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Anhedonia – a diminished interest or pleasure in activities – is a core self-reported symptom of depression which is poorly understood and often resistant to conventional antidepressants. This symptom may occur due to dysfunction in one or more sub-components of reward processing: motivation, consummatory experience and/or learning. However, the precise impairments remain elusive. Dissociating these components (ideally, using cross-species measures) and relating them to the subjective experience of anhedonia is critical as it may benefit fundamental biology research and novel drug development.
Using a battery of behavioural tasks based on rodent assays, we examined reward motivation (Joystick-Operated Runway Task, JORT; and Effort-Expenditure for Rewards Task, EEfRT) and reward sensitivity (Sweet Taste Test) in a non-clinical population who scored high (N = 32) or low (N = 34) on an anhedonia questionnaire (Snaith–Hamilton Pleasure Scale).
Compared to the low anhedonia group, the high anhedonia group displayed marginal impairments in effort-based decision-making (EEfRT) and reduced reward sensitivity (Sweet Taste Test). However, we found no evidence of a difference between groups in physical effort exerted for reward (JORT). Interestingly, whilst the EEfRT and Sweet Taste Test correlated with anhedonia measures, they did not correlate with each other. This poses the question of whether there are subgroups within anhedonia; however, further work is required to directly test this hypothesis.
Our findings suggest that anhedonia is a heterogeneous symptom associated with impairments in reward sensitivity and effort-based decision-making.
In March 2020, New York City (NYC) became the epicenter of the COVID-19 pandemic in the United States (US). As healthcare facilities were overwhelmed with patients, the Jacob K. Javits Convention Center was transformed into the nation’s largest alternate care site (ACS): Javits New York Medical Station (Javits). Protecting healthcare workers during a global shortage of personal protective equipment (PPE) in a non-traditional healthcare setting posed unique challenges. We describe components of the healthcare worker safety program implemented at Javits.
Javits, a large convention center transformed into a field hospital, with clinical staff from the US Public Health Service Commissioned Corps (USPHS) and the Department of Defense (DoD).
Healthcare Worker Safety Methods:
Key strategies included ensuring one-way flow of traffic on and off the patient floor; developing a matrix detailing PPE required for each work activity and location; PPE extended use and reuse protocols; personnel training; and monitoring adherence to PPE donning/doffing protocols when entering or exiting the patient floor. Javits staff who reported COVID-19 symptoms were immediately isolated, monitored, and offered a SARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) test.
A well-designed and implemented healthcare worker safety plan can minimize the risk of SARS-CoV-2 infection for healthcare workers. The lessons learned from operating the nation’s largest COVID-19 ACS can be adapted to other environments during public health emergencies.
A variety of information sources are used in the best-evidence diagnostic procedure in child and adolescent mental healthcare, including evaluation by referrers and structured assessment questionnaires for parents. However, the incremental value of these information sources is still poorly examined.
To quantify the added and unique predictive value of referral letters, screening, multi-informant assessment and clinicians’ remote evaluations in predicting mental health disorders.
Routine medical record data on 1259 referred children and adolescents were retrospectively extracted. Their referral letters, responses to the Strengths and Difficulties Questionnaire (SDQ), results on closed-ended questions from the Development and Well-Being Assessment (DAWBA) and its clinician-rated version were linked to classifications made after face-to-face intake in psychiatry. Following multiple imputations of missing data, logistic regression analyses were performed with the above four nodes of assessment as predictors and the five childhood disorders common in mental healthcare (anxiety, depression, autism spectrum disorders, attention-deficit hyperactivity disorder, behavioural disorders) as outcomes. Likelihood ratio tests and diagnostic odds ratios were computed.
Each assessment tool significantly predicted the classified outcome. Successive addition of the assessment instruments improved the prediction models, with the exception of behavioural disorder prediction by the clinician-rated DAWBA. With the exception of the SDQ for depressive and behavioural disorders, all instruments showed unique predictive value.
Structured acquisition and integrated use of diverse sources of information supports evidence-based diagnosis in clinical practice. The clinical value of structured assessment at the primary–secondary care interface should now be quantified in prospective studies.
