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| Content Provider | Springer Nature : BioMed Central |
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| Author | Schmidbauer, Moritz L. Putz, Timon Gehri, Leon Ratkovic, Luka Maskos, Andreas Zibold, Julia Bauchmüller, Johanna Imhof, Sophie Weig, Thomas Wuehr, Max Dimitriadis, Konstantinos |
| Abstract | Background Physical inactivity and subsequent muscle atrophy are highly prevalent in neurocritical care and are recognized as key mechanisms underlying intensive care unit acquired weakness (ICUAW). The lack of quantifiable biomarkers for inactivity complicates the assessment of its relative importance compared to other conditions under the syndromic diagnosis of ICUAW. We hypothesize that active movement, as opposed to passive movement without active patient participation, can serve as a valid proxy for activity and may help predict muscle atrophy. To test this hypothesis, we utilized non-invasive, body-fixed accelerometers to compute measures of active movement and subsequently developed a machine learning model to predict muscle atrophy. Methods This study was conducted as a single-center, prospective, observational cohort study as part of the MINCE registry (metabolism and nutrition in neurointensive care, DRKS-ID: DRKS00031472). Atrophy of rectus femoris muscle (RFM) relative to baseline (day 0) was evaluated at days 3, 7 and 10 after intensive care unit (ICU) admission and served as the dependent variable in a generalized linear mixed model with Least Absolute Shrinkage and Selection Operator regularization and nested-cross validation. Results Out of 407 patients screened, 53 patients (age: 59.2 years (SD 15.9), 31 (58.5%) male) with a total of 91 available accelerometer datasets were enrolled. RFM thickness changed − 19.5% (SD 12.0) by day 10. Out of 12 demographic, clinical, nutritional and accelerometer-derived variables, baseline RFM muscle mass (beta − 5.1, 95% CI − 7.9 to − 3.8) and proportion of active movement (% activity) (beta 1.6, 95% CI 0.1 to 4.9) were selected as significant predictors of muscle atrophy. Including movement features into the prediction model substantially improved performance on an unseen test data set (including movement features: R2 = 79%; excluding movement features: R2 = 55%). Conclusion Active movement, as measured with thigh-fixed accelerometers, is a key risk factor for muscle atrophy in neurocritical care patients. Quantifiable biomarkers reflecting the level of activity can support more precise phenotyping of ICUAW and may direct tailored interventions to support activity in the ICU. Studies addressing the external validity of these findings beyond the neurointensive care unit are warranted. Trial registration DRKS00031472, retrospectively registered on 13.03.2023. |
| Related Links | https://ccforum.biomedcentral.com/counter/pdf/10.1186/s13054-024-05067-y.pdf |
| Ending Page | 11 |
| Page Count | 11 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 13648535 |
| DOI | 10.1186/s13054-024-05067-y |
| Journal | Critical Care |
| Issue Number | 1 |
| Volume Number | 28 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-08-31 |
| Access Restriction | Open |
| Subject Keyword | Intensive Critical Care Medicine Emergency Medicine ICU ICUAW Sarcopenia Muscle atrophy Accelerometer Machine learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Critical Care and Intensive Care Medicine |
| Journal Impact Factor | 8.8/2023 |
| 5-Year Journal Impact Factor | 10.4/2023 |
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