Clinical trial NCT04455451: Intensive Care Therapy of Covid-19 Disease in Germany
Official title: Intensive Care Therapy of Covid-19 Disease in Germany an Multi-center Observational Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: COVID
ARDS
Lead sponsor: University Hospital Tuebingen
Sponsor class: OTHER
First posted: 2020-07-02
Enrollment: 2054
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: OTHER
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adults admitted for ICU therapy due to severe or critical Covid-19

Brief summary: Multi-center retrospective and prospective observational study of Covid-19 patients with severe or critical illness treated on a German ICU


Eligibility criteria: Inclusion Criteria:

* Age above 18 years
* Covid-19 disease (positive SARS-CoV-2 PCR)

Exclusion Criteria:

-none

Primary outcomes:
measure: Mortality
timeFrame: up to 150 days
Secondary outcomes:
measure: ICU length of stay
timeFrame: up to 150 days
Other outcomes:
Locations:
city: Aachen
country: Germany
facility: RWTH Aachen - Department for operative Intensive Care Medicine and intermediate Care
city: Balingen
country: Germany
facility: Zollernalbklinikum
city: Berlin
country: Germany
facility: Charité Universitätsmedizin Berlin
city: Bochum
country: Germany
facility: UH Bochum - Department for Anesthesiology and Intensive Care Medicine
city: Bonn
country: Germany
facility: Universitätsklinikum Bonn
city: Dresden
country: Germany
facility: Universitätsklinikum Carl Gustav Carus Dresden
city: Duisburg
country: Germany
facility: Ev. Krankenhaus Bethesda Duisburg Klinik für Anästhesiologie und Intensivmedizin
city: Düsseldorf
country: Germany
facility: Univerrsitätsklinikum Düsseldorf
city: Essen
country: Germany
facility: Universitätsklinikum Essen
city: Frankfurt
country: Germany
facility: Universitätsklinikum Frankfurt
city: Freiburg
country: Germany
facility: University Hospital Freiburg - Department for Anesthesiology and Intensive Care Medicine
city: Gießen
country: Germany
facility: Universitätsklinikum Giessen Klinik für Anaesthesiologie und operative Intensivmedizin
city: Greifswald
country: Germany
facility: Universitätsklinikum Greifswald
city: Göttingen
country: Germany
facility: Universitätsmedizin Göttingen Klinik für Anästhesiologie
city: Halle/Saale
country: Germany
facility: Universitätsklinikum Halle
city: Heidelberg
country: Germany
facility: University Hospital Heidelberg - Department for Anesthesiologie and Intensive Care Medicine
city: Homburg/Saar
country: Germany
facility: Universitätsklinikum des Saarlandes
city: Köln
country: Germany
facility: Klinikum der Universität Witten/Herdecke - Köln / Klinik für Anästhesiologie und operative Intensivmedizin
city: Leipzig
country: Germany
facility: Universitätsklinikum Leipzig
city: Lübeck
country: Germany
facility: UKSH Campus Lübeck - Department for Anesthesiology and Intensive Care Medicine
city: Magdeburg
country: Germany
facility: University Hospital Magdeburg - Department for Anesthesiology and Intensive Care Medicine
city: Marburg
country: Germany
facility: Universitätsklinikum Marburg
city: München
country: Germany
facility: Technische Universität München
city: Ravensburg
country: Germany
facility: Oberschwabenklinik Ravensburg Klinik für Anästhesie-, Intensiv-, Notfall- und Schmerzmedizin
city: Tübingen
country: Germany
facility: University Hospital Tübingen - Department for Anesthesiology and Intensive Care Medicine
city: Ulm
country: Germany
facility: Universitätsklinikum Ulm
city: Würzburg
country: Germany
facility: Universitätsklinikum Würzburg Klinik und Poliklinik für Anästhesiologie, Intensivmedizin und Schmerztherapie
Study officials:
affiliation: University Hospital Tübingen
name: Peter Rosenberger, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: University Hospital Tübingen
name: Harry Magunia, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Magunia H, Lederer S, Verbuecheln R, Gilot BJ, Koeppen M, Haeberle HA, Mirakaj V, Hofmann P, Marx G, Bickenbach J, Nohe B, Lay M, Spies C, Edel A, Schiefenhovel F, Rahmel T, Putensen C, Sellmann T, Koch T, Brandenburger T, Kindgen-Milles D, Brenner T, Berger M, Zacharowski K, Adam E, Posch M, Moerer O, Scheer CS, Sedding D, Weigand MA, Fichtner F, Nau C, Pratsch F, Wiesmann T, Koch C, Schneider G, Lahmer T, Straub A, Meiser A, Weiss M, Jungwirth B, Wappler F, Meybohm P, Herrmann J, Malek N, Kohlbacher O, Biergans S, Rosenberger P. Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort. Crit Care. 2021 Aug 17;25(1):295. doi: 10.1186/s13054-021-03720-4.