Sleep, Health Outcomes and Body Weight (SHOW) study: a measurement burst design study on sleep and risk factors for obesity in black emerging adults in North Carolina, USA.

INTRODUCTION: Black emerging adults (18-28 years) have the highest risk of short sleep duration and obesity. This increased risk may be partly explained by greater stress levels, which may result from race-related stress (racial discrimination and heightened race-related vigilance) or living in more disadvantaged home and neighbourhood environments. Insufficient sleep may also impact obesity risk via several weight-related mechanisms including energy balance, appetite and food reward, cortisol profiles and hydration status. This paper describes the rationale, design and methods for the Sleep, Health Outcomes and Body Weight (SHOW) study. This study aims to prospectively assess the effects of sleep, race-related stress and home/neighbourhood environments on weight-related mechanisms and obesity markers (body weight, waist circumference and fat mass) in 150 black emerging adults. METHODS AND ANALYSIS: The SHOW study follows a measurement burst design that includes 3, 7-day data collection bursts (baseline, 6-month and 12-month follow-ups). Sleep is measured with three methods: sleep diary, actigraphy and polysomnography. Energy balance over 7 days is based on resting and postprandial energy expenditure measured via indirect calorimetry, physical activity via accelerometry and self-reported and ad libitum energy intake methods. Self-reported methods and blood biomarkers assess fasting and postprandial appetite profiles and a behavioural-choice task measures food reward. Cortisol awakening response and diurnal cortisol profiles over 3 days are assessed via saliva samples and chronic cortisol exposure via a hair sample. Hydration markers are assessed with 24-hour urine collection over 3 days and fasting blood biomarkers. Race-related stress is self-reported over 7 days. Home and neighbourhood environments (via the Windshield Survey) is observer assessed. ETHICS AND DISSEMINATION: Ethics approval was granted by the University of North Carolina at Greensboro's Institutional Review Board. Study findings will be disseminated through peer-reviewed publications, presentations at scientific meetings and reports, briefs/infographics for lay and community audiences.

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians.

Importance: While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. Objective: To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. Design, Setting, and Participants: This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Main Outcomes and Measures: Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Results: Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Conclusions and Relevance: Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification.

The effectiveness of the lorazepam challenge test in pediatric catatonia: A multisite retrospective cohort study.

OBJECTIVE: Catatonia is a neuropsychiatric disorder associated with changes in behavior and affect. In adults, catatonia can respond rapidly to treatment with benzodiazepines as part of the "lorazepam challenge test." The acute effectiveness of benzodiazepine treatment in pediatric catatonia, however, has received less study. This study reports catatonia severity as measured by the Bush Francis Catatonia Rating Scale (BFCRS) in pediatric patients before and after treatment with lorazepam. METHODS: Multicenter retrospective cohort study from 1/1/2018 to 6/1/2023 of patients aged 18 and younger with a clinical diagnosis of catatonia and assessment using the BFCRS before and after treatment with lorazepam. RESULTS: Among 54 patients, median age was 16, and 26 (48.1 %) were female. Neurodevelopmental disabilities were present in 24 (44.4 %) of patients. Prior to treatment, patients had a mean BFCRS score of 16.6 ± 6.1, which significantly reduced to 9.5 ± 5.3 following treatment with lorazepam (mean paired difference 7.1; t = 9.0, df = 53, p < 0.001), representing a large effect size (Hedges's g = 1.20; 95 % CI: 0.85 to 1.55). No significant association was found between lorazepam dose or route of administration and clinical response, nor were age, sex, study site, the presence of a neurodevelopmental disorder, the presence of hyperactive catatonic features, or the time between treatment and reassessment associated with post-treatment BFCRS. CONCLUSIONS: Lorazepam resulted in a rapid improvement in BFCRS score in pediatric patients, with a large effect size. Further research is needed into optimal dosing and route of administration of the lorazepam challenge test in pediatric patients.

Stratifying Risk for Postpartum Depression at Time of Hospital Discharge.