There is currently a heightened need for transparency in pharmaceutical sectors. The inclusion of real-world (RW) evidence, in addition to clinical trial evidence, in decision-making processes, was an important step forward toward a more inclusive established value proposition. This advance has introduced new transparency challenges. Increasing transparency is a critical step toward accelerating improvement in type, quality, and access to data, regardless of whether these originate from clinical trials or from RW studies. However, so far, advances in transparency have been relatively restricted to clinical trials, and there remains a lack of similar expectations or standards of transparency concerning the generation and reporting of RW data. This perspective paper aims to highlight the need for transparency concerning RW studies, data, and evidence across health care sectors, to identify areas for improvement, and provide concrete recommendations and practices for the future. Specific issues are discussed from different stakeholder perspectives, culminating in recommended actions, from individual stakeholder perspectives, for improved RW study, data, and evidence transparency. Furthermore, a list of potential guidelines for consideration by stakeholders is proposed. While recommendations from different stakeholder perspectives are made, true transparency in the processes involved in the generation, reporting, and use of RW evidence will require a concerted effort from all stakeholders across health care sectors.
There is evidence that the COVID-19 pandemic has negatively affected mental health, but most studies have been conducted in the general population.
To identify factors associated with mental health during the COVID-19 pandemic in individuals with pre-existing mental illness.
Participants (N = 2869, 78% women, ages 18–94 years) from a UK cohort (the National Centre for Mental Health) with a history of mental illness completed a cross-sectional online survey in June to August 2020. Mental health assessments were the GAD-7 (anxiety), PHQ-9 (depression) and WHO-5 (well-being) questionnaires, and a self-report question on whether their mental health had changed during the pandemic. Regressions examined associations between mental health outcomes and hypothesised risk factors. Secondary analyses examined associations between specific mental health diagnoses and mental health.
A total of 60% of participants reported that mental health had worsened during the pandemic. Younger age, difficulty accessing mental health services, low income, income affected by COVID-19, worry about COVID-19, reduced sleep and increased alcohol/drug use were associated with increased depression and anxiety symptoms and reduced well-being. Feeling socially supported by friends/family/services was associated with better mental health and well-being. Participants with a history of anxiety, depression, post-traumatic stress disorder or eating disorder were more likely to report that mental health had worsened during the pandemic than individuals without a history of these diagnoses.
We identified factors associated with worse mental health during the COVID-19 pandemic in individuals with pre-existing mental illness, in addition to specific groups potentially at elevated risk of poor mental health during the pandemic.
Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.
To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.
This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.
The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.
Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.
The Pediatric Acute Care Cardiology Collaborative (PAC3) was established to improve acute care cardiology outcomes through the development of an accurate and well-validated clinical registry. We report the validation results of the initial PAC3 registry audits and describe a novel regional audit format developed to accommodate a rapidly expanding membership facilitate collaborative learning and allow for necessary modification due to the COVID-19 pandemic.
Materials and methods:
Six hospitals were audited using a regional audit format and three hospitals were subsequently audited virtually. Critical and challenging-to-collect data elements were audited among at least 40 randomly selected cases. Discrepancies were categorised as either major or minor depending on their relative importance to patient outcomes and clinical care. Results were tabulated and reported.
We audited 386 encounters and 27,086 individual data fields across 9 hospitals. The aggregate overall accuracy rate was 99.27% and the aggregate major discrepancy rate was 0.51%. The overall accuracy rate ranged from 98.77% to 99.59%, and the major discrepancy rate ranged from 0.26% to 0.88% across the cohort. No appreciable difference was seen between audit formats. Both the regional and virtual audit methods were viewed favourably by participants.
A low data discrepancy rate was found demonstrating that the PAC3 registry is a highly accurate data source for use in quality improvement, benchmarking, and research. Regional audits and virtual audits were both successfully implemented.
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.
Caregivers of patients with cancer are at significant risk for existential distress. Such distress negatively impacts caregivers’ quality of life and capacity to serve in their role as healthcare proxies, and ultimately, contributes to poor bereavement outcomes. Our team developed Meaning-Centered Psychotherapy for Cancer Caregivers (MCP-C), the first targeted psychosocial intervention that directly addresses existential distress in caregivers.
