanding of the underlying physiologic mechanisms may improve therapeutic strategies and patient prognosis."],["dc.description.sponsorship","University of Goettingen"],["dc.identifier.doi","10.3390/jcm9010046"],["dc.identifier.eissn","2077-0383"],["dc.identifier.pmid","31878238"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17053"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63513"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2077-0383"],["dc.relation.issn","2077-0383"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Favorable 90-Day Mortality in Obese Caucasian Patients with Septic Shock According to the Sepsis-3 Definition"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","210138"],["dc.bibliographiccitation.issue","162"],["dc.bibliographiccitation.journal","European Respiratory Review"],["dc.bibliographiccitation.volume","30"],["dc.contributor.author","Gattinoni, Luciano"],["dc.contributor.author","Gattarello, Simone"],["dc.contributor.author","Steinberg, Irene"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Palermo, Paola"],["dc.contributor.author","Lazzari, Stefano"],["dc.contributor.author","Romitti, Federica"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Marini, John J."],["dc.contributor.author","Camporota, Luigi"],["dc.date.accessioned","2022-04-01T10:02:45Z"],["dc.date.available","2022-04-01T10:02:45Z"],["dc.date.issued","2021"],["dc.description.abstract","Coronavirus disease 2019 (COVID-19) pneumonia is an evolving disease. We will focus on the development of its pathophysiologic characteristics over time, and how these time-related changes determine modifications in treatment. In the emergency department: the peculiar characteristic is the coexistence, in a significant fraction of patients, of severe hypoxaemia, near-normal lung computed tomography imaging, lung gas volume and respiratory mechanics. Despite high respiratory drive, dyspnoea and respiratory rate are often normal. The underlying mechanism is primarily altered lung perfusion. The anatomical prerequisites for PEEP (positive end-expiratory pressure) to work (lung oedema, atelectasis, and therefore recruitability) are lacking. In the high-dependency unit: the disease starts to worsen either because of its natural evolution or additional patient self-inflicted lung injury (P-SILI). Oedema and atelectasis may develop, increasing recruitability. Noninvasive supports are indicated if they result in a reversal of hypoxaemia and a decreased inspiratory effort. Otherwise, mechanical ventilation should be considered to avert P-SILI. In the intensive care unit: the primary characteristic of the advance of unresolved COVID-19 disease is a progressive shift from oedema or atelectasis to less reversible structural lung alterations to lung fibrosis. These later characteristics are associated with notable impairment of respiratory mechanics, increased arterial carbon dioxide tension ( P aCO 2 ), decreased recruitability and lack of response to PEEP and prone positioning."],["dc.identifier.doi","10.1183/16000617.0138-2021"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105996"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.eissn","1600-0617"],["dc.relation.issn","0905-9180"],["dc.rights.uri","http://creativecommons.org/licenses/by-nc/4.0/"],["dc.title","COVID-19 pneumonia: pathophysiology and management"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Physiology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Cressoni, Massimo"],["dc.contributor.author","Lotz, Joachim"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Saager, Leif"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2021-12-01T09:24:03Z"],["dc.date.available","2021-12-01T09:24:03Z"],["dc.date.issued","2021"],["dc.description.abstract","Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R 2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically."],["dc.description.abstract","Knowledge of gas volume, tissue mass and recruitability measured by the quantitative CT scan analysis (CT-qa) is important when setting the mechanical ventilation in acute respiratory distress syndrome (ARDS). Yet, the manual segmentation of the lung requires a considerable workload. Our goal was to provide an automatic, clinically applicable and reliable lung segmentation procedure. Therefore, a convolutional neural network (CNN) was used to train an artificial intelligence (AI) algorithm on 15 healthy subjects (1,302 slices), 100 ARDS patients (12,279 slices), and 20 COVID-19 (1,817 slices). Eighty percent of this populations was used for training, 20% for testing. The AI and manual segmentation at slice level were compared by intersection over union (IoU). The CT-qa variables were compared by regression and Bland Altman analysis. The AI-segmentation of a single patient required 5–10 s vs. 1–2 h of the manual. At slice level, the algorithm showed on the test set an IOU across all CT slices of 91.3 ± 10.0, 85.2 ± 13.9, and 84.7 ± 14.0%, and across all lung volumes of 96.3 ± 0.6, 88.9 ± 3.1, and 86.3 ± 6.5% for normal lungs, ARDS and COVID-19, respectively, with a U-shape in the performance: better in the lung middle region, worse at the apex and base. At patient level, on the test set, the total lung volume measured by AI and manual segmentation had a R 2 of 0.99 and a bias −9.8 ml [CI: +56.0/−75.7 ml]. The recruitability measured with manual and AI-segmentation, as change in non-aerated tissue fraction had a bias of +0.3% [CI: +6.2/−5.5%] and −0.5% [CI: +2.3/−3.3%] expressed as change in well-aerated tissue fraction. The AI-powered lung segmentation provided fast and clinically reliable results. It is able to segment the lungs of seriously ill ARDS patients fully automatically."],["dc.identifier.doi","10.3389/fphys.2021.676118"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94836"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-042X"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Using Artificial Intelligence for Automatic Segmentation of CT Lung Images in Acute Respiratory Distress Syndrome"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","21"],["dc.bibliographiccitation.journal","Intensive Care Medicine