Objective: Postpartum depression (PPD) represents a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. Thus, we aimed to develop and estimate the performance of a generalizable risk stratification model for PPD in patients without a history of depression using information collected as part of routine clinical care. Methods: We performed a retrospective cohort study of all individuals who delivered between 2017 and 2022 in one of two large academic medical centers and six community hospitals. An elastic net model was constructed and externally validated to predict PPD using sociodemographic factors, medical history, and prenatal depression screening information, all of which was known before discharge from the delivery hospitalization. Results: The cohort included 29,168 individuals; 2,703 (9.3%) met at least one criterion for postpartum depression in the 6 months following delivery. In the external validation data, the model had good discrimination and remained well-calibrated: area under the receiver operating characteristic curve 0.721 (95% CI: 0.707-0.734), Brier calibration score 0.088 (95% CI: 0.084 - 0.092). At a specificity of 90%, the positive predictive value was 28.0% (95% CI: 26.0-30.1%), and the negative predictive value was 92.2% (95% CI: 91.8-92.7%). Conclusions: These findings demonstrate that a simple machine-learning model can be used to stratify the risk for PPD before delivery hospitalization discharge. This tool could help identify patients within a practice at the highest risk and facilitate individualized postpartum care planning regarding the prevention of, screening for, and management of PPD at the start of the postpartum period and potentially the onset of symptoms.

Dimensional measures of psychopathology in children and adolescents using large language models.

BACKGROUND: To enable greater use of NIMH Research Domain Criteria (RDoC) in real-world settings, we applied large language models to estimate dimensional psychopathology from narrative clinical notes. METHODS: We conducted a cohort study using health records from individuals age 18 years or younger evaluated in the psychiatric emergency department of a large academic medical center between November 2008 and March 2015. Outcomes were hospital admission and length of emergency department stay. RDoC domains were estimated using a HIPAA-compliant large language model (gpt-4-1106-preview), and compared to a previously-validated token-based approach. RESULTS: The cohort included 3,059 individuals (median age 16 (25%-75% 13-18); 1580 (52%) female, 1479 (48%) male; 105 (3.4%) identified as Asian, 329 (11%) as Black, 288 (9.4%) Hispanic, 474 (15%) as another race, and 1863 (61%) as white), of whom 1695 (55%) were admitted. Correlation between LLM-extracted RDoC scores and the token-based scores ranged from small to medium by Kendall's Tau (0.14-0.22). In logistic regression models adjusted for sociodemographic and clinical features, admission likelihood was associated with greater scores on all domains, with the exception of sensorimotor, which was inversely associated (p<.001 for all adjusted associations). Tests for bias suggested modest but statistically significant differences in positive valence scores by race (p<.05 for Asian, Hispanic, and Black individuals). CONCLUSION: A large language model extracted estimates of 6 RDoC domains in an explainable manner, which were associated with clinical outcomes. This approach can contribute to a new generation of prediction models or biological investigations based on dimensional psychopathology.

Genome-Wide Association Study of Treatment-Resistant Depression: Shared Biology With Metabolic Traits.

OBJECTIVE: Treatment-resistant depression (TRD) occurs in roughly one-third of all individuals with major depressive disorder (MDD). Although research has suggested a significant common variant genetic component of liability to TRD, with heritability estimated at 8% when compared with non-treatment-resistant MDD, no replicated genetic loci have been identified, and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. The objective of this study was to perform a well-powered genetic study of TRD. METHODS: Using receipt of electroconvulsive therapy (ECT) as a surrogate for TRD, the authors applied standard machine learning methods to electronic health record data to derive predicted probabilities of receiving ECT. These probabilities were then applied as a quantitative trait in a genome-wide association study of 154,433 genotyped patients across four large biobanks. RESULTS: Heritability estimates ranged from 2% to 4.2%, and significant genetic overlap was observed with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits, and body mass index. Two genome-wide significant loci were identified, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. CONCLUSIONS: This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

Self-Care of Diabetes and Diabetes Distress in Non-Hispanic Black Adults With Type 2 Diabetes.

