| Autor: |
Kristina E. Fuest, Bernhard Ulm, Nils Daum, Maximilian Lindholz, Marco Lorenz, Kilian Blobner, Nadine Langer, Carol Hodgson, Margaret Herridge, Manfred Blobner, Stefan J. Schaller |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Critical Care, Vol 27, Iss 1, Pp 1-11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1364-8535 |
| DOI: |
10.1186/s13054-022-04291-8 |
| Popis: |
Abstract Background While early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characterization of the optimal individual dosage (frequency, level or duration) remains unclear. The aim of this study was to demonstrate that artificial intelligence-based clustering of a large ICU cohort can provide individualized mobilization recommendations that have a positive impact on the likelihood of being discharged home. Methods This study is an analysis of a prospective observational database of two interdisciplinary intensive care units in Munich, Germany. Dosage of mobilization is determined by sessions per day, mean duration, early mobilization as well as average and maximum level achieved. A k-means cluster analysis was conducted including collected parameters at ICU admission to generate clinically definable clusters. Results Between April 2017 and May 2019, 948 patients were included. Four different clusters were identified, comprising “Young Trauma,” “Severely ill & Frail,” “Old non-frail” and “Middle-aged” patients. Early mobilization ( |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink