ICU Bed Utilization During the Coronavirus Disease 2019 Pandemic in a Multistate Analysis-March to June 2020.

OBJECTIVES: Given finite ICU bed capacity, knowledge of ICU bed utilization during the coronavirus disease 2019 pandemic is critical to ensure future strategies for resource allocation and utilization. We sought to examine ICU census trends in relation to ICU bed capacity during the rapid increase in severe coronavirus disease 2019 cases early during the pandemic. DESIGN: Observational cohort study. SETTING: Thirteen geographically dispersed academic medical centers in the United States. PATIENTS/SUBJECTS: We obtained daily ICU censuses from March 26 to June 30, 2020, as well as prepandemic ICU bed capacities. The primary outcome was daily census of ICU patients stratified by coronavirus disease 2019 and mechanical ventilation status in relation to ICU capacity. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Prepandemic overall ICU capacity ranged from 62 to 225 beds (median 109). During the study period, the median daily coronavirus disease 2019 ICU census per hospital ranged from 1 to 84 patients, and the daily ICU census exceeded overall ICU capacity for at least 1 day at five institutions. The number of critically ill patients exceeded ICU capacity for a median (interquartile range) of 17 (12-50) of 97 days at these five sites. All 13 institutions experienced decreases in their noncoronavirus disease ICU population, whereas local coronavirus disease 2019 cases increased. Coronavirus disease 2019 patients reached their greatest proportion of ICU capacity on April 12, 2020, when they accounted for 44% of ICU patients across all participating hospitals. Maximum ICU census ranged from 52% to 289% of overall ICU capacity, with three sites less than 80%, four sites 80-100%, five sites 100-128%, and one site 289%. CONCLUSIONS: From March to June 2020, the coronavirus disease 2019 pandemic led to ICU censuses greater than ICU bed capacity at fives of 13 institutions evaluated. These findings demonstrate the short-term adaptability of U.S. healthcare institutions in redirecting limited resources to accommodate a public health emergency.

Association between increased theta cordance and early response to ECT in late-life depression.

OBJECTIVES: More than half of patients with major depression who do not respond to initial antidepressants become treatment resistant (TRD), and while electroconvulsive therapy (ECT) is effective, it involves anesthesia and other medical risks that are of concern in geriatric patients. Past studies have suggested that theta cordance (TC), a correlate of cerebral metabolism measured by electroencephalography, could guide treatment decisions related to patient selection and engagement of the therapeutic target. METHODS/DESIGN: Eight patients with late-life treatment resistant depression (LL-TRD) underwent magnetoencephalography (MEG) at baseline and following seven sessions of ECT. We tested whether the mean and regional frontal cortex TC were able to differentiate early responders from nonresponders. RESULTS: Five patients whose depression severity decreased by >30% after seven sessions were considered early responders. We found no baseline differences in mean frontal TC between early responders compared with nonresponders, but early responders exhibited a significant increase in TC following ECT. Further, we found that compared with nonresponders, early responders exhibited a greater change in TC specifically within the right prefrontal cortex. CONCLUSIONS: These results support the hypothesis that increases in frontal TC are associated with antidepressant response. We expand on previous findings by showing that this change is specific to the right prefrontal cortex. Validation of this neural marker could contribute to improved ECT outcomes, by informing early clinical decisions about the acute efficacy of this treatment.

Quantitatively validating the efficacy of artifact suppression techniques to study the cortical consequences of deep brain stimulation with magnetoencephalography.

Deep brain stimulation (DBS) is an established and effective treatment for several movement disorders and is being developed to treat a host of neuropsychiatric disorders including epilepsy, chronic pain, obsessive compulsive disorder, and depression. However, the neural mechanisms through which DBS produces therapeutic benefits, and in some cases unwanted side effects, in these disorders are only partially understood. Non-invasive neuroimaging techniques that can assess the neural effects of active stimulation are important for advancing our understanding of the neural basis of DBS therapy. Magnetoencephalography (MEG) is a safe, passive imaging modality with relatively high spatiotemporal resolution, which makes it a potentially powerful method for examining the cortical network effects of DBS. However, the degree to which magnetic artifacts produced by stimulation and the associated hardware can be suppressed from MEG data, and the comparability between signals measured during DBS-on and DBS-off conditions, have not been fully quantified. The present study used machine learning methods in conjunction with a visual perception task, which should be relatively unaffected by DBS, to quantify how well neural data can be salvaged from artifact contamination introduced by DBS and how comparable DBS-on and DBS-off data are after artifact removal. Machine learning also allowed us to determine whether the spatiotemporal pattern of neural activity recorded during stimulation are comparable to those recorded when stimulation is off. The spatiotemporal patterns of visually evoked neural fields could be accurately classified in all 8 patients with DBS implants during both DBS-on and DBS-off conditions and performed comparably across those two conditions. Further, the classification accuracy for classifiers trained on the spatiotemporal patterns evoked during DBS-on trials and applied to DBS-off trials, and vice versa, were similar to that of the classifiers trained and tested on either trial type, demonstrating the comparability of these patterns across conditions. Together, these results demonstrate the ability of MEG preprocessing techniques, like temporal signal space separation, to salvage neural data from recordings contaminated with DBS artifacts and validate MEG as a powerful tool to study the cortical consequences of DBS.