PURPOSE: The purpose of the study was to describe the level of self-care of diabetes and diabetes distress and examine their relationship in non-Hispanic Black adults living with type 2 diabetes. METHODS: This cross-sectional, correlational study was conducted with 275 non-Hispanic Black adults with type 2 diabetes in North Carolina over 10 weeks, July 2022 to September 2022. An online survey collected sociodemographic and clinical characteristics, self-care measures of diabetes, and diabetes distress. The theory of self-care of chronic illness guided the study. Median regression examined the relationship between self-care of diabetes and diabetes distress. RESULTS: Of the 275 participants, over half reported a diagnosis of type 2 diabetes at younger ages than expected, with a mean age of 40. The self-reported mean A1C of 9% exceeded the recommended goal of <7%, with an average of 2 diabetes-related comorbidities. The self-care of diabetes scale scores were low, and total diabetes distress scores were high. Significant positive associations were found between total diabetes distress and self-care monitoring and self-care management. CONCLUSIONS: In this sample, non-Hispanic Black adults had low levels of self-care of diabetes and high levels of diabetes distress. The findings indicate a relationship between self-care of diabetes and diabetes distress; as diabetes distress increases, so do the attempted activities of self-care of diabetes. Health care professionals in diabetes care are uniquely positioned to address further the behavioral aspects of diabetes management, particularly diabetes distress, to support non-Hispanic Black adults living with type 2 diabetes.

Feasibility, Acceptability, and Preliminary Efficacy of a Smartphone App-Led Cognitive Behavioral Therapy for Depression Under Therapist Supervision: Open Trial.

BACKGROUND: Major depressive disorder affects approximately 1 in 5 adults during their lifetime and is the leading cause of disability worldwide. Yet, a minority receive adequate treatment due to person-level (eg, geographical distance to providers) and systems-level (eg, shortage of trained providers) barriers. Digital tools could improve this treatment gap by reducing the time and frequency of therapy sessions needed for effective treatment through the provision of flexible, automated support. OBJECTIVE: This study aimed to examine the feasibility, acceptability, and preliminary clinical effect of Mindset for Depression, a deployment-ready 8-week smartphone-based cognitive behavioral therapy (CBT) supported by brief teletherapy appointments with a therapist. METHODS: This 8-week, single-arm open trial tested the Mindset for Depression app when combined with 8 brief (16-25 minutes) video conferencing visits with a licensed doctoral-level CBT therapist (n=28 participants). The app offers flexible, accessible psychoeducation, CBT skills practice, and support to patients as well as clinician guidance to promote sustained engagement, monitor safety, and tailor treatment to individual patient needs. To increase accessibility and thus generalizability, all study procedures were conducted remotely. Feasibility and acceptability were assessed via attrition, patient expectations and feedback, and treatment utilization. The primary clinical outcome measure was the clinician-rated Hamilton Depression Rating Scale, administered at pretreatment, midpoint, and posttreatment. Secondary measures of functional impairment and quality of life as well as maintenance of gains (3-month follow-up) were also collected. RESULTS: Treatment credibility (week 4), expectancy (week 4), and satisfaction (week 8) were moderate to high, and attrition was low (n=2, 7%). Participants self-reported using the app or practicing (either on or off the app) the CBT skills taught in the app for a median of 50 (IQR 30-60; week 4) or 60 (IQR 30-90; week 8) minutes per week; participants accessed the app on an average 36.8 (SD 10.0) days and completed a median of 7 of 8 (IQR 6-8) steps by the week 8 assessment. The app was rated positively across domains of engagement, functionality, aesthetics, and information. Participants' depression severity scores decreased from an average Hamilton Depression Rating Scale score indicating moderate depression (mean 19.1, SD 5.0) at baseline to a week 8 mean score indicating mild depression (mean 10.8, SD 6.1; d=1.47; P<.001). Improvement was also observed for functional impairment and quality of life. Gains were maintained at 3-month follow-up. CONCLUSIONS: The results show that Mindset for Depression is a feasible and acceptable treatment option for individuals with major depressive disorder. This smartphone-led treatment holds promise to be an efficacious, scalable, and cost-effective treatment option. The next steps include testing Mindset for Depression in a fully powered randomized controlled trial and real-world clinical settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05386329; https://clinicaltrials.gov/study/NCT05386329?term=NCT05386329.

Identifying data-driven subtypes of major depressive disorder with electronic health records.

BACKGROUND: Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration. METHODS: We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups. RESULTS: Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LIMITATIONS: Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions. CONCLUSION: These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

Pediatric Physical Restraint Coding in US Hospitals: A 2019 Kids Inpatient Database Study.

