Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.

T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).

PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).

PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.

To date, the NIH Helping to End Addiction Long-term (HEAL) Initiative has funded over 1,000 projects that aim to identify new therapeutic targets for pain and substance use disorder (SUD), develop nonpharmacological strategies for pain management, and improve overdose and addiction treatment across settings. This study conducted a portfolio analysis of HEAL’s research to assess opportunities to advance translation and implementation.

HEAL projects (FY 2018–2022) were classified into early (T0–T1) and later (T2–T4) translational stages. Eleven coders used a 54-item data collection tool based on the Consolidated Framework for Implementation Research (CFIR) to extract project characteristics (e.g., population, research setting) relevant to translation and implementation. Descriptive statistics and visualization techniques were employed to analyze and map aggregate characteristics onto CFIR’s domains (e.g., outer setting).

HEAL’s portfolio comprised 923 projects (33.7% T0–T1; 67.3% T2–T4), ranging from basic science (27.1%) and preclinical research (21.4%) to clinical (36.8%), implementation (27.1%), and dissemination research (13.1%). Most projects primarily addressed either addiction (46.3%) or pain (37.4%). Implementation-related gaps included the underrepresentation of certain populations (e.g., sexual/gender minorities: 0.5%). T0–T1 projects occurred primarily in laboratory settings (35.1%), while T2–T4 projects were concentrated in healthcare settings (e.g., hospitals: 21.6%) with limited transferability to other contexts (e.g., community: 12.9%).

Opportunities to advance translational and implementation efforts include fostering interdisciplinary collaboration, prioritizing underserved populations, engaging with community leaders and policy stakeholders, and targeting evidence-based practices in nonclinical settings. Ongoing analyses can guide strategic investments to maximize HEAL’s impact on substance use and pain crises.

Duchenne muscular dystrophy is a devastating neuromuscular disorder characterized by the loss of dystrophin, inevitably leading to cardiomyopathy. Despite publications on prophylaxis and treatment with cardiac medications to mitigate cardiomyopathy progression, gaps remain in the specifics of medication initiation and optimization.

This document is an expert opinion statement, addressing a critical gap in cardiac care for Duchenne muscular dystrophy. It provides thorough recommendations for the initiation and titration of cardiac medications based on disease progression and patient response. Recommendations are derived from the expertise of the Advance Cardiac Therapies Improving Outcomes Network and are informed by established guidelines from the American Heart Association, American College of Cardiology, and Duchenne Muscular Dystrophy Care Considerations. These expert-derived recommendations aim to navigate the complexities of Duchenne muscular dystrophy-related cardiac care.

Comprehensive recommendations for initiation, titration, and optimization of critical cardiac medications are provided to address Duchenne muscular dystrophy-associated cardiomyopathy.

The management of Duchenne muscular dystrophy requires a multidisciplinary approach. However, the diversity of healthcare providers involved in Duchenne muscular dystrophy can result in variations in cardiac care, complicating treatment standardization and patient outcomes. The aim of this report is to provide a roadmap for managing Duchenne muscular dystrophy-associated cardiomyopathy, by elucidating timing and dosage nuances crucial for optimal therapeutic efficacy, ultimately improving cardiac outcomes, and improving the quality of life for individuals with Duchenne muscular dystrophy.

This document seeks to establish a standardized framework for cardiac care in Duchenne muscular dystrophy, aiming to improve cardiac prognosis.

Validate a public health model identifying patients at high risk for carbapenem-resistant Enterobacterales (CRE) on admission and evaluate performance across a healthcare network.

Retrospective case-control studies

Adults hospitalized with a clinical CRE culture within 3 days of admission (cases) and those hospitalized without a CRE culture (controls).

Using public health data from Atlanta, GA (1/1/2016–9/1/2019), we validated a CRE prediction model created in Chicago. We then closely replicated this model using clinical data from a healthcare network in Atlanta (1/1/2015–12/31/2021) (“Public Health Model”) and optimized performance by adding variables from the healthcare system (“Healthcare System Model”). We frequency-matched cases and controls based on year and facility. We evaluated model performance in validation datasets using area under the curve (AUC).

