Simone Piva1, Giancarlo Dora2, Cosetta Minelli3, Mariachiara Michelini4, Fabio Turla1, Stefania Mazza1, Patrizia D'Ottavi4, Ingrid Moreno-Duarte5, Caterina Sottini2, Matthias Eikermann5, Nicola Latronico6. 1. Department of Anesthesia, Critical Care Medicine and Emergency, Spedali Civili University Hospital, Brescia, Italy. 2. Department of Physical Medicine and Rehabilitation, Spedali Civili University Hospital, Brescia, Italy. 3. National Heart and Lung Institute, Imperial College London, London, England, UK. 4. Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy. 5. Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA. 6. Department of Anesthesia, Critical Care Medicine and Emergency, Spedali Civili University Hospital, Brescia, Italy; Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy. Electronic address: nicola.latronico@unibs.it.
Abstract
PURPOSE: We validated the Italian version of Surgical Optimal Mobility Score (SOMS) and evaluated its ability to predict intensive care unit (ICU) and hospital length of stay (LOS), and hospital mortality in a mixed population of ICU patients. MATERIALS AND METHODS: We applied the Italian version of SOMS in a consecutive series of prospectively enrolled, adult ICU patients. Surgical Optimal Mobility Score level was assessed twice a day by ICU nurses and twice a week by an expert mobility team. Zero-truncated Poisson regression was used to identify predictors for ICU and hospital LOS, and logistic regression for hospital mortality. All models were adjusted for potential confounders. RESULTS: Of 98 patients recruited, 19 (19.4%) died in hospital, of whom 17 without and 2 with improved mobility level achieved during the ICU stay. SOMS improvement was independently associated with lower hospital mortality (odds ratio, 0.07; 95% confidence interval [CI], 0.01-0.42) but increased hospital LOS (odds ratio, 1.21; 95% CI: 1.10-1.33). A higher first-morning SOMS on ICU admission, indicating better mobility, was associated with lower ICU and hospital LOS (rate ratios, 0.89 [95% CI, 0.80-0.99] and 0.84 [95% CI, 0.79-0.89], respectively). CONCLUSIONS: The first-morning SOMS on ICU admission predicted ICU and hospital LOS in a mixed population of ICU patients. SOMS improvement was associated with reduced hospital mortality but increased hospital LOS, suggesting the need of optimizing hospital trajectories after ICU discharge.
PURPOSE: We validated the Italian version of Surgical Optimal Mobility Score (SOMS) and evaluated its ability to predict intensive care unit (ICU) and hospital length of stay (LOS), and hospital mortality in a mixed population of ICU patients. MATERIALS AND METHODS: We applied the Italian version of SOMS in a consecutive series of prospectively enrolled, adult ICU patients. Surgical Optimal Mobility Score level was assessed twice a day by ICU nurses and twice a week by an expert mobility team. Zero-truncated Poisson regression was used to identify predictors for ICU and hospital LOS, and logistic regression for hospital mortality. All models were adjusted for potential confounders. RESULTS: Of 98 patients recruited, 19 (19.4%) died in hospital, of whom 17 without and 2 with improved mobility level achieved during the ICU stay. SOMS improvement was independently associated with lower hospital mortality (odds ratio, 0.07; 95% confidence interval [CI], 0.01-0.42) but increased hospital LOS (odds ratio, 1.21; 95% CI: 1.10-1.33). A higher first-morning SOMS on ICU admission, indicating better mobility, was associated with lower ICU and hospital LOS (rate ratios, 0.89 [95% CI, 0.80-0.99] and 0.84 [95% CI, 0.79-0.89], respectively). CONCLUSIONS: The first-morning SOMS on ICU admission predicted ICU and hospital LOS in a mixed population of ICU patients. SOMS improvement was associated with reduced hospital mortality but increased hospital LOS, suggesting the need of optimizing hospital trajectories after ICU discharge.
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