Robotic-Assisted In-Bed Mobilization in Ventilated ICU Patients With COVID-19: An Interventional, Randomized, Controlled Pilot Study (ROBEM II Study).

OBJECTIVES: The COVID-19 pandemic significantly impacted global healthcare systems, particularly in managing critically ill mechanically ventilated patients. This study aims to assess the feasibility of robotic-assisted mobilization in COVID-19 patients. DESIGN: Randomized controlled pilot study. SETTING: Four COVID-19 specialized ICUs at Charité-Universitätsmedizin Berlin (March 2021 to February 2022). PATIENTS: Twenty critically ill COVID-19 patients expected to require greater than 24 hours of ventilation. INTERVENTIONS: A 5-day intervention phase with bid robotic-assisted mobilization greater than or equal to 20 minutes and follow-up at day 180, compared with standard care. MEASUREMENTS AND MAIN RESULTS: Intervention sessions were conducted in 98.9% according to protocol, with one session missing due to staff shortage. Primary outcome was the mobilization level measured with the ICU Mobility Scale (IMS) and Surgical ICU Optimal Mobilization Score (SOMS), assessed until day 5 or extubation. Safety events were recorded during mobilization. The median IMS and SOMS were 0 (0-0.16) and 1 (1-1.03) in the intervention group, and 0 (0-0.15) ( p = 0.77) and 0.8 (0.65-1.20) ( p = 0.08) in the standard care group, respectively. Significant secondary outcomes included average number of mobilization sessions (intervention: 8.5 [7.75-10] vs. standard care: 4.5 [3.5-5]; p = 0.001), total mobilization time (intervention: 232.5 min [187.25-266.5 min] vs. standard care: 147.5 min [107.5-167.5 min]; p = 0.011), and healthcare providers per session (intervention: 2 [2-2] vs. standard care: 1 [1-1.4]; p = 0.001) during intervention. Four safety events (hypertension and agitation, n = 2 each) in the intervention group and none in the standard care group were reported. CONCLUSIONS: Robotic-assisted mobilization in mechanically ventilated COVID-19 patients appears to be safe and feasible.

Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU.

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 (< 72 h) was the most important factor to be discharged home in "Young Trauma" patients (ORadj 10.0 [2.8 to 44.0], p < 0.001). In the cluster of "Middle-aged" patients, the likelihood to be discharged home increased with each mobilization level, to a maximum 24-fold increased likelihood for ambulating (ORadj 24.0 [7.4 to 86.1], p < 0.001). The likelihood increased significantly when standing or ambulating was achieved in the older, non-frail cluster (ORadj 4.7 [1.2 to 23.2], p = 0.035 and ORadj 8.1 [1.8 to 45.8], p = 0.010). CONCLUSIONS: An artificial intelligence-based learning approach was able to divide a heterogeneous critical care cohort into four clusters, which differed significantly in their clinical characteristics and in their mobilization parameters. Depending on the cluster, different mobilization strategies supported the likelihood of being discharged home enabling an individualized and resource-optimized mobilization approach. TRIAL REGISTRATION: Clinical Trials NCT03666286, retrospectively registered 04 September 2018.

Contextual cueing under working memory load: selective interference of visuospatial load with expression of learning.

In a series of experiments, we investigated the dependence of contextual cueing on working memory resources. A visual search task with 50 % repeated displays was run in order to elicit the implicit learning of contextual cues. The search task was combined with a concurrent visual working memory task either during an initial learning phase or a later test phase. The visual working memory load was either spatial or nonspatial. Articulatory suppression was used to prevent verbalization. We found that nonspatial working memory load had no effect, independent of presentation in the learning or test phase. In contrast, visuospatial load diminished search facilitation in the test phase, but not during learning. We concluded that visuospatial working memory resources are needed for the expression of previously learned spatial contexts, whereas the learning of contextual cues does not depend on visuospatial working memory.