In response to the COVID-19 pandemic, we rapidly implemented a plasma coordination center, within two months, to support transfusion for two outpatient randomized controlled trials. The center design was based on an investigational drug services model and a Food and Drug Administration-compliant database to manage blood product inventory and trial safety.

A core investigational team adapted a cloud-based platform to randomize patient assignments and track inventory distribution of control plasma and high-titer COVID-19 convalescent plasma of different blood groups from 29 donor collection centers directly to blood banks serving 26 transfusion sites.

We performed 1,351 transfusions in 16 months. The transparency of the digital inventory at each site was critical to facilitate qualification, randomization, and overnight shipments of blood group-compatible plasma for transfusions into trial participants. While inventory challenges were heightened with COVID-19 convalescent plasma, the cloud-based system, and the flexible approach of the plasma coordination center staff across the blood bank network enabled decentralized procurement and distribution of investigational products to maintain inventory thresholds and overcome local supply chain restraints at the sites.

The rapid creation of a plasma coordination center for outpatient transfusions is infrequent in the academic setting. Distributing more than 3,100 plasma units to blood banks charged with managing investigational inventory across the U.S. in a decentralized manner posed operational and regulatory challenges while providing opportunities for the plasma coordination center to contribute to research of global importance. This program can serve as a template in subsequent public health emergencies.

Recent conceptualizations of concussion symptoms have begun to shift from a latent perspective (which suggests a common cause; i.e., head injury), to a network perspective (where symptoms influence and interact with each other throughout injury and recovery). Recent research has examined the network structure of the Post-Concussion Symptom Scale (PCSS) cross-sectionally at pre-and post-concussion, with the most important symptoms including dizziness, sadness, and feeling more emotional. However, within-subject comparisons between network structures at pre-and post-concussion have yet to be made. These analyses can provide invaluable information on whether concussion alters symptom interactions. This study examined within-athlete changes in PCSS network connectivity and centrality (the importance of different symptoms within the networks) from baseline to post-concussion.

Participants were selected from a larger longitudinal database of high school athletes who completed the PCSS in English as part of their standard athletic training protocol (N=1,561). The PCSS is a 22-item self-report measure of common concussion symptoms (i.e., headache, vomiting, dizziness, etc.) in which individuals rate symptom severity on a 7-point Likert scale. Participants were excluded if they endorsed history of brain surgery, neurodevelopmental disorder, or treatment history for epilepsy, migraines, psychiatric disorders, or alcohol/substance use. Network analysis was conducted on PCSS ratings from a baseline and acute post-concussion (within 72-hours post-injury) assessment. In each network, the nodes represented individual symptoms, and the edges connecting them their partial correlations. Estimations of the regularized partial correlation networks were completed using the Gaussian graphical model, and the GLASSO algorithm was used for regularization. Each symptom’s expected influence (the sum of its partial correlations with other symptoms) was calculated to identify the most central symptoms in each network. Recommended techniques from Epskamp et al. (2018) were completed for assessing the accuracy of the estimated symptom importance and relationships. Network Comparison Tests were conducted to observe changes in network connectivity, structure, and node influence.

Both baseline and acute post-concussion networks contained negative and positive relationships. The expected influence of symptoms was stable in both networks, with difficulty concentrating having the greatest expected influence in both. The strongest edges in the networks were between symptoms within similar domains of functioning (e.g., sleeping less was associated with trouble falling asleep). Network connectivity was not significantly different between networks (S=0.43), suggesting the overall degree to which symptoms are related was not different at acute post-concussion. Network structure significantly differed at acute post-concussion (M=0.305), suggesting specific relationships in the acute post-concussion network were different than they were at baseline. In the acute post concussion network, vomiting was less central and sensitivity to noise and mentally foggy more central.

PCSS network structure at acute post-concussion is altered, suggesting concussion may disrupt symptom networks and certain symptoms’ associations with the experience of others after sustaining a concussive injury. Future research should compare PCSS networks later in recovery to examine if similar structural changes remain or return to baseline structure, with the potential that observing PCSS network structure changes post-concussion could inform symptom resolution trajectories.

Long-term exposure to the psychoactive ingredient in cannabis, delta-9-tetrahydrocanabinol (THC), has been consistently raised as a notable risk factor for schizophrenia. Additionally, cannabis is frequently used as a coping mechanism for individuals diagnosed with schizophrenia. Cannabis use in schizophrenia has been associated with greater severity of psychotic symptoms, non-compliance with medication, and increased relapse rates. Neuropsychological changes have also been implicated in long-term cannabis use and the course of illness of schizophrenia. However, the impact of co-occurring cannabis use in individuals with schizophrenia on cognitive functioning is less thoroughly explored. The purpose of this meta-analysis was to examine whether neuropsychological test performance and symptoms in schizophrenia differ as a function of THC use status. A second aim of this study was to examine whether symptom severity moderates the relationship between THC use and cognitive test performance among people with schizophrenia.