Experimental"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Bonifazi, Matteo"],["dc.contributor.author","Romitti, Federica"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Palumbo, Maria Michela"],["dc.contributor.author","Steinberg, Irene"],["dc.contributor.author","Gattarello, Simone"],["dc.contributor.author","Palermo, Paola"],["dc.contributor.author","Saager, Leif"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Chiumello, Davide"],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2022-07-29T10:03:15Z"],["dc.date.available","2022-07-29T10:03:15Z"],["dc.date.issued","2021-04-19"],["dc.description.abstract","The physiological dead space is a strong indicator of severity and outcome of acute respiratory distress syndrome (ARDS). The \"ideal\" alveolar PCO2, in equilibrium with pulmonary capillary PCO2, is a central concept in the physiological dead space measurement. As it cannot be measured, it is surrogated by arterial PCO2 which, unfortunately, may be far higher than ideal alveolar PCO2, when the right-to-left venous admixture is present. The \"ideal\" alveolar PCO2 equals the end-tidal PCO2 (PETCO2) only in absence of alveolar dead space. Therefore, in the perfect gas exchanger (alveolar dead space = 0, venous admixture = 0), the PETCO2/PaCO2 is 1, as PETCO2, PACO2 and PaCO2 are equal. Our aim is to investigate if and at which extent the PETCO2/PaCO2, a comprehensive meter of the \"gas exchanger\" performance, is related to the anatomo physiological characteristics in ARDS."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1186/s40635-021-00377-9"],["dc.identifier.pmid","33871738"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112553"],["dc.language.iso","en"],["dc.relation.issn","2197-425X"],["dc.rights","CC BY 4.0"],["dc.title","End-tidal to arterial PCO2 ratio: a bedside meter of the overall gas exchanger performance"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Physiology"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Hülsmann, Swen"],["dc.contributor.author","Khabbazzadeh, Sepideh"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.date.accessioned","2021-04-14T08:29:50Z"],["dc.date.available","2021-04-14T08:29:50Z"],["dc.date.issued","2021"],["dc.description.abstract","Acute respiratory distress syndrome (ARDS) represents an acute diffuse inflammation of the lungs triggered by different causes, uniformly leading to a noncardiogenic pulmonary edema with inhomogeneous densities in lung X-ray and lung CT scan and acute hypoxemia. Edema formation results in “heavy” lungs, inducing loss of compliance and the need to spend more energy to “move” the lungs. Consequently, an ARDS patient, as long as the patient is breathing spontaneously, has an increased respiratory drive to ensure adequate oxygenation and CO2 removal. One would expect that, once the blood gases get back to “physiological” values, the respiratory drive would normalize and the breathing effort return to its initial status. However, in many ARDS patients, this is not the case; their respiratory drive appears to be upregulated and fully or at least partially detached from the blood gas status. Strikingly, similar alteration of the respiratory drive can be seen in patients suffering from SARS, especially SARS-Covid-19. We hypothesize that alterations of the renin-angiotensin-system (RAS) related to the pathophysiology of ARDS and SARS are involved in this dysregulation of chemosensitive control of breathing."],["dc.identifier.doi","10.3389/fphys.2020.588248"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83000"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1664-042X"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","A Potential Role of the Renin-Angiotensin-System for Disturbances of Respiratory Chemosensitivity in Acute Respiratory Distress Syndrome and Severe Acute Respiratory Syndrome"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Critical Care Medicine"],["dc.contributor.author","Thair, Simone"],["dc.contributor.author","Mewes, Caspar"],["dc.contributor.author","Hinz, José"],["dc.contributor.author","Bergmann, Ingo"],["dc.contributor.author","Büttner, Benedikt"],["dc.contributor.author","Sehmisch, Stephan"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Sweeney, Timothy E."],["dc.contributor.author","Mansur, Ashham"],["dc.date.accessioned","2021-06-01T10:46:57Z"],["dc.date.available","2021-06-01T10:46:57Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1097/CCM.0000000000005027"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85430"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.issn","0090-3493"],["dc.title","Gene Expression–Based Diagnosis of Infections in Critically Ill Patients—Prospective Validation of the SepsisMetaScore in a Longitudinal Severe Trauma Cohort"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","318"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","American Journal of Respiratory and Critical Care Medicine"],["dc.bibliographiccitation.lastpage","327"],["dc.bibliographiccitation.volume","203"],["dc.contributor.author","Giosa, Lorenzo"],["dc.contributor.author","Busana, Mattia"],["dc.contributor.author","Bonifazi, Matteo"],["dc.contributor.author","Romitti, Federica"],["dc.contributor.author","Vassalli, Francesco"],["dc.contributor.author","Pasticci, Iacopo"],["dc.contributor.author","Macrì, Matteo Maria"],["dc.contributor.author","D’Albo, Rosanna"],["dc.contributor.author","Collino, Francesca"],["dc.contributor.author","Gatta, Alessandro"],["dc.contributor.author","Palumbo, Maria Michela"],["dc.contributor.author","Herrmann, Peter"],["dc.contributor.author","Moerer, Onnen"],["dc.contributor.author","Iapichino, Gaetano"],["dc.contributor.author","Meissner, Konrad"],["dc.contributor.author","Quintel, Michael"],["dc.contributor.author","Gattinoni, Luciano"],["dc.date.accessioned","2021-04-14T08:29:57Z"],["dc.date.available","2021-04-14T08:29:57Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1164/rccm.202005-1687OC"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83049"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1535-4970"],["dc.relation.issn","1073-449X"],["dc.title","Mobilizing Carbon Dioxide Stores. An Experimental Study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]