Prior reports of healthcare-associated respiratory syncytial virus (RSV) have been limited to cases diagnosed after the third day of hospitalization. The omission of other healthcare settings where RSV transmission may occur underestimates the true incidence of healthcare-associated RSV.

Retrospective cross-sectional study.

United States RSV Hospitalization Surveillance Network (RSV-NET) during 2016–2017 through 2018–2019 seasons.

Laboratory-confirmed RSV-related hospitalizations in an eight-county catchment area in Tennessee.

Surveillance data from RSV-NET were used to evaluate the population-level burden of healthcare-associated RSV. The incidence of healthcare-associated RSV was determined using the traditional definition (i.e., positive RSV test after hospital day 3) in addition to often under-recognized cases associated with recent post-acute care facility admission or a recent acute care hospitalization for a non-RSV illness in the preceding 7 days.

Among the 900 laboratory-confirmed RSV-related hospitalizations, 41 (4.6%) had traditionally defined healthcare-associated RSV. Including patients with a positive RSV test obtained in the first 3 days of hospitalization and who were either transferred to the hospital directly from a post-acute care facility or who were recently discharged from an acute care facility for a non-RSV illness in the preceding 7 days identified an additional 95 cases (10.6% of all RSV-related hospitalizations).

RSV is an often under-recognized healthcare-associated infection. Capturing other healthcare exposures that may serve as the initial site of viral transmission may provide more comprehensive estimates of the burden of healthcare-associated RSV and inform improved infection prevention strategies and vaccination efforts.

Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points.

We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367).

Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals.

Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

The clinical high risk for psychosis (CHR-p) syndrome enables early identification of individuals at risk of schizophrenia and related disorders. We differentiate between the stigma associated with the at-risk identification itself (‘labelling-related’ stigma) versus stigma attributed to experiencing mental health symptoms (‘symptom-related’ stigma) and examine their relationships with key psychosocial variables.

We compare labelling- and symptom-related stigma in rates of endorsement and associations with self-esteem, social support loss and quality of life.

We assessed stigma domains of shame-related emotions, secrecy and experienced discrimination for both types of stigma. Individuals at CHR-p were recruited across three sites (N = 150); primary analyses included those who endorsed awareness of psychosis risk (n = 113). Paired-sample t-tests examined differences in labelling- versus symptom-related stigma; regressions examined associations with psychosocial variables, controlling for covariates, including CHR-p symptoms.

Respondents reported greater symptom-related shame, but more labelling-related secrecy. Of the nine significant associations between stigma and psychosocial variables, eight were attributable to symptom-related stigma, even after adjusting for CHR-p symptoms.

Stigma attributed to symptoms had a stronger negative association with psychosocial variables than did labelling-related stigma among individuals recently identified as CHR-p. That secrecy related to the CHR-p designation was greater than its symptom-related counterpart suggests that labelling-related stigma may still be problematic for some CHR-p participants. To optimise this pivotal early intervention effort, interventions should address the holistic ‘stigmatising experience’ of having symptoms, namely any harmful reactions received as well as participants’ socially influenced concerns about what their experiences mean, in addition to the symptoms themselves.

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.

Passive oxygenation with non-rebreather face mask (NRFM) has been used during cardiac arrest as an alternative to positive pressure ventilation (PPV) with bag-valve-mask (BVM) to minimize chest compression disruptions. A dual-channel pharyngeal oxygen delivery device (PODD) was created to open obstructed upper airways and provide oxygen at the glottic opening. It was hypothesized for this study that the PODD can deliver oxygen as efficiently as BVM or NRFM and oropharyngeal airway (OPA) in a cardiopulmonary resuscitation (CPR) manikin model.

Oxygen concentration was measured in test lungs within a resuscitation manikin. These lungs were modified to mimic physiologic volumes, expansion, collapse, and recoil. Automated compressions were administered. Five trials were performed for each of five arms: (1) CPR with 30:2 compression-to-ventilation ratio using BVM with 15 liters per minute (LPM) oxygen; continuous compressions with passive oxygenation using (2) NRFM and OPA with 15 LPM oxygen, (3) PODD with 10 LPM oxygen, (4) PODD with 15 LPM oxygen; and (5) control arm with compressions only.

Mean peak oxygen concentrations were: (1) 30:2 CPR with BVM 49.3% (SD = 2.6%); (2) NRFM 47.7% (SD = 0.2%); (3) PODD with 10 LPM oxygen 52.3% (SD = 0.4%); (4) PODD with 15 LPM oxygen 62.7% (SD = 0.3%); and (5) control 21% (SD = 0%). Oxygen concentrations rose rapidly and remained steady with passive oxygenation, unlike 30:2 CPR with BVM, which rose after each ventilation and decreased until the next ventilation cycle (sawtooth pattern, mean concentration 40% [SD = 3%]).

Continuous compressions and passive oxygenation with the PODD resulted in higher lung oxygen concentrations than NRFM and BVM while minimizing CPR interruptions in a manikin model.

Major depressive disorder (MDD) is the leading cause of disability globally, with moderate heritability and well-established socio-environmental risk factors. Genetic studies have been mostly restricted to European settings, with polygenic scores (PGS) demonstrating low portability across diverse global populations.

