13 results on '"Edel, Andreas"'
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2. Interdisciplinary perspectives on computed tomography in sepsis: survey among medical doctors at a large university medical center
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Opper Hernando, Maria Isabel, Witham, Denis, Steinhagen, Peter Richard, Angermair, Stefan, Bauer, Wolfgang, Compton, Friederike, Edel, Andreas, Kruse, Jan, Kühnle, York, Lachmann, Gunnar, Marz, Susanne, Müller-Redetzky, Holger, Nee, Jens, Paul, Oliver, Praeger, Damaris, Skurk, Carsten, Stegemann, Miriam, Uhrig, Alexander, Wolf, Stefan, Zimmermann, Elke, Rubarth, Kerstin, Bolanaki, Myrto, Seybold, Joachim, Dewey, Marc, and Pohlan, Julian
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- 2023
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3. A time-resolved proteomic and prognostic map of COVID-19
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Kleinschmidt, Malte, Heim, Katrin M., Millet, Belén, Meyer-Arndt, Lil, Hübner, Ralf H., Andermann, Tim, Doehn, Jan M., Opitz, Bastian, Sawitzki, Birgit, Grund, Daniel, Radünzel, Peter, Schürmann, Mariana, Zoller, Thomas, Alius, Florian, Knape, Philipp, Breitbart, Astrid, Li, Yaosi, Bremer, Felix, Pergantis, Panagiotis, Schürmann, Dirk, Temmesfeld-Wollbrück, Bettina, Wendisch, Daniel, Brumhard, Sophia, Haenel, Sascha S., Conrad, Claudia, Georg, Philipp, Eckardt, Kai-Uwe, Lehner, Lukas, Kruse, Jan M., Ferse, Carolin, Körner, Roland, Spies, Claudia, Edel, Andreas, Weber-Carstens, Steffen, Krannich, Alexander, Zvorc, Saskia, Li, Linna, Behrens, Uwe, Schmidt, Sein, Rönnefarth, Maria, Dang-Heine, Chantip, Röhle, Robert, Lieker, Emma, Kretzler, Lucie, Wirsching, Isabelle, Wollboldt, Christian, Wu, Yinan, Schwanitz, Georg, Hillus, David, Kasper, Stefanie, Olk, Nadine, Horn, Alexandra, Briesemeister, Dana, Treue, Denise, Hummel, Michael, Corman, Victor M., Drosten, Christian, von Kalle, Christof, Demichev, Vadim, Tober-Lau, Pinkus, Lemke, Oliver, Nazarenko, Tatiana, Thibeault, Charlotte, Whitwell, Harry, Röhl, Annika, Freiwald, Anja, Szyrwiel, Lukasz, Ludwig, Daniela, Correia-Melo, Clara, Aulakh, Simran Kaur, Helbig, Elisa T., Stubbemann, Paula, Lippert, Lena J., Grüning, Nana-Maria, Blyuss, Oleg, Vernardis, Spyros, White, Matthew, Messner, Christoph B., Joannidis, Michael, Sonnweber, Thomas, Klein, Sebastian J., Pizzini, Alex, Wohlfarter, Yvonne, Sahanic, Sabina, Hilbe, Richard, Schaefer, Benedikt, Wagner, Sonja, Mittermaier, Mirja, Machleidt, Felix, Garcia, Carmen, Ruwwe-Glösenkamp, Christoph, Lingscheid, Tilman, Bosquillon de Jarcy, Laure, Stegemann, Miriam S., Pfeiffer, Moritz, Jürgens, Linda, Denker, Sophy, Zickler, Daniel, Enghard, Philipp, Zelezniak, Aleksej, Campbell, Archie, Hayward, Caroline, Porteous, David J., Marioni, Riccardo E., Uhrig, Alexander, Müller-Redetzky, Holger, Zoller, Heinz, Löffler-Ragg, Judith, Keller, Markus A., Tancevski, Ivan, Timms, John F., Zaikin, Alexey, Hippenstiel, Stefan, Ramharter, Michael, Witzenrath, Martin, Suttorp, Norbert, Lilley, Kathryn, Mülleder, Michael, Sander, Leif Erik, Ralser, Markus, and Kurth, Florian
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- 2021
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4. Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort
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Magunia, Harry, Lederer, Simone, Verbuecheln, Raphael, Gilot, Bryant Joseph, Koeppen, Michael, Haeberle, Helene A., Mirakaj, Valbona, Hofmann, Pascal, Marx, Gernot, Bickenbach, Johannes, Nohe, Boris, Lay, Michael, Spies, Claudia, Edel, Andreas, Schiefenhövel, Fridtjof, Rahmel, Tim, Putensen, Christian, Sellmann, Timur, Koch, Thea, Brandenburger, Timo, Kindgen-Milles, Detlef, Brenner, Thorsten, Berger, Marc, Zacharowski, Kai, Adam, Elisabeth, Posch, Matthias, Moerer, Onnen, Scheer, Christian S., Sedding, Daniel, Weigand, Markus A., Fichtner, Falk, Nau, Carla, Prätsch, Florian, Wiesmann, Thomas, Koch, Christian, Schneider, Gerhard, Lahmer, Tobias, Straub, Andreas, Meiser, Andreas, Weiss, Manfred, Jungwirth, Bettina, Wappler, Frank, Meybohm, Patrick, Herrmann, Johannes, Malek, Nisar, Kohlbacher, Oliver, Biergans, Stephanie, and Rosenberger, Peter
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- 2021
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5. Circadian rhythms in septic shock patients
