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Schellenberg CM, Lindholz M, Grunow JJ, Boie S, Bald A, Warner LO, Ulm B, Milnik A, Zickler D, Angermair S, Reißhauer A, Witzenrath M, Menk M, Balzer F, Ocker T, Weber-Carstens S, Schaller SJ. Mobilisation practices during the SARS-CoV-2 pandemic: A retrospective analysis (MobiCOVID). Anaesth Crit Care Pain Med 2023; 42:101255. [PMID: 37257753 PMCID: PMC10226277 DOI: 10.1016/j.accpm.2023.101255] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/17/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND Corona Virus Disease 2019 (COVID-19) patients display risk factors for intensive care unit acquired weakness (ICUAW). The pandemic increased existing barriers to mobilisation. This study aimed to compare mobilisation practices in COVID-19 and non-COVID-19 patients. METHODS This retrospective cohort study was conducted at Charité-Universitätsmedizin Berlin, Germany, including adult patients admitted to one of 16 ICUs between March 2018, and November 2021. The effect of COVID-19 on mobilisation level and frequency, early mobilisation (EM) and time to active sitting position (ASP) was analysed. Subgroup analysis on COVID-19 patients and the ICU type influencing mobilisation practices was performed. Mobilisation entries were converted into the ICU mobility scale (IMS) using supervised machine learning. The groups were matched using 1:1 propensity score matching. RESULTS A total of 12,462 patients were included, receiving 59,415 mobilisations. After matching 611 COVID-19 and non-COVID-19 patients were analysed. They displayed no significant difference in mobilisation frequency (0.4 vs. 0.3, p = 0.7), maximum IMS (3 vs. 3; p = 0.17), EM (43.2% vs. 37.8%; p = 0.06) or time to ASP (HR 0.95; 95% CI: 0.82, 1.09; p = 0.44). Subgroup analysis showed that patients in surge ICUs, i.e., temporarily created ICUs for COVID-19 patients during the pandemic, more commonly received EM (53.9% vs. 39.8%; p = 0.03) and reached higher maximum IMS (4 vs. 3; p = 0.03) without difference in mobilisation frequency (0.5 vs. 0.3; p = 0.32) or time to ASP (HR 1.15; 95% CI: 0.85, 1.56; p = 0.36). CONCLUSION COVID-19 did not hinder mobilisation. Those treated in surge ICUs were more likely to receive EM and reached higher mobilisation levels.
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Affiliation(s)
- Clara M Schellenberg
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Berlin, Germany
| | - Maximilian Lindholz
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Berlin, Germany
| | - Julius J Grunow
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Berlin, Germany
| | - Sebastian Boie
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
| | - Annika Bald
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Berlin, Germany
| | - Linus O Warner
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Berlin, Germany
| | - Bernhard Ulm
- Technical University of Munich, School of Medicine, Department of Anesthesiology and Intensive Care, Munich, Germany; Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, University Hospital Ulm, Ulm, Germany
| | - Annette Milnik
- Research Platform Molecular and Cognitive Neurosciences (MCN), Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Daniel Zickler
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Nephrology and Medical Intensive Care, Berlin, Germany
| | - Stefan Angermair
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine (CBF), Berlin, Germany
| | - Anett Reißhauer
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Rehabilitation Medicine, Berlin, Germany
| | - Martin Witzenrath
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Pulmonary Medicine and Critical Care, Berlin, Germany; German Center for Lung Research (DZL), Berlin, Germany
| | - Mario Menk
- Department of Anesthesiology and Intensive Care Medicine, University Hospital "Carl Gustav Carus", Technische Universität Dresden, Dresden, Germany
| | - Felix Balzer
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
| | - Thomas Ocker
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Berlin, Germany; Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
| | - Steffen Weber-Carstens
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Berlin, Germany
| | - Stefan J Schaller
- Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Berlin, Germany; Technical University of Munich, School of Medicine, Department of Anesthesiology and Intensive Care, Munich, Germany.
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Koenig T, Neumann C, Ocker T, Kramer S, Spies C, Schuster M. Estimating the time needed for induction of anaesthesia and its importance in balancing anaesthetists' and surgeons' waiting times around the start of surgery. Anaesthesia 2011; 66:556-62. [PMID: 21564042 DOI: 10.1111/j.1365-2044.2011.06661.x] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
After the anaesthetist has induced anaesthesia, it is desirable that the surgeon is present and ready to start surgery, otherwise the team needs to wait for the surgeon. From another perspective, however, the surgeon does not necessarily wish to be present from the start of induction, since that process can take a variable time and the surgeon might be otherwise occupied in productive activity rather than waiting for the patient to be ready. Waiting times in the morning can therefore be a source of constant friction between anaesthetists and surgeons. In this prospective study we used the data from 718 first cases of the day, during a 4-week study period at two university hospitals, to develop a simple spreadsheet-based method to analyse the interaction of anaesthesia and surgical start time, anaesthesia technique and the probability of waiting time for anaesthetist or surgeon, respectively. This method can be used to determine the best surgical or anaesthesia start time for each case, so that the waiting time for anaesthetists and surgeons can be minimised.
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Affiliation(s)
- T Koenig
- Department of Anaesthesiology and Intensive Care, Campus Charité Mitte and Campus Virchow Klinikum, Charité - Universitätsmedizin Berlin, Berlin, Germany
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