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.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

The COVID-19 pandemic significantly disrupted schools and learning formats. Children with epilepsy are at-risk for generalized academic difficulties. We investigated the potential impact of COVID-19 on learning in those with epilepsy by comparing achievement on well-established academic measures among school-age children with epilepsy referred prior to the COVID-19 pandemic and those referred during the COVID-19 pandemic.

This study included 466 children [52% male, predominately White (76%), MAge=10.75 years] enrolled in the Pediatric Epilepsy Research Consortium Epilepsy (PERC) Surgery database project who were referred for surgery and seen for neuropsychological testing. Patients were divided into two groups based on a proxy measure of pandemic timing completed by PERC research staff at each site (i.e., “were there any changes to typical in-person administration [of the evaluation] due to COVID?”). 31% of the sample (N = 144) were identified as having testing during the pandemic (i.e., “yes” response), while 69% were identified as having testing done pre-pandemic (i.e., “no” response). Of the 31% who answered yes, 99% of administration changes pertained to in-person testing or other changes, with 1% indicating remote testing. Academic achievement was assessed by performance measures (i.e., word reading, reading comprehension, spelling, math calculations, and math word problems) across several different tests. T-tests compared the two groups on each academic domain. Subsequent analyses examined potential differences in academic achievement among age cohorts that approximately matched grade level [i.e., grade school (ages 5-10), middle school (ages 11-14), and high school (ages 15-18)].

No significant differences were found between children who underwent an evaluation before the pandemic compared to those assessed during the pandemic based on age norms across academic achievement subtests (all p’s > .34). Similarly, there were no significant differences among age cohorts. The average performance for each age cohort generally fell in the low average range across academic skills. Performance inconsistently varied between age cohorts. The youngest cohort (ages 5-10) scored lower than the other cohorts for sight-word reading, whereas this cohort scored higher than the middle cohort (ages 11-14) for math word problems and reading comprehension. There were no significant differences between the two pandemic groups on demographic variables, intellectual functioning, or epilepsy variables (i.e., age of onset, number of seizure medications, seizure frequency).

Academic functioning was generally equivalent between children with epilepsy who underwent academic testing as part of a pre-surgical evaluation prior to the pandemic compared to those who received testing during the pandemic. Additionally, academic functioning did not significantly differ between age cohorts. Children with epilepsy may have entered the pandemic with effective academic supports and/or were accustomed to school disruptions given their seizure history. Replication is needed as findings are based on a proxy measure of pandemic timing and the extent to which children experienced in-person, remote, and hybrid learning is unknown. Children tested a year into the pandemic, after receiving instruction through varying educational methods, may score differently than those tested earlier. Future research can address these gaps. Although it is encouraging that academic functioning was not disproportionately impacted during the pandemic in this sample, children with epilepsy are at-risk for generalized academic difficulties and continued monitoring of academic functioning is necessary.

The Pediatric Epilepsy Research Consortium (PERC) Epilepsy Surgery Database Project is a multisite collaborative that includes neuropsychological evaluations of children presenting for epilepsy surgery. There is some evidence for specific neuropsychological phenotypes within epilepsy (Hermann et al, 2016); however, this is less clear in pediatric patients. As a first step, we applied an empirically-based subtyping approach to determine if there were specific profiles using indices from the Wechsler scales [Verbal IQ (VIQ), Nonverbal IQ (NVIQ), Processing Speed Index (PSI), Working Memory Index (WMI)]. We hypothesized that there would be at least four profiles that are distinguished by slow processing speed and poor working memory as well as profiles with significant differences between verbal and nonverbal reasoning abilities.

Our study included 372 children (M=12.1 years SD=4.1; 77.4% White; 48% male) who completed an age-appropriate Wechsler measure, enough to render at least two index scores. Epilepsy characteristics included 84.4% with focal epilepsy (evenly distributed between left and right focus) and 13.5% with generalized or mixed seizure types; mean age of onset = 6.7 years, SD = 4.5; seizure frequency ranged from daily to less than monthly; 53% had structural etiology; 71% had an abnormal MRI; and mean number of antiseizure medications was two. Latent profile analysis was used to identify discrete underlying cognitive profiles based on intellectual functioning. Demographic and epilepsy characteristics were compared among profiles.