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Lachmann, Gunnar, Ananthasubramaniam, Bharath, Wünsch, Viktor A., Scherfig, Lara-Marie, von Haefen, Clarissa, Knaak, Cornelia, Edel, Andreas, Ehlen, Lukas, Koller, Barbara, Goldmann, Anton, Herzel, Hanspeter, Kramer, Achim, and Spies, Claudia
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- 2021
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6. 3rd Prize: Enhanced Recovery after Intensive Care (ERIC): Tele-ICU
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Staerck, Franziska, Albers, Lucas, Adam, Moritz, Pobering, Ivo, Herm, Julian, Paul, Nicolas, Menk, Mario, Kleinwächter, Robin, Steinecke, Karin, Gassner, Martina, Dähnert, Enrico, Edel, Andreas, Weiss, Björn, and Spies, Claudia
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Acute Critical Care ,Intensive Care Medicine ,Intensivmedizin ,Evidence-based Quality Indicators - Abstract
In Germany, more than 2.1 million patients are admitted to the intensive care unit (ICU) annually (Federal Statistical Office 2016). To enhance quality of care in the ICU, the German Interdisciplinary Association for Intensive Care and Emergency Medicine (DIVI) has issued ten evidence-based quality indicators (QIs) for acute critical care., Mission – Innovation: Telematics, eHealth and High-Definition Medicine in Patient-Centered Acute Medicine, p. 225
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- 2021
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7. Validation study of German inpatient administrative health data for epidemiological surveillance and measurement of quality of care for sepsis: the OPTIMISE study protocol
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Schwarzkopf, Daniel, Fleischmann-Struzek, Carolin, Schlattmann, Peter, Dorow, Heike, Ouart, Dominique, Edel, Andreas, Gonnert, Falk A, Götz, Jürgen, Gründling, Matthias, Heim, Markus, Jaschinski, Ulrich, Lindau, Simone, Meybohm, Patrick, Putensen, Christian, Sander, Michael, and Reinhart, Konrad
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Inpatients ,Adolescent ,Incidence ,public health ,quality in health care ,Infectious Diseases ,Sepsis ,Humans ,epidemiology ,Hospital Mortality ,ddc:610 ,adult intensive & critical care ,Retrospective Studies - Abstract
Introduction Sepsis is a major cause of preventable deaths in hospitals. This study aims to investigate if sepsis incidence and quality of care can be assessed using inpatient administrative health data (IAHD). Methods and analysis Design: Retrospective observational validation study using routine data to assess the diagnostic accuracy of sepsis coding in IAHD regarding sepsis diagnosis based on medical record review. Procedure: A stratified sample of 10 000 patients with an age ≥15 years treated in between 2015 and 2017 in 10 German hospitals is investigated. All available information of medical records is screened by trained physicians to identify true sepsis cases (‘gold standard’) both according to current (‘sepsis-1’) definitions and new (‘sepsis-3’) definitions. Data from medical records are linked to IAHD on patient level using a pseudonym. Analyses: Proportions of cases with sepsis according to sepsis-1 and sepsis-3 definitions are calculated and compared with estimates from coding of sepsis in IAHD. Predictive accuracy (sensitivity, specificity) of different coding abstraction strategies regarding the gold standard is estimated. Predictive accuracy of mortality risk factors obtained from IAHD regarding the respective risk factors obtained from medical records is calculated. An IAHD-based risk model for hospital mortality is compared with a record-based risk model regarding model-fit and predicted risk of death. Analyses adjust for sampling weights. The obtained estimates of sensitivity and specificity for sepsis coding in IAHD are used to estimate adjusted incidence proportions of sepsis based on German national IAHD. Ethics and dissemination The study has been approved by the ethics commission of the Jena University Hospital (No. 2018-1065-Daten). The results of the study will be discussed in an expert panel to write a memorandum on improving the utility of IAHD for epidemiological surveillance and quality management of sepsis care. Trial registration number DRKS00017775; Pre-results.