Nine caregivers of patients with glioblastoma multiforme (GBM) enrolled in a pilot randomized controlled trial evaluating the feasibility, acceptability, and effects of MCP-C, and completed in-depth interviews about their experience in the therapy. One focus group with three MCP-C interventionists was also completed.
Four key themes emerged from interviews: (1) MCP-C validated caregivers’ experience of caregiving; (2) MCP-C helped participants reframe their “caregiving identity” as a facet of their larger self-identity, by placing caregiving in the context of their life's journey; (3) MCP-C enabled caregivers to find ways to assert their agency through caregiving; and (4) the structure and sequence of sessions made MCP-C accessible and feasible. Feedback from interventionists highlighted several potential manual changes and overall ways in which MCP-C can help facilitate caregivers’ openness to discussing death and engaging in advanced care planning discussions with the patient.
Significance of results
The overarching goal of MCP-C is to allow caregivers to concurrently experience meaning and suffering; the intervention does not seek to deny the reality of challenges endured by caregivers, but instead to foster a connection to meaning and purpose alongside their suffering. Through in-depth interviews with caregivers and a focus group with MCP interventionists, we have refined and improved our MCP-C manual so that it can most effectively assist caregivers in experiencing meaning and purpose, despite inevitable suffering.
The last 50 years have seen an increasing dependence on academic institutions to develop and commercialize new biomedical innovations, a responsibility for which many universities are ill-equipped. To address this need, we created LEAP, an asset development and gap fund program at Washington University in St. Louis (WUSTL). Beyond awarding funds to promising projects, this program aimed to promote a culture of academic entrepreneurship, and thus improve WUSTL technology transfer, by providing university inventors with individualized consulting and industry expert feedback. The purpose of this work is to document the structure of the LEAP program and evaluate its impact on the WUSTL entrepreneurial ecosystem. Our analysis utilizes program data, participant surveys, and WUSTL technology transfer office records to demonstrate that LEAP consistently attracted new investigators and that the training provided by the program was both impactful and highly valued by participants. We also show that an increase in annual WUSTL start-up formation during the years after LEAP was established and implicate the program in this increase. Taken together, our results illustrate that programs like LEAP could serve as a model for other institutions that seek to support academic entrepreneurship initiatives.
Ambulance patients who are unable to be quickly transferred to an emergency department (ED) bed represent a key contributing factor to ambulance offload delay (AOD). Emergency department crowding and associated AOD are exacerbated by multiple factors, including infectious disease outbreaks such as the coronavirus disease 2019 (COVID-19) pandemic. Initiatives to address AOD present an opportunity to streamline ambulance offload procedures while improving patient outcomes.
The goal of this study was to evaluate the initial outcomes and impact of a novel Emergency Medical Service (EMS)-based Hospital Liaison Program (HLP) on ambulance offload times (AOTs).
Ambulance offload times associated with EMS patients transported to a community hospital six months before and after HLP implementation were retrospectively analyzed using proportional significance tests, t-tests, and multiple regression analysis.
A proportional increase in incidents in the zero to <30 minutes time category after program implementation (+2.96%; P <.01) and a commensurate decrease in the proportion of incidents in the 30 to <60 minutes category (−2.65%; P <.01) were seen. The fully adjusted regression model showed AOT was 16.31% lower (P <.001) after HLP program implementation, holding all other variables constant.
The HLP is an innovative initiative that constitutes a novel pathway for EMS and hospital systems to synergistically enhance ambulance offload procedures. The greatest effect was demonstrated in patients exhibiting potentially life-threatening symptoms, with a reduction of approximately three minutes. While small, this outcome was a statistically significant decrease from the pre-intervention period. Ultimately, the HLP represents an additional strategy to complement existing approaches to mitigate AOD.