This study examines genetic architecture, polygenic prediction, and socio-environmental correlates of MDD in a family-based sample of 10 032 individuals from Nepal with array genotyping data. We used genome-based restricted maximum likelihood to estimate heritability, applied S-LDXR to estimate the cross-ancestry genetic correlation between Nepalese and European samples, and modeled PGS trained on a GWAS meta-analysis of European and East Asian ancestry samples.

We estimated the narrow-sense heritability of lifetime MDD in Nepal to be 0.26 (95% CI 0.18–0.34, p = 8.5 × 10−6). Our analysis was underpowered to estimate the cross-ancestry genetic correlation (rg = 0.26, 95% CI −0.29 to 0.81). MDD risk was associated with higher age (beta = 0.071, 95% CI 0.06–0.08), female sex (beta = 0.160, 95% CI 0.15–0.17), and childhood exposure to potentially traumatic events (beta = 0.050, 95% CI 0.03–0.07), while neither the depression PGS (beta = 0.004, 95% CI −0.004 to 0.01) or its interaction with childhood trauma (beta = 0.007, 95% CI −0.01 to 0.03) were strongly associated with MDD.

Estimates of lifetime MDD heritability in this Nepalese sample were similar to previous European ancestry samples, but PGS trained on European data did not predict MDD in this sample. This may be due to differences in ancestry-linked causal variants, differences in depression phenotyping between the training and target data, or setting-specific environmental factors that modulate genetic effects. Additional research among under-represented global populations will ensure equitable translation of genomic findings.

We sought to validate available tools for predicting recurrent C. difficile infection (CDI) including recurrence risk scores (by Larrainzar-Coghen, Reveles, D’Agostino, Cobo, and Eyre et al) alongside consensus guidelines risk criteria, the leading severity score (ATLAS), and PCR cycle threshold (as marker of fecal organism burden) using electronic medical records.

Retrospective cohort study validating previously described tools.

Tertiary care academic hospital.

Hospitalized adult patients with CDI at University of Virginia Medical Center.

Risk scores were calculated within ±48 hours of index CDI diagnosis using a large retrospective cohort of 1,519 inpatient infections spanning 7 years and compared using area under the receiver operating characteristic curve (AUROC) and the DeLong test. Recurrent CDI events (defined as a repeat positive test or symptom relapse within 60 days requiring retreatment) were confirmed by clinician chart review.

Reveles et al tool achieved the highest AUROC of 0.523 (and 0.537 among a subcohort of 1,230 patients with their first occurrence of CDI), which was not substantially better than other tools including the current IDSA/SHEA C. difficile guidelines or PCR cycle threshold (AUROC: 0.564), regardless of prior infection history.

All tools performed poorly for predicting recurrent C. difficile infection (AUROC range: 0.488–0.564), especially among patients with a prior history of infection (AUROC range: 0.436–0.591). Future studies may benefit from considering novel biomarkers and/or higher-dimensional models that could augment or replace existing tools that underperform.

Profiling patients on a proposed ‘immunometabolic depression’ (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 0.06, P = 0.049, 95% CI 0.0001–0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, βpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01–0.22, I2 = 23.91%), with a higher – but still small – effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Definitive diagnosis of Alzheimer’s disease (AD) is often unavailable, so clinical diagnoses with some degree of inaccuracy are often used in research instead. When researchers test methods that may improve clinical accuracy, the error in initial diagnosis can penalize predictions that are more accurate to true diagnoses but differ from clinical diagnoses. To address this challenge, the current study investigated the use of a simple bias adjustment for use in logistic regression that accounts for known inaccuracy in initial diagnoses.

A Bayesian logistic regression model was developed to predict unobserved/true diagnostic status given the sensitivity and specificity of an imperfect reference. This model considers cases as a mixture of true (with rate = sensitivity) and false positives (rate = 1 - specificity) while controls are mixtures of true (rate = specificity) and false negatives (rate = 1 - sensitivity). This bias adjustment was tested using Monte Carlo simulations over four conditions that varied the accuracy of clinical diagnoses. Conditions utilized 1000 iterations each generating a random dataset of n = 1000 based on a true logistic model with an intercept and three arbitrary predictors. Coefficients for parameters were randomly selected in each iteration and used to produce a set of two diagnoses: true diagnoses and observed diagnoses with imperfect accuracy. Sensitivity and specificity of the simulated clinical diagnosis varied with each of the four conditions (C): C1 = (0.77, 0.60), C2 = (0.87, 0.44), C3 = (0.71, 0.71), and C4 = (0.83, 0.55), which are derived from published values for clinical AD diagnoses against autopsy-confirmed pathology. Unadjusted and bias-adjusted logistic regressions were then fit to the simulated data to determine the models’ accuracy in estimating regression parameters and prediction of true diagnosis.