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- 2020
8. Impact of dexamethasone on SARS-CoV-2 concentration kinetics and antibody response in hospitalized COVID-19 patients: results from a prospective observational study
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Hippenstiel, Stefan, Haenel, Sascha S., Mittermaier, Mirja, Steinbeis, Fridolin, Lingscheid, Tilman, Temmesfeld-Wollbrück, Bettina, Zoller, Thomas, Müller-Redetzky, Holger, Uhrig, Alexander, Grund, Daniel, Ruwwe-Glösenkamp, Christoph, Stegemann, Miriam S., Heim, Katrin M., Hübner, Ralf H., Opitz, Bastian, Eckardt, Kai-Uwe, Möckel, Martin, Balzer, Felix, Spies, Claudia, Weber-Carstens, Steffen, Tacke, Frank, Dang-Heine, Chantip, Hummel, Michael, Schwanitz, Georg, Behrens, Uwe D., Rönnefarth, Maria, Schmidt, Sein, Krannich, Alexander, von Kalle, Christof, Jürgens, Linda, Kleinschmidt, Malte, Denker, Sophy, Pfeiffer, Moritz, Pascual-Leone, Belén Millet, Mrziglod, Luisa, Machleidt, Felix, Albus, Sebastian, Bremer, Felix, Doehn, Jan-Moritz, Andermann, Tim, Garcia, Carmen, Knape, Philipp, Krause, Philipp M., Lechtenberg, Liron, Li, Yaosi, Pergantis, Panagiotis, Jacobi, Till, Ritter, Teresa, Yedikat, Berna, Pfannkuch, Lennart, Zobel, Christian, Kellermann, Ute, Fieberg, Susanne, Bosquillon de Jarcy, Laure, Wetzel, Anne, Tabeling, Christoph, Brack, Markus C., Müller-Plathe, Moritz, Kruse, Jan M., Zickler, Daniel, Edel, Andreas, Stier, Britta, Körner, Roland, Müller, Nils B., Enghard, Philipp, Stubbemann, Paula, Olk, Nadine, Koch, Willi M., Horn, Alexandra, Stoyanova, Katrin K., Zvorc, Saskia, Kretzler, Lucie, Meyer-Arndt, Lil A., Li, Linna, Wirsching, Isabelle, Treue, Denise, Briesemeister, Dana, Schlesinger, Jenny, Sawitzki, Birgit, Bardtke, Lara, Pohl, Kai, Georg, Philipp, Wendisch, Daniel, Hiller, Anna L., Brumhard, Sophie, Schmidt, Marie Luisa, Meiners, Leonie, Tscheak, Patricia, Mühlemann, Barbara, Thibeault, Charlotte, Hillus, David, Helbig, Elisa T., Lippert, Lena J., Tober-Lau, Pinkus, Schwarz, Tatjana, Müller, Marcel A., Witzenrath, Martin, Suttorp, Norbert, Sander, Leif E., Drosten, Christian, Jones, Terry C., Corman, Victor M., and Kurth, Florian
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- 2021
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9. Timing Is Everything
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Edel, Andreas, Tafelski, Sascha, Nachtigall, Irit, and Spies, Claudia
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- 2012
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10. FAREWELL TO A EUROPEAN FRIEND. IN MEMORIAM JANINA JÓŹWIAK (1948-2016).