BACKGROUND: Reduction of physical restraint utilization is a goal of high-quality hospital care, but there is little nationally-representative data about physical restraint utilization in hospitalized children in the United States. This study reports the rate of physical restraint coding among hospitalizations for patients aged 1 to 18 years old in the United States and explores associated demographic and diagnostic factors. METHODS: The Kids' Inpatient Database, an all-payors database of community hospital discharges in the United States, was queried for hospitalizations with a diagnosis of physical restraint status in 2019. Logistic regression using patient sociodemographic characteristics was used to characterize factors associated with physical restraint coding. RESULTS: A coded diagnosis of physical restraint status was present for 8893 (95% confidence interval [CI]: 8227-9560) hospitalizations among individuals aged 1 to 18 years old, or 0.63% of hospitalizations. Diagnoses associated with physical restraint varied by age, with mental health diagnoses overall the most frequent in an adjusted model, male sex (adjusted odds ratio [aOR] 1.56; 95% CI: 1.47-1.65), Black race (aOR 1.43; 95% CI: 1.33-1.55), a primary mental health or substance diagnosis (aOR 7.13; 95% CI: 6.42-7.90), Medicare or Medicaid insurance (aOR 1.33; 95% CI: 1.24-1.43), and more severe illness (aOR 2.83; 95% CI: 2.73-2.94) were associated with higher odds of a hospitalization involving a physical restraint code. CONCLUSIONS: Physical restraint coding varied by age, sex, race, region, and disease severity. These results highlight potential disparities in physical restraint utilization, which may have consequences for equity.

Understanding uptake of COVID-19 testing, vaccination, and boosters among Spanish-speaking Latines in the United States: Qualitative insights from Spanish speakers and key informants.

BACKGROUND: Latine communities in the United States have been disproportionately affected by COVID-19. It is critical to gain a better understanding of the sociocultural determinants that challenge and facilitate COVID-19 testing, vaccination, and booster uptake within these vulnerable communities to inform culturally congruent strategies and interventions. METHODS: In summer 2022, our community-based participatory research partnership conducted 30 key informant interviews and 7 focus groups with 64 Spanish-speaking Latine participants in North Carolina. Interviewees consisted of representatives from health and service organizations, most of whom were engaged with direct service to Spanish speakers. Interviews were conducted in either English or Spanish, depending on the preference of the participant; all focus groups were conducted in Spanish. Interviews and focus groups were conducted in person or by videoconference. RESULTS: Twenty themes emerged that we organize into four domains: general perceptions about COVID-19; barriers to COVID-19 testing, vaccination, and booster uptake; facilitators to COVID-19 testing, vaccination, and booster uptake; and recommendations to promote testing, vaccination, and booster uptake. DISCUSSION: Results underscore important sociocultural determinants of ongoing COVID-19 testing, vaccination, and booster uptake to consider in developing interventions for Spanish-speaking Latines in the United States. Based on this formative work, our partnership developed Nuestra Comunidad Saludable (Our Healthy Community). We are implementing the intervention to test whether trained peer navigators can increase COVID-19 testing, vaccination, and booster uptake among Spanish-speaking Latines through blending in-person interactions and mHealth (mobile health) strategies using social media.

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.

OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovarian cancer patients at risk of early mortality more accurately than the Charlson Comorbidity Index (CCI) for use in health services research. METHODS: We utilized SEER-Medicare data to identify patients with stage IIIC and IV ovarian cancer, diagnosed in 2010-2015. We employed partial least squares regression, a supervised machine learning algorithm, to develop the MCI by extracting latent factors that optimally captured the variation in health insurance claims made in the year preceding cancer diagnosis, and 1-year mortality. We assessed the discrimination and calibration of the MCI for 1-year mortality and compared its performance to the commonly-used CCI. Finally, we evaluated the MCI's ability to reduce confounding in the association of neoadjuvant chemotherapy (NACT) and all-cause mortality. RESULTS: We included 4723 patients in the development cohort and 933 in the validation cohort. The MCI demonstrated good discrimination for 1-year mortality (c-index: 0.75, 95% CI: 0.72-0.79), while the CCI had poor discrimination (c-index: 0.59, 95% CI: 0.56-0.63). Calibration plots showed better agreement between predicted and observed 1-year mortality risk for the MCI compared with CCI. When comparing all-cause mortality between NACT with primary cytoreductive surgery, NACT was associated with a higher hazard of death (HR: 1.13, 95% CI: 1.04-1.23) after controlling for tumor characteristics, demographic factors, and the CCI. However, when controlling for the MCI instead of the CCI, there was no longer a significant difference (HR: 1.05, 95% CI: 0.96-1.14). CONCLUSIONS: The MCI outperformed the conventional CCI in predicting 1-year mortality, and reducing confounding due to differences in baseline health status in comparative effectiveness analysis of NACT versus primary surgery.