Under all conditions, the bias-adjusted logistic regression model outperformed its unadjusted counterpart. Root mean square error (the variability of estimated coefficients around their true parameter values) ranged from 0.23 to 0.79 for the unadjusted model versus 0.24 to 0.29 for the bias-adjusted model. The empirical coverage rate (the proportion of 95% credible intervals that include their true parameter) ranged from 0.00 to 0.47 for the unadjusted model versus 0.95 to 0.96 for the bias-adjusted model. Finally, the bias-adjusted model produced the best overall diagnostic accuracy with correct classification of true diagnostic values about 78% of the time versus 62-72% without adjustment.

Results of this simulation study, which used published AD sensitivity and specificity statistics, provide evidence that bias-adjustments to logistic regression models are needed when research involves diagnoses from an imperfect standard. Results showed that unadjusted methods rarely identified true effects with credible intervals for coefficients including the true value anywhere from never to less than half of the time. Additional simulations are needed to examine the bias-adjusted model’s performance under additional conditions. Future research is needed to extend the bias adjustment to multinomial logistic regressions and to scenarios where the rate of misdiagnosis is unknown. Such methods may be valuable for improving detection of other neurological disorders with greater diagnostic error as well.

Learning curve patterns on list-learning tasks can help clinicians determine the nature of memory difficulties, as an “impaired” score may actually reflect attention and/or executive difficulties rather than a true memory impairment. Though such pattern analysis is often qualitative, there are quantitative methods to assess these concepts that have been generally underutilized. This study aimed to develop a model that decomposes learning over repeated trials into separate cognitive processes and then include other testing data to predict performance at each trial as a function of general cognitive functioning.

Data for CVLT-II learning trials were obtained from an outpatient neuropsychology service within an academic medical center referred for clinical reasons. Participants with a cognitive diagnosis of non-demented (ND) or probable Alzheimer’s disease (AD) were included. The final sample consisted of 323 ND [Mage = 58.6 (14.8); Medu = 15.4 (2.7); 55.7% female] and 915 AD [Mage = 72.6 (9.0); Medu = 14.2 (3.1); 60.1% female cases. A Bayesian non-linear beta-binomial multilevel model was used, which uses three parameters to predict CVLT-II recall-by-trial: verbal attention span (VAS), maximal learning potential (MLP), and learning rate (LR). Briefly, VAS predicts expected first trial performance while MLP, conversely, predicts the expected best performance as trials are repeated, and LR weights the influence of VAS versus MLR over repeated trials. Predictors of these parameters included age, education, sex, race, and clinical diagnosis, in addition to raw scores on Trail Making Test Parts A and B, phonemic (FAS) fluency, animal fluency, Boston Naming Test, Wisconsin Card Sorting Test (WCST) Categories Completed, and then age-adjusted scaled scores from WAIS-IV Digit Span, Block Design, Vocabulary, and Coding. Random intercepts were included for each parameter and extracted for comparison of residual differences by diagnosis.

The model explained 84% of the variance in CVLT-II raw scores. VAS reduced with age and time-to-complete Trails B but improved with both verbal fluencies and confrontation naming. MLP increased as a function of WAIS Digit Span, animal fluency, confrontation naming, and WCST categories completed. Finally, LR was greater for females and WAIS-IV Coding and Vocabulary performances but reduced with age. Participants with AD had lower estimates of all three parameters: Cohen’s d = 2.49 (VAS) - 3.48 (LR), though including demographic and neuropsychological tests attenuated differences, Cohen’s d = 0.34 (LR) - 0.95 (MLP).

The resulting model highlights how non-memory neuropsychological deficits affect list-learning test performance. At the same time, the model demonstrated that memory patterns on the CVLT-II can still be identified beyond other confounding deficits since having AD affected all parameters independent of other cognitive impairments. The modeling approach can generate conditional learning curves for individual patient data, and when multiple diagnoses are included in the model, a person-fit statistic can be computed to return the mostly likely diagnosis for an individual. The model can also be used in research to quantify or adjust for the effect of other patient data (e.g., neuroimaging, biomarkers, medications).

Children and young people with CHD benefit from regular physical activity. Parents are reported as facilitators and barriers to their children’s physical activity. The aim of this study was to explore parental factors, child factors, and their clinical experience on physical activity participation in young people with CHD.

An online questionnaire was co-developed with parents (n = 3) who have children with CHD. The survey was then distributed in the United Kingdom by social media and CHD networks, between October 2021 and February 2022. Data were analysed using mixed methods.

Eighty-three parents/guardians responded (94% mothers). Young people with CHD were 7.3 ± 5.0 years old (range 0–20 years; 53% female) and 84% performed activity. Parental participation in activity (X2(1) = 6.9, P < 0.05) and perceiving activity as important for their child were positively associated with activity (Fisher’s Exact, P < 0.05). Some parents (∼15%) were unsure of the safety of activity, and most (∼70%) were unsure where to access further information about activity. Fifty-two parents (72%) had never received activity advice in clinic, and of the 20 who received advice, 10 said it was inconsistent. Qualitative analysis produced the theme “Knowledge is power and comfort.” Parents described not knowing what activity was appropriate or the impact of it on their child.

Parental participation and attitudes towards activity potentially influence their child’s activity. A large proportion of young people performed activity despite a lack and inconsistency of activity advice offered by CHD clinics. Young people with CHD would benefit from activity advice with their families in clinics.