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Edel, Andreas
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- 2017
11. FAREWELL TO A EUROPEAN FRIEND. IN MEMORIAM JANINA JÓŹWIAK (1948–2016).
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Edel, Andreas
- Published
- 2016
12. A proteomic survival predictor for COVID-19 patients in intensive care.
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Demichev V, Tober-Lau P, Nazarenko T, Lemke O, Kaur Aulakh S, Whitwell HJ, Röhl A, Freiwald A, Mittermaier M, Szyrwiel L, Ludwig D, Correia-Melo C, Lippert LJ, Helbig ET, Stubbemann P, Olk N, Thibeault C, Grüning NM, Blyuss O, Vernardis S, White M, Messner CB, Joannidis M, Sonnweber T, Klein SJ, Pizzini A, Wohlfarter Y, Sahanic S, Hilbe R, Schaefer B, Wagner S, Machleidt F, Garcia C, Ruwwe-Glösenkamp C, Lingscheid T, Bosquillon de Jarcy L, Stegemann MS, Pfeiffer M, Jürgens L, Denker S, Zickler D, Spies C, Edel A, Müller NB, Enghard P, Zelezniak A, Bellmann-Weiler R, Weiss G, Campbell A, Hayward C, Porteous DJ, Marioni RE, Uhrig A, Zoller H, Löffler-Ragg J, Keller MA, Tancevski I, Timms JF, Zaikin A, Hippenstiel S, Ramharter M, Müller-Redetzky H, Witzenrath M, Suttorp N, Lilley K, Mülleder M, Sander LE, Kurth F, and Ralser M
- Abstract
Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care., Competing Interests: The authors declare no competing interests. Author John F. Timms was unable to confirm their authorship contributions. On their behalf, the corresponding author has reported their contributions to the best of their knowledge., (Copyright: © 2022 Demichev et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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- 2022
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13. Validation study of German inpatient administrative health data for epidemiological surveillance and measurement of quality of care for sepsis: the OPTIMISE study protocol.
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Schwarzkopf D, Fleischmann-Struzek C, Schlattmann P, Dorow H, Ouart D, Edel A, Gonnert FA, Götz J, Gründling M, Heim M, Jaschinski U, Lindau S, Meybohm P, Putensen C, Sander M, and Reinhart K
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- Adolescent, Hospital Mortality, Humans, Incidence, Retrospective Studies, Inpatients, Sepsis diagnosis, Sepsis epidemiology, Sepsis therapy
- Abstract
Introduction: Sepsis is a major cause of preventable deaths in hospitals. This study aims to investigate if sepsis incidence and quality of care can be assessed using inpatient administrative health data (IAHD)., Methods and Analysis: Design: Retrospective observational validation study using routine data to assess the diagnostic accuracy of sepsis coding in IAHD regarding sepsis diagnosis based on medical record review., Procedure: A stratified sample of 10 000 patients with an age ≥15 years treated in between 2015 and 2017 in 10 German hospitals is investigated. All available information of medical records is screened by trained physicians to identify true sepsis cases ('gold standard') both according to current ('sepsis-1') definitions and new ('sepsis-3') definitions. Data from medical records are linked to IAHD on patient level using a pseudonym., Analyses: Proportions of cases with sepsis according to sepsis-1 and sepsis-3 definitions are calculated and compared with estimates from coding of sepsis in IAHD. Predictive accuracy (sensitivity, specificity) of different coding abstraction strategies regarding the gold standard is estimated. Predictive accuracy of mortality risk factors obtained from IAHD regarding the respective risk factors obtained from medical records is calculated. An IAHD-based risk model for hospital mortality is compared with a record-based risk model regarding model-fit and predicted risk of death. Analyses adjust for sampling weights. The obtained estimates of sensitivity and specificity for sepsis coding in IAHD are used to estimate adjusted incidence proportions of sepsis based on German national IAHD., Ethics and Dissemination: The study has been approved by the ethics commission of the Jena University Hospital (No. 2018-1065-Daten). The results of the study will be discussed in an expert panel to write a memorandum on improving the utility of IAHD for epidemiological surveillance and quality management of sepsis care., Trial Registration Number: DRKS00017775; Pre-results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
- Published
- 2020
- Full Text
- View/download PDF
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