Shear wave elastography (SWE) is a clinical ultrasound imaging modality that enables non-invasive estimation of tissue elasticity. However, various methodological factors—such as vendor-specific implementations of SWE, mechanical anisotropy of tissue, varying anatomical position of muscle and changes in elasticity due to passive muscle stretch—can confound muscle SWE measurements and increase their variability. A measurement protocol with a low variability of reference measurements in healthy subjects is desirable to facilitate diagnostic conclusions on an individual-patient level. Here, we present data from 52 healthy volunteers in the areas of: (1) Characterizing different limb and truncal muscles in terms of inter-subject variability of SWE measurements. Superficial muscles with little pennation, such as biceps brachii, exhibit the lowest variability whereas paravertebral muscles show the highest. (2) Comparing two protocols with different limb positioning in a trade-off between examination convenience and SWE measurement variability. Repositioning to achieve low passive extension of each muscle results in the lowest SWE variability. (3) Providing SWE shear wave velocity (SWV) reference values for a specific ultrasound machine/transducer setup (Canon Aplio i800, 18 MHz probe) for a number of muscles and two positioning protocols. We argue that methodological issues limit the current clinical applicability of muscle SWE.

This proof-of-principle-study evaluated the extent to which spontaneous activity (SA) of the muscle can be detected via non-invasive magnetomyography (MMG) with optically pumped magnetometers (OPM). Five patients, who together exhibited all forms of SA (fibrillations, positive sharp waves, fasciculations, myotonic discharges, complex-repetitive discharges) with conventional needle electromyography (EMG), were studied by OPM-MMG and simultaneous surface EMG (sEMG) while at rest, during light muscle activation, and when a muscle stretch reflex was elicited. Three healthy subjects were measured as controls. SA was considered apparent in the OPM-MMG if a signal could be visually detected that corresponded in shape and frequency to the SA in the respective needle EMG. SA in the context of fasciculations could be detected in 2 of 5 patients by simultaneous OPM-MMG/sEMG. Other forms of SA could not be detected at rest, during light muscle activation, or after provocation of a muscle stretch reflex. Results show that fasciculations could be detected non-invasively via a new method (OPM). We show that other forms of SA are not detectable with current OPM and propose necessary technical solutions to overcome this circumstance. Our results motivate to pursue OPM-MMG as a new clinical neurophysiological diagnostic.

Background: Predisposing and precipitating factors for delirium for the elderly, over the age of 65 years, are known, but not for the very old, over 80 years. As the society is getting older and evermore patients will reach >80 years, more evidence of the factors and their contribution to delirium is required in this patient group. Methods: In the course of 1 year, 3,076 patients above 80 years were screened prospectively for delirium based on a Delirium Observation Screening (DOS) scale, Intensive Care Delirium Screening Checklist (ICDSC), and a DSM (Diagnostic and Statistical Manual)-5 nursing instrument (ePA-AC) construct. Relevant predisposing and precipitating factors for delirium were assessed with a multiple regression analysis. Results: Of 3,076 patients above 80 years, 1,285 (41.8%) developed a delirium, which led to twice prolonged hospitalization (p < 0.001), requirement for subsequent assisted living (OR 2.2, CI: 1.73–2.8, p < 0.001), and increased mortality (OR 24.88, CI: 13.75–45.03, p < 0.001). Relevant predisposing factors were dementia (OR 15.6, CI: 10.17–23.91, p < 0.001), pressure sores (OR 4.61, CI: 2.74–7.76, p < 0.001), and epilepsy (OR 3.65, CI: 2.12–6.28, p < 0.0001). Relevant precipitating factors were acute renal failure (4.96, CI: 2.38–10.3, p < 0.001), intracranial hemorrhage (OR 8.7, CI: 4.27–17.7, p < 0.001), and pleural effusions (OR 3.25, CI: 1.77–17.8, p < 0.001). Conclusion: Compared to the general delirium rate of approximately 20%, the prevalence of delirium doubled above the age of 80 years (41.8%) due to predisposing factors uncommon in younger patients.

