Background and Purpose: Coronavirus disease 2019 (COVID-19) has been associated with an increased incidence of thrombotic events, including stroke. However, characteristics and outcomes of COVID-19 patients with stroke are not well known. Methods: We conducted a retrospective observational study of risk factors, stroke characteristics, and short-term outcomes in a large health system in New York City. We included consecutively admitted patients with acute cerebrovascular events from March 1, 2020 through April 30, 2020. Data were stratified by COVID-19 status, and demographic variables, medical comorbidities, stroke characteristics, imaging results, and in-hospital outcomes were examined. Among COVID-19-positive patients, we also summarized laboratory test results. Results: Of 277 patients with stroke, 105 (38.0%) were COVID-19-positive. Compared with COVID-19-negative patients, COVID-19-positive patients were more likely to have a cryptogenic (51.8% versus 22.3%, P <0.0001) stroke cause and were more likely to suffer ischemic stroke in the temporal ( P =0.02), parietal ( P =0.002), occipital ( P =0.002), and cerebellar ( P =0.028) regions. In COVID-19-positive patients, mean coagulation markers were slightly elevated (prothrombin time 15.4±3.6 seconds, partial thromboplastin time 38.6±24.5 seconds, and international normalized ratio 1.4±1.3). Outcomes were worse among COVID-19-positive patients, including longer length of stay ( P <0.0001), greater percentage requiring intensive care unit care ( P =0.017), and greater rate of neurological worsening during admission ( P <0.0001); additionally, more COVID-19-positive patients suffered in-hospital death (33% versus 12.9%, P <0.0001). Conclusions: Baseline characteristics in patients with stroke were similar comparing those with and without COVID-19. However, COVID-19-positive patients were more likely to experience stroke in a lobar location, more commonly had a cryptogenic cause, and had worse outcomes.

Impaired inhibitory control accompanied by enhanced salience attributed to drug-related cues, both associated with function of the dorsolateral prefrontal cortex (dlPFC), are hallmarks of drug addiction, contributing to worse symptomatology including craving. dlPFC modulation with transcranial direct current stimulation (tDCS) previously showed craving reduction in inpatients with cocaine use disorder (CUD). Our study aimed at assessing feasibility of a longer tDCS protocol in CUD (15 versus the common five/10 sessions) and replicability of previous results. In a randomized double-blind sham-controlled protocol, 17 inpatients with CUD were assigned to either a real-tDCS (right anodal/left cathodal) or a sham-tDCS condition for 15 sessions. Following the previous report, primary outcome measures were self-reported craving, anxiety, depression, and quality of life. Secondary measures included sleepiness, readiness to change drug use, and affect. We also assessed cognitive function including impulsivity. An 88% retention rate demonstrated feasibility. Partially supporting the previous results, there was a trend for self-reported craving to decrease in the real-tDCS group more than the sham-group, an effect that would reach significance with 15 subjects per group. Quality of life and impulsivity improved over time in treatment in both groups. Daytime sleepiness and readiness to change drug use showed significant Group × Time interactions whereby improvements were noted only in the real-tDCS group. One-month follow-up suggested transient effects of tDCS on sleepiness and craving. These preliminary results suggest the need for including more subjects to show a unique effect of real-tDCS on craving and examine the duration of this effect. After replication in larger sample sizes, increased vigilance and motivation to change drug use in the real-tDCS group may suggest fortification of dlPFC-supported executive functions. K E Y W O R D S cocaine use disorder, dorsolateral prefrontal cortex, drug addiction, self-reported craving, transcranial direct current stimulation | 3213 GAUDREAULT ET AL.

Introduction Drug addiction is characterized by impaired response inhibition and salience attribution (iRISA), where the salience of drug cues is postulated to overpower that of other reinforcers with a concomitant decrease in self-control. However, the neural underpinnings of the interaction between the salience of drug cues and inhibitory control in drug addiction remain unclear. Methods We developed a novel stop-signal functional magnetic resonance imaging task where the stop-signal reaction time (SSRT–a classical inhibitory control measure) was tested under different salience conditions (modulated by drug, food, threat, or neutral words) in individuals with cocaine use disorder (CUD; n = 26) versus demographically matched healthy control participants (n = 26). Results Despite similarities in drug cue-related SSRT and valence and arousal word ratings between groups, dorsolateral prefrontal cortex (dlPFC) activity was diminished during the successful inhibition of drug versus food cues in CUD and was correlated with lower frequency of recent use, lower craving, and longer abstinence (Z &gt; 3.1, P &lt; 0.05 corrected). Discussion Results suggest altered involvement of cognitive control regions (e.g. dlPFC) during inhibitory control under a drug context, relative to an alternative reinforcer, in CUD. Supporting the iRISA model, these results elucidate the direct impact of drug-related cue reactivity on the neural signature of inhibitory control in drug addiction.

Background Over the last decade, increasing numbers of emergency department attendances and an even greater increase in emergency admissions have placed severe strain on the bed capacity of the National Health Service (NHS) of the United Kingdom. The result has been overcrowded emergency departments with patients experiencing long wait times for admission to an appropriate hospital bed. Nevertheless, scheduling issues can still result in significant underutilization of bed capacity. Bed occupancy rates may not correlate well with bed availability. More accurate and reliable long-term prediction of bed requirements will help anticipate the future needs of a hospital’s catchment population, thus resulting in greater efficiencies and better patient care. Objective This study aimed to evaluate widely used automated time-series forecasting techniques to predict short-term daily nonelective bed occupancy at all trusts in the NHS. These techniques were used to develop a simple yet accurate national health system–level forecasting framework that can be utilized at a low cost and by health care administrators who do not have statistical modeling expertise. Methods Bed occupancy models that accounted for patterns in occupancy were created for each trust in the NHS. Daily nonelective midnight trust occupancy data from April 2011 to March 2017 for 121 NHS trusts were utilized to generate these models. Forecasts were generated using the three most widely used automated forecasting techniques: exponential smoothing; Seasonal Autoregressive Integrated Moving Average; and Trigonometric, Box-Cox transform, autoregressive moving average errors, and Trend and Seasonal components. The NHS Modernisation Agency’s recommended forecasting method prior to 2020 was also replicated. Results The accuracy of the models varied on the basis of the season during which occupancy was forecasted. For the summer season, percent root-mean-square error values for each model remained relatively stable across the 6 forecasted weeks. However, only the trend and seasonal components model (median error=2.45% for 6 weeks) outperformed the NHS Modernisation Agency’s recommended method (median error=2.63% for 6 weeks). In contrast, during the winter season, the percent root-mean-square error values increased as we forecasted further into the future. Exponential smoothing generated the most accurate forecasts (median error=4.91% over 4 weeks), but all models outperformed the NHS Modernisation Agency’s recommended method prior to 2020 (median error=8.5% over 4 weeks). Conclusions It is possible to create automated models, similar to those recently published by the NHS, which can be used at a hospital level for a large national health care system to predict nonelective bed admissions and thus schedule elective procedures.

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