The grief of relatives of patients who died of COVID-19 in an intensive care unit (ICU) has exacted an enormous toll worldwide.

To determine the prevalence of probable prolonged grief disorder (PGD) at 12 months post-loss and beyond. We also sought to examine circumstances of the death during the COVID-19 pandemic that might pose a heightened risk of PGD, and the associations between probable PGD diagnosis, quality of life and social disconnection.

We conducted an observational, cross-sectional multicentre study of the next of kin of those who died of COVID-19 between March 2020 and December 2021. Participants were recruited from ICUs in South-East London. The Prolonged Grief Disorder Scale (PG-13-R), Quality-of-Life Scale (QOLS) and Oxford Grief-Social Disconnection Scale (OG-SD) were used.

A total of 73 relatives were recruited and assessed, all of them over a year after their loss. Twenty-five (34.2%; 95% CI 23.1–45.4%) relatives of patients who died in the ICU met the criteria for PGD. Those who met the criteria had significantly worse quality of life (QOLS score mean difference 26; 95% CI 17–34; P < 0.001) and endorsed greater social disconnection (OG-SD score means difference 41; 95% CI 27–54; P < 0.001).

The findings suggest that rates of PGD are elevated among relatives of patients who died of COVID-19 in the ICU. This, coupled with worse quality of life and greater social disconnection experienced by those meeting the criteria, suggests the need to attend to the social deprivations and social dysfunctions of this population group.

Return to driving after moderate-to-severe traumatic brain injury (TBI) is often a key step in recovery to regain independence. Survivors are often eager to resume driving and may do so despite having residual cognitive limitations from their injury. A better understanding is needed of how cognition and self-awareness impact survivors’ driving after injury. This study examined the influence of cognition and self-awareness on driving patterns following moderate-to-severe TBI.

Participants were 350 adults aged 19-87 years (mean age = 46 years; 70% male) with history of moderate-to-severe TBI, who resumed driving and were enrolled in the TBI Model System. Cross-sectional data were obtained ranging 1-30 years post injury, including questions on driving practices, the Brief Test of Adult Cognition by Telephone (BTACT), and the Functional Independence Measure (FIM). Self-awareness of cognitive function was measured via the discrepancy between dichotomized ratings (intact versus impaired) of objective cognitive testing (BTACT) and self-reported cognitive function (FIM Cognition subscale). Driving patterns included frequency (driving 'more than once a week’ versus 'once a week or less') and restricted driving behavior (total number of driving situations the survivor described as restricted, ranging 0-15). Regression analyses were conducted to examine the relationships between cognition, self-awareness, and each driving outcome (frequency and restriction), followed by causal mediation analyses to examine the mediating effect of self-awareness. Demographics (age, sex, education), injury characteristics (time since injury, injury severity, history of seizures in past year), and medical/social factors (family income, motor function, urban-rural classification) were included in the models as covariates.

Thirty-nine percent of survivors had impaired self-awareness, 88% of survivors drove numerous times per week, and the average survivor reported limited driving in 6 situations (out of 15 total situations). Cognition was inversely related to impaired self-awareness (OR = 0.03, p < 0.001) and inversely related to restricted driving behavior (b = -0.79, p < 0.001). Motor function was positively related to impaired self-awareness (OR = 1.28, p < 0.01). Cognition was not related to driving frequency, and self-awareness did not mediate the relationships between cognition and driving patterns (all p > 0.05).

Most survivors who drive after their injury are driving frequently, but the situations they drive in differ based on their cognitive ability. Impaired self-awareness of cognitive deficits is common after TBI, and self-awareness of cognitive function does not affect driving patterns. Future research needs to focus on how cognition affects nuanced aspects of driving behavior after injury (i.e., types of situations survivors drive in).

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

The use of online platforms for pediatric healthcare research is timely, given the current pandemic. These platforms facilitate trial efficiency integration including electronic consent, randomization, collection of patient/family survey data, delivery of an intervention, and basic data analysis.

We created an online digital platform for a multicenter study that delivered an intervention for sleep disorders to parents of children with autism spectrum disorder (ASD). An advisory parent group provided input. Participants were randomized to receive either a sleep education pamphlet only or the sleep education pamphlet plus three quick-tips sheets and two videos that reinforced the material in the pamphlet (multimedia materials). Three measures – Family Inventory of Sleep Habits (FISH), Children’s Sleep Habits Questionnaire modified for ASD (CSHQ-ASD), and Parenting Sense of Competence (PSOC) – were completed before and after 12 weeks of sleep education.

Enrollment exceeded recruitment goals. Trial efficiency was improved, especially in data entry and automatic notification of participants related to survey completion. Most families commented favorably on the study. While study measures did not improve with treatment in either group (pamphlet or multimedia materials), parents reporting an improvement of ≥3 points in the FISH score showed a significantly improved change in the total CSHQ (P = 0.038).

Our study demonstrates the feasibility of using online research delivery platforms to support studies in ASD, and more broadly, pediatric clinical and translational research. Online platforms may increase participant inclusion in enrollment and increase convenience and safety for participants and study personnel.

Stop the Bleed (STB) is a national initiative that provides lifesaving hemorrhagic control education. In 2019, pharmacists were added as health-care personnel eligible to become STB instructors. This study was conducted to evaluate the efficacy of pharmacist-led STB trainings for school employees in South Texas.

Pharmacist-led STB trainings were provided to teachers and staff in Laredo, Texas. The 60-min trainings included a presentation followed by hands-on practice of tourniquet application, wound-packing, and direct pressure application. Training efficacy was assessed through anonymous pre- and postevent surveys, which evaluated changes in knowledge, comfort level, and willingness to assist in hemorrhage control interventions. Student volunteers (predominantly pharmacy and medical students) assisted in leading the hands-on portion, providing a unique interprofessional learning opportunity.

Participants with previous training (N = 98) were excluded, resulting in a final cohort of 437 (response rate 87.4%). Compared with baseline, comfort level using tourniquets (mean, 3.17/5 vs 4.20/5; P < 0.0001), opinion regarding tourniquet safety (2.59/3 vs 2.94/3; P < 0.0001), and knowledge regarding tourniquets (70.86/100 vs 75.84/100; P < 0.0001) and proper tourniquet placement (2.40/4 vs 3.15/4; P < 0.0001) significantly improved.

Pharmacist-led STB trainings are efficacious in increasing school worker knowledge and willingness to respond in an emergency hemorrhagic situation.