Background: Delirium in trauma surgery is common, especially post-operatively, but medical characteristics, risk factors and residence post-discharge have not comprehensively been investigated in all trauma patients. Methods: Over 1 year, 2026 trauma patients were prospectively screened for delirium with the following tools: Delirium Observation screening scale (DOS), Intensive Care Delirium Screening Checklist (ICDSC) and a DSM (Diagnostic and Statistical Manual)-5, nursing tool (ePA-AC) construct. Risk factors—predisposing und precipitating—for delirium were assessed via multiple regression analysis. Results: Of 2026 trauma patients, 440 (21.7%) developed delirium, which was associated with an increased risk of assisted living (OR 6.42, CI 3.92–10.49), transfer to nursing home (OR 4.66, CI 3.29–6.6), rehabilitation (OR 3.96, CI 3.1–5.1), or death (OR 70.72, CI 22–227.64). Intensive care management (OR 18.62, CI 14.04–24.68), requirement of ventilation (OR 32.21, CI 21.27–48.78), or its duration (OR 67.22, CI 33.8–133.71) all increased the risk for developing delirium. Relevant predisposing risk factors were dementia (OR 50.92, CI 15.12–171.45), cardiac insufficiency (OR 11.76, CI 3.6–38.36), and polypharmacy (OR 5.9, CI 4.01–8.68).Relevant precipitating risk factors were brain edema (OR 40.53, CI 4.81–341.31), pneumonia (OR 39.66, CI 8.89–176.93) and cerebral inflammation (OR 21.74, CI 2.34–202.07). Conclusion: Delirium in trauma patients is associated with poor outcome as well as with intensive care management and various predisposing and/or precipitating factors. Three quarters of patients who had undergone delirium were not able to live independently at home any more.

BACKGROUND AND PURPOSE: Voxel-based morphometry is widely used for detecting gray matter abnormalities in epilepsy. However, its performance with changing parameters, smoothing and statistical threshold, is debatable. More important, the potential yield of combining multiple MR imaging contrasts (multispectral voxel-based morphometry) is still unclear. Our aim was to objectify smoothing and statistical cutoffs and systematically compare the performance of multispectral voxel-based morphometry with existing T1 voxel-based morphometry in patients with focal epilepsy and previously negative MRI. MATERIALS AND METHODS: 3D T1-, T2-, and T2-weighted FLAIR scans were acquired for 62 healthy volunteers and 13 patients with MR imaging negative for focal epilepsy on a Magnetom Skyra 3T scanner with an isotropic resolution of 0.9 mm³. We systematically optimized the main voxel-based morphometry parameters, smoothing level and statistical cutoff, with T1 voxel-based morphometry as a reference. As a next step, the performance of multispectral voxel-based morphometry models, T1+T2, T1+FLAIR, and T1+T2+FLAIR, was compared with that of T1 voxel-based morphometry using gray matter concentration and gray matter volume analysis. RESULTS: We found the best performance of T1 at 12 mm and a T-threshold (statistical cutoff) of 3.7 for gray matter concentration analysis. When we incorporated these parameters, after expert visual interpretation of concordant and discordant findings, we identified T1+FLAIR as the best model with a concordant rate of 46.2% and a concordant rate/discordant rate of 1.20 compared with T1 with 30.8% and 0.67, respectively. Visual interpretation of voxel-based morphometry findings decreased concordant rates from 38.5%–46.2% to 15.4%–46.2% and discordant rates from 53.8%–84.6% to 30.8%–46.2% and increased specificity across models from 33.9%–40.3% to 46.8%–54.8%. CONCLUSIONS: Multispectral voxel-based morphometry, especially T1+FLAIR, can yield superior results over single-channel T1 in focal epilepsy patients with a negative conventional MR imaging.