Using public health data, we matched 181 cases to 764,408 controls, and the Chicago model performed well (AUC 0.85). Using clinical data, we matched 91 cases to 384,013 controls. The Public Health Model included age, prior infection diagnosis, number of and mean length of stays in acute care hospitalizations (ACH) in the prior year. The final Healthcare System Model added Elixhauser score, antibiotic days of therapy in prior year, diabetes, admission to the intensive care unit in prior year and removed prior number of ACH. The AUC increased from 0.68 to 0.73.

A CRE risk prediction model using prior healthcare exposures performed well in a geographically distinct area and in an academic healthcare network. Adding variables from healthcare networks improved model performance.

Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Among inpatients, peer-comparison of prescribing metrics is challenging due to variation in patient-mix and prescribing by multiple providers daily. We established risk-adjusted provider-specific antibiotic prescribing metrics to allow peer-comparisons among hospitalists.

Using clinical and billing data from inpatient encounters discharged from the Hospital Medicine Service between January 2020 through June 2021 at four acute care hospitals, we calculated bimonthly (every two months) days of therapy (DOT) for antibiotics attributed to specific providers based on patient billing dates. Ten patient-mix characteristics, including demographics, infectious disease diagnoses, and noninfectious comorbidities were considered as potential predictors of antibiotic prescribing. Using linear mixed models, we identified risk-adjusted models predicting the prescribing of three antibiotic groups: broad spectrum hospital-onset (BSHO), broad-spectrum community-acquired (BSCA), and anti-methicillin-resistant Staphylococcus aureus (Anti-MRSA) antibiotics. Provider-specific observed-to-expected ratios (OERs) were calculated to describe provider-level antibiotic prescribing trends over time.

Predictors of antibiotic prescribing varied for the three antibiotic groups across the four hospitals, commonly selected predictors included sepsis, COVID-19, pneumonia, urinary tract infection, malignancy, and age >65 years. OERs varied within each hospital, with medians of approximately 1 and a 75th percentile of approximately 1.25. The median OER demonstrated a downward trend for the Anti-MRSA group at two hospitals but remained relatively stable elsewhere. Instances of heightened antibiotic prescribing (OER >1.25) were identified in approximately 25% of the observed time-points across all four hospitals.

Our findings indicate provider-specific benchmarking among inpatient providers is achievable and has potential utility as a valuable tool for inpatient stewardship efforts.

The incubation period for Clostridioides difficile infection (CDI) is generally considered to be less than 1 week, but some recent studies suggest that prolonged carriage prior to disease onset may be common.

To estimate the incubation period for patients developing CDI after initial negative cultures.

In 3 tertiary care medical centers, we conducted a cohort study to identify hospitalized patients and long-term care facility residents with negative initial cultures for C. difficile followed by a diagnosis of CDI with or without prior detection of carriage. Cases were classified as healthcare facility-onset, community-onset, healthcare facility-associated, or community-associated and were further classified as probable, possible, or unlikely CDI. A parametric accelerated failure time model was used to estimate the distribution of the incubation period.

Of 4,179 patients with negative enrollment cultures and no prior CDI diagnosis within 56 days, 107 (2.6%) were diagnosed as having CDI, including 19 (17.8%) with and 88 (82.2%) without prior detection of carriage. When the data were censored to only include participants with negative cultures collected within 14 days, the estimated median incubation period was 6 days with 25% and 75% of estimated incubation periods occurring within 3 and 12 days, respectively. The observed estimated incubation period did not differ significantly for patients classified as probable, possible, or unlikely CDI.

Our findings are consistent with the previous studies that suggested the incubation period for CDI is typically less than 1 week and is less than 2 weeks in most cases.