Based on class enumeration procedures, a 3-cluster solution provided the best fit for the data, with profiles characterized by generally Average, Low Average, or Below Average functioning. 32.8% were in the Average profile with mean index scores ranging from 91.7-103.2; 47.6% were in the Low Average profile with mean index ranging from 80.7 to 84.5; and 19.6% were in the Below Average profile with mean index scores ranging from 55.0-63.1. Across all profiles, the lowest mean score was the PSI, followed by WMI. VIQ and NVIQ represented relatively higher scores for all three profiles. Mean discrepancy between indices within a profile was as large as 11.5 IQ points. No demographics or epilepsy characteristics were significantly different across cognitive phenotypes.

Latent cognitive phenotypes in a pediatric presurgical cohort were differentiated by general level of functioning; however, across profiles, processing speed was consistently the lowest index followed by working memory. These findings across phenotypes suggest a common relative weakness which may result from a global effect of antiseizure medications and/or the widespread impact of seizures on neural networks even in a largely focal epilepsy cohort; similar to adult studies with temporal lobe epilepsy (Hermann et al, 2007). Future work will use latent profile analysis to examine phenotypes across other domains relevant to pediatric epilepsy including attention, naming, motor, and memory functioning. These findings are in line with collaborative efforts towards cognitive phenotyping which is the aim of our PERC Epilepsy Surgery Database Project that has already established one of the largest pediatric epilepsy surgery cohorts.

Children with epilepsy are at greater risk of lower academic achievement than their typically developing peers (Reilly and Neville, 2015). Demographic, social, and neuropsychological factors, such as executive functioning (EF), mediate this relation. While research emphasizes the importance of EF skills for academic achievement among typically developing children (e.g., Best et al., 2011; Spiegel et al., 2021) less is known among children with epilepsy (Ng et al., 2020). The purpose of this study is to examine the influence of EF skills on academic achievement in a nationwide sample of children with epilepsy.

Participants included 427 children with epilepsy (52% male; MAge= 10.71), enrolled in the Pediatric Epilepsy Research Consortium (PERC) Epilepsy Surgery Database who had been referred for surgery and underwent neuropsychological testing. Academic achievement was assessed by performance measures (word reading, reading comprehension, spelling, and calculation and word-based mathematics) and parent-rating measures (Adaptive Behavior Assessment System (ABAS) Functional Academics and Child Behavior Checklist (CBCL) School Performance). EF was assessed by verbal fluency measures, sequencing, and planning measures from the Delis Kaplan Executive Function System (DKEFS), NEPSY, and Tower of London test. Rating-based measures of EF included the 'Attention Problems’ subscale from the CBCL and 'Cognitive Regulation’ index from the Behavior Rating Inventory of Executive Function (BRIEF-2). Partial correlations assessed associations between EF predictors and academic achievement, controlling for fullscale IQ (FSIQ; A composite across intelligence tests). Significant predictors of each academic skill or rating were entered into a two-step regression that included FSIQ, demographics, and seizure variables (age of onset, current medications) in the first step with EF predictors in the second step.

Although zero-order correlations were significant between EF predictors and academic achievement (.29 < r’s < .63 for performance; -.63 < r’s < -.50 for rating measures), partial correlations controlling for FSIQ showed fewer significant relations. For performance-based EF, only letter fluency (DKEFS Letter Fluency) and cognitive flexibility (DKEFS Trails Condition 4) demonstrated significant associations with performance-based academic achievement (r’s > .29). Regression models for performance-based academic achievement indicated that letter fluency (ß = .22, p = .017) and CBCL attention problems (ß = -.21, p =.002) were significant predictors of sight-word reading. Only letter fluency (ß = .23, p =.006) was significant for math calculation. CBCL Attention Problems were a significant predictor of spelling performance (ß = -.21, p = .009) and reading comprehension (ß = -.18, p =.039). CBCL Attention Problems (ß = -.38, p <.001 for ABAS; ß = -.34, p =.002 for CBCL School) and BRIEF-2 Cognitive Regulation difficulties (ß = -.46, p < .001 for ABAS; ß = -.46, p =.013 for CBCL School) were significant predictors of parent-rated ABAS Functional Academics and CBCL School Performance.

Among a national pediatric epilepsy dataset, performance-based and ratings-based measures of EF predicted performance academic achievement, whereas only ratings-based EF predicted parent-rated academic achievement, due at least in part to shared method variance. These findings suggest that interventions that increase cognitive regulation, reduce symptoms of attention dysfunction, and promote self-generative, flexible thinking, may promote academic achievement among children with epilepsy.

Adults with congenital heart disease (CHD) face a unique set of medical, psychological, and social challenges, and access to specialised adult congenital heart disease care has been associated with improved outcomes. Rural adults with CHD may represent a uniquely disadvantaged group given additional challenges when accessing specialised care. The aim of this study was to investigate the challenges faced by adults with CHD in accessing outpatient cardiac care, with a specific focus on understanding differences between urban- and rural-dwelling patients.