Peer-reviewed articles comparing schizophrenia with and without cannabis use disorder (SZ SUD+; SZ SUD-) were selected from three scholarly databases; Ovid, Google Scholar, and PubMed. The following search terms were applied to yield studies for inclusion: neuropsychology, cognition, cognitive, THC, cannabis, marijuana, and schizophrenia. 11 articles containing data on psychotic symptoms and neurocognition, with SZ SUD+ and SZ SUD- groups, were included in the final analyses. Six domains of neurocognition were identified across included articles (Processing Speed, Attention, Working Memory, Verbal Learning Memory, and Reasoning and Problem Solving). Positive and negative symptom data was derived from eligible studies consisting of the Positive and Negative Syndrome Scale (PANSS), the Scale for the Assessment of Positive Symptoms (SAPS), the Scale for the Assessment of Negative Symptoms (SANS), Self-Evaluation of Negative Symptoms (SNS), Brief Psychiatric Rating Scale (BPRS), and Structured Clinical Interview for DSM Disorders (SCID) scores. Meta analysis and meta-regression was conducted using R.

No statistically significant differences were observed between SZ SUD+ and SZ SUD-across the cognitive domains of Processing Speed, Attention, Working Memory, Verbal Learning Memory, and Reasoning and Problem Solving. Positive symptom severity was found to moderate the relationship between THC use and processing speed, but not negative symptoms. Positive and negative symptom severity did not significantly moderate the relationship between THC use and the other cognitive domains.

Positive symptoms moderated the relationship between cannabis use and processing speed among people with schizophrenia. The reasons for this are unclear, and require further exploration. Additional investigation is warranted to better understand the impact of THC use on other tests of neuropsychological performance and symptoms in schizophrenia.

The rapid spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) throughout key regions of the United States in early 2020 placed a premium on timely, national surveillance of hospital patient censuses. To meet that need, the Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN), the nation’s largest hospital surveillance system, launched a module for collecting hospital coronavirus disease 2019 (COVID-19) data. We present time-series estimates of the critical hospital capacity indicators from April 1 to July 14, 2020.

From March 27 to July 14, 2020, the NHSN collected daily data on hospital bed occupancy, number of hospitalized patients with COVID-19, and the availability and/or use of mechanical ventilators. Time series were constructed using multiple imputation and survey weighting to allow near–real-time daily national and state estimates to be computed.

During the pandemic’s April peak in the United States, among an estimated 431,000 total inpatients, 84,000 (19%) had COVID-19. Although the number of inpatients with COVID-19 decreased from April to July, the proportion of occupied inpatient beds increased steadily. COVID-19 hospitalizations increased from mid-June in the South and Southwest regions after stay-at-home restrictions were eased. The proportion of inpatients with COVID-19 on ventilators decreased from April to July.

The NHSN hospital capacity estimates served as important, near–real-time indicators of the pandemic’s magnitude, spread, and impact, providing quantitative guidance for the public health response. Use of the estimates detected the rise of hospitalizations in specific geographic regions in June after they declined from a peak in April. Patient outcomes appeared to improve from early April to mid-July.

The Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) is commonly used to assist with post-concussion return-to-play decisions for athletes. Additional investigation is needed to determine whether embedded indicators used to determine the validity of scores are influenced by the presence of neurodevelopmental disorders (NDs).

This study examined standard and novel ImPACT validity indicators in a large sample of high school athletes (n = 33,772) with or without self-reported ND.

Overall, 7.1% of athletes’ baselines were judged invalid based on standard ImPACT validity criteria. When analyzed by group (healthy, ND), there were significantly more invalid ImPACT baselines for athletes with an ND diagnosis or special education history (between 9.7% and 54.3% for standard and novel embedded validity criteria) when compared to athletes without NDs. ND history was a significant predictor of invalid baseline performance above and beyond other demographic characteristics (i.e., age, sex, and sport), although it accounted for only a small percentage of variance. Multivariate base rates are presented stratified for age, sex, and ND.

These data provide evidence of higher than normal rates of invalid baselines in athletes who report ND (based on both the standard and novel embedded validity indicators). Although ND accounted for a small percentage of variance in the prediction of invalid performance, negative consequences (e.g., extended time out of sports) of incorrect decision-making should be considered for those with neurodevelopmental conditions. Also, reasons for the overall increase noted here, such as decreased motivation, “sandbagging”, or disability-related cognitive deficit, require additional investigation.