Anxiety is a common comorbid feature of late-life depression (LLD) and is associated with poorer global cognitive functioning independent of depression severity. However, little is known about whether comorbid anxiety is associated with a domain-specific pattern of cognitive dysfunction. We therefore examined group differences (LLD with and without comorbid anxiety) in cognitive functioning performance across multiple domains.

Older adults with major depressive disorder (N = 228, ages 65–91) were evaluated for anxiety and depression severity, and cognitive functioning (learning, memory, language, processing speed, executive functioning, working memory, and visuospatial functioning). Ordinary least squares regression adjusting for age, sex, education, and concurrent depression severity examined anxiety group differences in performance on tests of cognitive functioning.

Significant group differences emerged for confrontation naming and visuospatial functioning, as well as for verbal fluency, working memory, and inhibition with lower performance for LLD with comorbid anxiety compared to LLD only, controlling for depression severity.

Performance patterns identified among older adults with LLD and comorbid anxiety resemble neuropsychological profiles typically seen in neurodegenerative diseases of aging. These findings have potential implications for etiological considerations in the interpretation of neuropsychological profiles.

Knowledge of sex differences in risk factors for posttraumatic stress disorder (PTSD) can contribute to the development of refined preventive interventions. Therefore, the aim of this study was to examine if women and men differ in their vulnerability to risk factors for PTSD.

As part of the longitudinal AURORA study, 2924 patients seeking emergency department (ED) treatment in the acute aftermath of trauma provided self-report assessments of pre- peri- and post-traumatic risk factors, as well as 3-month PTSD severity. We systematically examined sex-dependent effects of 16 risk factors that have previously been hypothesized to show different associations with PTSD severity in women and men.

Women reported higher PTSD severity at 3-months post-trauma. Z-score comparisons indicated that for five of the 16 examined risk factors the association with 3-month PTSD severity was stronger in men than in women. In multivariable models, interaction effects with sex were observed for pre-traumatic anxiety symptoms, and acute dissociative symptoms; both showed stronger associations with PTSD in men than in women. Subgroup analyses suggested trauma type-conditional effects.

Our findings indicate mechanisms to which men might be particularly vulnerable, demonstrating that known PTSD risk factors might behave differently in women and men. Analyses did not identify any risk factors to which women were more vulnerable than men, pointing toward further mechanisms to explain women's higher PTSD risk. Our study illustrates the need for a more systematic examination of sex differences in contributors to PTSD severity after trauma, which may inform refined preventive interventions.

Understanding characteristics of healthcare personnel (HCP) with SARS-CoV-2 infection supports the development and prioritization of interventions to protect this important workforce. We report detailed characteristics of HCP who tested positive for SARS-CoV-2 from April 20, 2020 through December 31, 2021.

CDC collaborated with Emerging Infections Program sites in 10 states to interview HCP with SARS-CoV-2 infection (case-HCP) about their demographics, underlying medical conditions, healthcare roles, exposures, personal protective equipment (PPE) use, and COVID-19 vaccination status. We grouped case-HCP by healthcare role. To describe residential social vulnerability, we merged geocoded HCP residential addresses with CDC/ATSDR Social Vulnerability Index (SVI) values at the census tract level. We defined highest and lowest SVI quartiles as high and low social vulnerability, respectively.

Our analysis included 7,531 case-HCP. Most case-HCP with roles as certified nursing assistant (CNA) (444, 61.3%), medical assistant (252, 65.3%), or home healthcare worker (HHW) (225, 59.5%) reported their race and ethnicity as either non-Hispanic Black or Hispanic. More than one third of HHWs (166, 45.2%), CNAs (283, 41.7%), and medical assistants (138, 37.9%) reported a residential address in the high social vulnerability category. The proportion of case-HCP who reported using recommended PPE at all times when caring for patients with COVID-19 was lowest among HHWs compared with other roles.

To mitigate SARS-CoV-2 infection risk in healthcare settings, infection prevention, and control interventions should be specific to HCP roles and educational backgrounds. Additional interventions are needed to address high social vulnerability among HHWs, CNAs, and medical assistants.