This cross-sectional, survey-based study took place in the adult congenital heart disease clinic at an urban academic medical center. Additional medical information was abstracted in a retrospective manner from the electronic health record. In addition to descriptive statistics, t-tests and Chi-square tests were performed to investigate differences between urban and rural dwelling patients.

A total of 100 patients participated in the study (mean age 40 ± 13 years, 60% female, 18% rural dwelling). Across the total sample, the median driving distance to clinic was 20 miles (interquartile range 12–77); it was 15 miles for urban dwellers and 77 miles for rural dwelling patients (p < 0.001). The most commonly identified barriers to cardiac clinic visits were financial losses related to taking time off from work (39%), distance of clinic from home (33%), and weather (33%). Compared to urban dwelling patients, on average those who were rural dwelling had a lower level of education (p = 0.04), more difficulty paying insurance premiums (p < 0.001) and copays (p = 0.005), and were more likely to identify the distance from clinic (p = 0.05) and having to go into the city (p = 0.02) as barriers to clinic appointments.

The financial impact and distance to clinic were the most commonly identified barriers to outpatient cardiac care in this cohort of adults with CHD. These barriers, along with difficulty paying insurance premiums, are more common in rural dwelling patients. Initiatives such as telemedicine visits or providing financial subsidies for travel and treatment could help to expand specialty adult congenital heart disease care and better serve this growing patient population.

Psychiatry in the UK has longstanding recruitment problems (1). Evidence suggests the positive effects of clinical attachments on attitudes towards psychiatry are often transient (2). We therefore created the Psychiatry Early Experience Programme (PEEP) where year 1 medical students are paired with psychiatry trainees and shadow them at work. Students will ideally remain in PEEP throughout medical school, providing consistent exposure to psychiatry and a broad experience of its subspecialties.

1. 1. To present PEEP

2. 2. To assess:

   1. a. Students’ baseline attitudes to psychiatry

   2. b. PEEPs’ impact on students’ attitudes to psychiatry

Design

A prospective survey based cohort study of King’s College London medical students.

PEEP started in 2013. In this cohort all students that signed up were accepted.

Students’ attitudes towards psychiatry were assessed on recruitment using the ATP-30 questionnaire (3), and will be re-assessed annually.

127 students were recruited. Attitudes were positive overall. 73% listed psychiatry in their top three specialities. 95.3% agreed or strongly agreed that ‘psychiatric illness deserves at least as much attention as physical illness.’ 84.3% disagreed or strongly disagreed that ‘at times it is hard to think of psychiatrists as equal to other doctors.’

Baseline attitudes to psychiatry were positive. By March 2015 we aim to collect and analyse data on students’ attitudes after one year in PEEP. Through on-ongoing analysis of this and future cohorts, we aim to assess the impact of PEEP on improving attitudes to psychiatry and whether this will ultimately improve recruitment.

Traumatic brain injuries (TBI) may lead to persistent depression symptoms. We conducted several pilot studies to examine the efficacy of mindfulness-based interventions to deal with this issue; all showed strong effect sizes. The logical next step was to conduct a randomized controlled trial (RCT).

We sought to determine the efficacy of mindfulness-based cognitive therapy for people with depression symptoms post-TBI (MBCT-TBI).

Using a multi-site RCT design, participants (mean age = 47) were randomized to intervention or control arms. Treatment participants received a group-based, 10-week intervention; control participants waited. Outcome measures, administered pre- and post-intervention, and after three months, included: Beck Depression Inventory-II (BDI-II), Patient Health Questionnaire-9 (PHQ-9), and Symptom Checklist-90-Revised (SCL-90-R). The Philadelphia Mindfulness Scale (PHLMS) captured present moment awareness and acceptance.

BDI-II scores decreased from 25.47 to 18.84 in treatment groups while they stayed relatively stable in control groups (respectively 27.13 to 25.00; p = .029). We did not find statistically significant differences on the PHQ-9 and SCL-90R post- treatment. However, after three months, all scores were statistically significantly lower than at baseline (ps < .01). Increases in mindfulness were associated with decreases in BDI-II scores (r[29] = -.401, p = .025).

MBCT-TBI may alleviate depression symptoms up to three months post-intervention. Greater mindfulness may have contributed to the reduction in depression symptoms although the association does not confirm causality. More work is required to replicate these findings, identify subgroups that may better respond to the intervention, and refine the intervention to maximize its effectiveness.

The authors have not supplied their declaration of competing interest.