Reliable spatially resolved compositional analysis through atom probe tomography requires an accurate placement of the detected ions within the three-dimensional reconstruction. Unfortunately, for heterogeneous systems, traditional reconstruction protocols are prone to position some ions incorrectly. This stems from the use of simplified projection laws which treat the emitter apex as a spherical cap, although the actual shape may be far more complex. For instance, sampled materials with compositional heterogeneities are known to develop local variations in curvature across the emitter due to their material phase specific evaporation fields. This work provides three pivotal precursors to improve the spatial accuracy of the reconstructed volume in such cases. First, we show scanning probe microscopy enables the determination of the local curvature of heterogeneous emitters, thus providing the essential information for a more advanced reconstruction considering the actual shape. Second, we demonstrate the cyclability between scanning probe characterization and atom probe analysis. This is a key ingredient of more advanced reconstruction protocols whereby the characterization of the emitter topography is executed at multiple stages of the atom probe analysis. Third, we show advances in the development of an electrostatically driven reconstruction protocol which are expected to enable reconstruction based on experimental tip shapes.
Background: Phase 3 COMET trial (NCT02782741) compares avalglucosidase alfa (n=51) with alglucosidase alfa (n=49) in treatment-naïve LOPD. Methods: Primary objective: determine avalglucosidase alfa effect on respiratory muscle function. Secondary/other objectives include: avalglucosidase alfa effect on functional endurance, inspiratory/expiratory muscle strength, lower/upper extremity muscle strength, motor function, health-related quality of life, safety. Results: At Week 49, change (LSmean±SE) from baseline in upright forced vital capacity %predicted was greater with avalglucosidase alfa (2.89%±0.88%) versus alglucosidase alfa (0.46%±0.93%)(absolute difference+2.43%). The primary objective, achieving statistical non-inferiority (p=0.0074), was met. Superiority testing was borderline significant (p=0.0626). Week 49 change from baseline in 6-minute walk test was 30.01-meters greater for avalglucosidase alfa (32.21±9.93m) versus alglucosidase alfa (2.19±10.40m). Positive results for avalglucosidase alfa were seen for all secondary/other efficacy endpoints. Treatment-emergent adverse events (AEs) occurred in 86.3% of avalglucosidase alfa-treated and 91.8% of alglucosidase alfa-treated participants. Five participants withdrew, 4 for AEs, all on alglucosidase alfa. Serious AEs occurred in 8 avalglucosidase alfa-treated and 12 alglucosidase alfa-treated participants. IgG antidrug antibody responses were similar in both. High titers and neutralizing antibodies were more common for alglucosidase alfa. Conclusions: Results demonstrate improvements in clinically meaningful outcome measures and a more favorable safety profile with avalglucosidase alfa versus alglucosidase alfa. Funding: Sanofi Genzyme
Background: Sample entropy (SampEn) can quantify the unpredictability of a physiological signal. We sought to assess if SampEn on EEG could reflect recent seizure activity.
Methods: Charts of all patients undergoing an outpatient EEG between January and March 2018 were reviewed to assess seizure occurrences in the follow-up period between the two clinical visits surrounding the EEG. 9s-EEG segments were extracted at pre-specified time points. SampEn was calculated for all segments and values aggregated at the 25thpercentile. We performed a multivariate zero-inflated analysis to test the association between SampEn and seizure rate around the EEG, after controlling for age, presence of IED, presence of abnormal slowing, and presence of a focal brain lesion. Results: 269 EEGs were screened and 133 met inclusion criteria (112 patients). 80 EEGs (60%) were from patients with epilepsy, of which 47 had at least one seizure within the year preceding the EEG. Remaining EEGs were from patients who were deemed not to have epilepsy at last follow-up. Each 1SD decrease in SampEn was associated with a 3.93-fold increase in the rate of daily seizures (95% CI: 1.19–12.99, p = 0.02). Conclusions: Sample entropy of EEG is a potential objective method to assess contemporary seizure occurrence.
Impaired olfaction may be a biomarker for early Lewy body disease, but its value in mild cognitive impairment with Lewy bodies (MCI-LB) is unknown. We compared olfaction in MCI-LB with MCI due to Alzheimer’s disease (MCI-AD) and healthy older adults. We hypothesized that olfactory function would be worse in probable MCI-LB than in both MCI-AD and healthy comparison subjects (HC).
