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","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.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"]]