There is limited understanding of the cognitive profiles of Spanish-speaking children with Attention-Deficit/Hyperactivity Disorder (ADHD). The current study investigated the cognitive cluster profiles of Puerto Rican Spanish-speaking children with ADHD using the Wechsler Intelligence Scales for Children-Fourth Edition Spanish (WISC-IV Spanish) Index scores and examined the association between cognitive cluster profiles with other potentially relevant factors.

Hierarchical cluster analysis was used to identify WISC-IV clusters in a sample of 165 Puerto Rican children who had a primary diagnosis of ADHD. To examine the validity of the ADHD clusters, analysis of variances and chi-square analyses were conducted to compare the clusters across sociodemographics (e.g., age and education), type of ADHD diagnosis (ADHD subtype, Learning Disorder comorbidity), and academic achievement.

Clusters were differentiated by level and pattern of performance. A five-cluster solution was identified as optimal that included (C1) multiple cognitive deficits, (C2) processing speed deficits, (C3) generally average performance, (C4) perceptual reasoning strengths, and (C5) working memory deficits. Among the five clusters, the profile with multiple cognitive deficits was characterized by poorer performance on the four WISC-IV Spanish Indexes and was associated with adverse sociodemographic characteristics.

Results illustrate that there is substantial heterogeneity in cognitive abilities of Puerto Rican Spanish-speaking children with ADHD, and this heterogeneity is associated with a number of relevant outcomes.

Disturbances in trait emotions are a predominant feature in schizophrenia. However, less is known about (a) differences in trait emotion across phases of the illness such as the clinical high-risk (CHR) phase and (b) whether abnormalities in trait emotion that are associated with negative symptoms are driven by primary (i.e. idiopathic) or secondary (e.g. depression, anxiety) factors.

To examine profiles of trait affective disturbance and their clinical correlates in individuals with schizophrenia and individuals at CHR for psychosis.

In two studies (sample 1: 56 out-patients diagnosed with schizophrenia and 34 demographically matched individuals without schizophrenia (controls); sample 2: 50 individuals at CHR and 56 individuals not at CHR (controls)), participants completed self-report trait positive affect and negative affect questionnaires, clinical symptom interviews (positive, negative, disorganised, depression, anxiety) and community-based functional outcome measures.

Both clinical groups reported lower levels of positive affect (specific to joy among individuals with schizophrenia) and higher levels of negative affect compared with controls. For individuals with schizophrenia, links were found between positive affect and negative symptoms (which remained after controlling for secondary factors) and between negative affect and positive symptoms. For individuals at CHR, links were found between both affect dimensions and both types of symptom (which were largely accounted for by secondary factors).

Both clinical groups showed some evidence of reduced trait positive affect and elevated trait negative affect, suggesting that increasing trait positive affect and reducing trait negative affect is an important treatment goal across both populations. Clinical correlates of these emotional abnormalities were more integrally linked to clinical symptoms in individuals with schizophrenia and more closely linked to secondary influences such as depression and anxiety in individuals at CHR.

None.

In an effort to optimize patient outcomes, considerable attention is being devoted to identifying patient characteristics associated with major depressive disorder (MDD) and its responsiveness to treatment. In the current study, we extend this work by evaluating whether early change in these sensitivities is associated with response to antidepressant treatment for MDD.

Participants included 210 patients with MDD who were treated with 8 weeks of escitalopram and 112 healthy comparison participants. Of the original 210 patients, 90 non-responders received adjunctive aripiprazole for an additional 8 weeks. Symptoms of depression and anhedonia were assessed at the beginning of treatment and 8 weeks later in both samples. Reward and punishment sensitivity were assessed using the BIS/BAS scales measured at the initiation of treatment and 2 weeks later.

Individuals with MDD exhibited higher punishment sensitivity and lower reward sensitivity compared with healthy comparison participants. Change in reward sensitivity during the first 2 weeks of treatment was associated with improved depressive symptoms and anhedonia following 8 weeks of treatment with escitalopram. Similarly, improvement in reward responsiveness during the first 2 weeks of adjunctive therapy with aripiprazole was associated with fewer symptoms of depression at post-treatment.

Findings highlight the predictive utility of early change in reward sensitivity during antidepressant treatment for major depression. In a clinical setting, a lack of change in early reward processing may signal a need to modify a patient's treatment plan with alternative or augmented treatment approaches.