Altered prefrontal signaling during inhibitory control in a salient drug context in cocaine use disorder.

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 > 3.1, P < 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.

Leveraging technology to address unhealthy drug use in primary care: Effectiveness of the Substance use Screening and Intervention Tool (SUSIT).

Background: Screening for unhealthy drug use is now recommended for adult primary care patients, but primary care providers (PCPs) generally lack the time and knowledge required to screen and deliver an intervention during the medical visit. To address these barriers, we developed a tablet computer-based 'Substance Use Screening and Intervention Tool (SUSIT)'. Using the SUSIT, patients self-administer screening questionnaires prior to the medical visit, and results are presented to the PCP at the point of care, paired with clinical decision support (CDS) that guides them in providing a brief intervention (BI) for unhealthy drug use. Methods: PCPs and their patients with moderate-risk drug use were recruited from primary care and HIV clinics. A pre-post design compared a control 'screening only' (SO) period to an intervention 'SUSIT' period. Unique patients were enrolled in each period. In both conditions, patients completed screening and identified their drug of most concern (DOMC) before the visit, and completed a questionnaire about BI delivery by the PCP after the visit. In the SUSIT condition only, PCPs received the tablet with the patient's screening results and CDS. Multilevel models with random intercepts and patients nested within PCPs examined the effect of the SUSIT intervention on PCP delivery of BI. Results: 20 PCPs and 79 patients (42 SO, 37 SUSIT) participated. Most patients had moderate-risk marijuana use (92.4%), and selected marijuana as the DOMC (68.4%). Moderate-risk use of drugs other than marijuana included cocaine (15.2%), hallucinogens (12.7%), and sedatives (12.7%). Compared to the SO condition, patients in SUSIT had higher odds of receiving any BI for drug use, with an adjusted odds ratio of 11.59 (95% confidence interval: 3.39, 39.25), and received more elements of BI for drug use. Conclusions: The SUSIT significantly increased delivery of BI for drug use by PCPs during routine primary care encounters.

Acute Cerebrovascular Events With COVID-19 Infection.

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.

A double-blind sham-controlled phase 1 clinical trial of tDCS of the dorsolateral prefrontal cortex in cocaine inpatients: Craving, sleepiness, and contemplation to change.

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.

Hypertension, diabetes, and corresponding annual clinical testing utilization: Comparison between Asian Indians and other races/ethnicities.

Asian Indians are at increased risk of developing cardiometabolic diseases. We sought to determine differences between Asian Indians and other races/ethnicities in hypertension and diabetes prevalence and associated annual blood pressure (BP) and fasting blood glucose (FBG) testing. A total of 257,652 adults ≥18 years from the 2011-2018 U.S. National Health Interview Surveys (NHIS) were included. BP and FBG testing in the past 12 months was defined dichotomously (yes/not yes). Racial/ethnic groups included non-Hispanic White (NHW), non-Hispanic Black (NHB), Asian Indian, Other Asians, and Hispanic/Multiracial. We used logistic regression, adjusting for covariates and the survey design. Analyses were completed from 08/2020-06/2021. Asian Indians (N = 3049) had 21% and 99% higher odds of hypertension and diabetes, respectively, than NHWs (aOR [95% CI]; hypertension: 1.21[1.04,1.40], diabetes: 1.99[1.64,2.41]). Accordingly, Asian Indians without diabetes had significantly higher odds of FBG screening than NHWs (Asian Indian: 1.41[1.25,1.59], NHB: 0.99 [0.95,1.04], Other Asian: 1.07[0.98, 1.18], Hispanic: 1.13[1.07,1.20]). Asian Indians without hypertension had a 14% insignificant increase in BP testing compared to NHWs (1.14[0.97,1.33]). Predictors of testing in Asian Indians included older age, doctor's visit, graduate-level education, insurance coverage, and history of hypertension or diabetes. NHBs with diabetes and Hispanics with hypertension had lower odds of FBG testing (0.75[0.66,0.84]) and BP testing (0.85[0.79,0.92]), respectively, than NHWs. Asian Indians have higher odds of diabetes and hypertension than NHWs and higher, but relatively lower, odds of FBG and BP testing. Increasing routine BP and FBG testing in Asian Indians in younger adults may allow for earlier detection of high-risk individuals.

Variation in US acute ischemic stroke treatment by hospital regions: limited endovascular access despite evidence.

BACKGROUND: Although national organizations recognize the importance of regionalized acute ischemic stroke (AIS) care, data informing expansion are sparse. We assessed real-world regional variation in emergent AIS treatment, including growth in revascularization therapies and stroke center certification. We hypothesized that we would observe overall growth in revascularization therapy utilization, but observed differences would vary greatly regionally. METHODS: A retrospective cross-sectional analysis was carried out of de-identified national inpatient Medicare Fee-for-Service datasets from 2016 to 2019. We identified AIS admissions and treatment with thrombolysis and endovascular thrombectomy (ET) with International Classification of Diseases, 10th Revision, Clinical Modification codes. We grouped hospitals in Dartmouth Atlas of Healthcare Hospital Referral Regions (HRR) and calculated hospital, demographic, and acute stroke treatment characteristics for each HRR. We calculated the percent of hospitals with stroke certification and AIS cases treated with thrombolysis or ET per HRR. RESULTS: There were 957 958 AIS admissions. Relative mean (SD) growth in percent of AIS admissions receiving revascularization therapy per HRR from 2016 to 2019 was 13.4 (31.7)% (IQR -6.1-31.7%) for thrombolysis and 28.0 (72.0)% (IQR 0-56.0%) for ET. The proportion of HRRs with decreased or no difference in ET utilization was 38.9% and the proportion of HRRs with decreased or no difference in thrombolysis utilization was 32.7%. Mean (SD) stroke center certification proportion across HRRs was 45.3 (31.5)% and this varied widely (IQR 18.3-73.4%). CONCLUSIONS: Overall growth in AIS treatment has been modest and, within HRRs, growth in AIS treatment and the proportion of centers with stroke certification varies dramatically.

Forecasting the Requirement for Nonelective Hospital Beds in the National Health Service of the United Kingdom: Model Development Study.

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.