Cross-sectional study assessing olfaction using Sniffin’ Sticks 16 (SS-16) in MCI-LB, MCI-AD, and HC with longitudinal follow-up. Differences were adjusted for age, and receiver operating characteristic (ROC) curves were used for discriminating MCI-LB from MCI-AD and HC.
Participants were recruited from Memory Services in the North East of England.
Thirty-eight probable MCI-LB, 33 MCI-AD, 19 possible MCI-LB, and 32HC.
Olfaction was assessed using SS-16 and a questionnaire.
Participants with probable MCI-LB had worse olfaction than both MCI-AD (age-adjusted mean difference (B) = 2.05, 95% CI: 0.62–3.49, p = 0.005) and HC (B = 3.96, 95% CI: 2.51–5.40, p < 0.001). The previously identified cutoff score for the SS-16 of ≤ 10 had 84% sensitivity for probable MCI-LB (95% CI: 69–94%), but 30% specificity versus MCI-AD. ROC analysis found a lower cutoff of ≤ 7 was better (63% sensitivity for MCI-LB, with 73% specificity vs MCI-AD and 97% vs HC). Asking about olfactory impairments was not useful in identifying them.
MCI-LB had worse olfaction than MCI-AD and normal aging. A lower cutoff score of ≤ 7 is required when using SS-16 in such patients. Olfactory testing may have value in identifying early LB disease in memory services.
This study assessed the extent to which women's preconception binge drinking, tobacco use and cannabis use, reported prospectively in adolescence and young adulthood, predicted use of these substances during pregnancy and at 1 year postpartum.
Data were pooled from two intergenerational cohort studies: the Australian Temperament Project Generation 3 Study (395 mothers, 691 pregnancies) and the Victorian Intergenerational Health Cohort Study (398 mothers, 609 pregnancies). Alcohol, tobacco and cannabis use were assessed in adolescence (13–18 years), young adulthood (19–29 years) and at ages 29–35 years for those transitioning to parenthood. Exposures were weekly or more frequent preconception binge drinking (5 + drinks in one session), tobacco use and cannabis use. Outcomes were any alcohol, tobacco and cannabis use prior to awareness of the pregnancy, after awareness of pregnancy (up to and including the third trimester pregnancy) and at 1 year postpartum.
Frequent preconception binge drinking, tobacco use and cannabis use across both adolescence and young adulthood were strong predictors of continued use post-conception, before and after awareness of the pregnancy and at 1 year postpartum. Substance use limited to young adulthood also predicted continued use post-conception.
Persistent alcohol, tobacco use and cannabis use that starts in adolescence has a strong continuity into parenthood. Reducing substance use in the perinatal period requires action well before pregnancy, commencing in adolescence and continuing into the years before conception and throughout the perinatal period.
The Middle Mississippian component at Aztalan was a mixed, Late Woodland / Mississippian occupation sited within a heavily fortified habitation and mound center that is located on a tributary of the Rock River in Wisconsin. It represents the northernmost large Cahokian-related village recorded. The Oneota Lake Koshkonong Locality of the Rock River drainage is located approximately 20 km south of Aztalan, and it consists of a 25 km2 area along the northwest shore with a small cluster of habitation settlements. Sixty-eight radiocarbon measurements have been obtained from Aztalan, and 52 from Oneota settlements in the Lake Koshkonong Locality. We discuss how to best interpret this dataset, and we use Bayesian chronological modeling to analyze these dates. The results suggest that (1) Aztalan's Late Woodland (Kekoskee phase) occupation began in the AD 900s or early AD 1000s, (2) Aztalan's Mississippian occupation ceased in the AD 1200s, (3) Oneota occupations at Lake Koshkonong began after AD 1050 and were established by the AD 1200s, and (4) Oneota occupations at Lake Koshkonong continued after Aztalan's Mississippian abandonment until at least the late AD 1300s. Additionally, the results demonstrate that Aztalan was fortified with a palisade with bastions for much of the Mississippian occupation, suggesting a contested presence in a multiethnic landscape.