Implications of childhood adversity for women's perinatal sleep quality and depressive symptoms over time: A serial mediation model.

This study sought to examine the effects of childhood adversity on the longitudinal associations between perinatal sleep quality and depressive symptoms, and to determine the prospective associations between these constructs over time. A cross-lagged autoregressive model was used to examine the longitudinal association between sleep quality and depressive symptoms at four points during the perinatal period: 18 and 32 weeks of pregnancy, and 6 and 12 weeks postpartum. Longitudinal mediation models were used to examine whether sleep quality or depressive symptoms mediated the effects of childhood adversity on these symptoms. Most participants (86%) reported poor sleep quality during pregnancy. Significant cross-lagged effects of depressive symptoms on subsequent sleep quality were observed during pregnancy and postpartum. Depressive symptoms significantly mediated the effects of childhood trauma on sleep quality during pregnancy, but sleep quality did not significantly mediate the effects of childhood trauma on depressive symptoms. While sleep quality and depressive symptoms tend to co-occur, our analyses indicate that perinatal depressive symptoms work as the primary driver of sleep quality over time. Childhood adversity may have long-reaching effects in women as it was associated with more depressive symptoms in the perinatal period, which in turn appeared to undermine sleep quality.

Save Your Back: Comparison of the Compressive Force on the Lower Back Based on Differences in the Training Techniques.

INTRODUCTION: Musculoskeletal injury prevention for nurses is aimed at removing the need to manually position patients. In the ED, this is not always possible or practical. The purpose of this study is to compare the calculated estimated compressive force on the lumbar spine between recommended lifting techniques and the SHAPE lifting method during the horizontal transfer of a patient. METHODS: Twenty-one student nurses completed the horizontal transfer of a simulated patient while motion was collected using inertial measurement units. Motion data were analyzed to calculate an estimated compressive force on the lumbar spine while completing the movement based on current recommended lifting methods and while using the SHAPE lifting method. RESULTS: A significant reduction in estimated peak and average compressive force at the lumbar spine was found during both the push and the pull portions (P < .001) of the horizontal transfer. DISCUSSION: While the optimal way to limit musculoskeletal injury among nurses is to eliminate the need for manual handling of a patient, this is not always possible in the ED. It is critical that when emergency nurses must reposition a patient, they perform the movement in the most biomechanically sound method while using a friction reduction. These findings, coupled with the previous biomechanical risk factor reduction related to the SHAPE lifting intervention, gives promise to a safer lifting strategy for emergency nurses moving forward.

Performance of a Maternal Risk Stratification System for Predicting Low Apgar Scores.

OBJECTIVE: Maternal risk stratification systems are increasingly employed in predicting and preventing obstetric complications. These systems focus primarily on maternal morbidity, and few tools exist to stratify neonatal risk. We sought to determine if a maternal risk stratification score was associated with neonatal morbidity. STUDY DESIGN: Retrospective cohort study of patients with liveborn infants born at ≥24 weeks at four hospitals in one health system between January 1, 2020, and December 31, 2020. The Expanded Obstetric Comorbidity Score (EOCS) is used as the maternal risk score. The primary neonatal outcome was 5-minute Apgar <7. Logistic regression models determined associations between EOCS and neonatal morbidity. Secondary analyses were performed, including stratifying outcomes by gestational age and limiting analysis to "low-risk" term singletons. Model discrimination assessed using the area under the receiver operating characteristic curves (AUC) and calibration via calibration plots. RESULTS: A total of 14,497 maternal-neonatal pairs were included; 236 (1.6%) had 5-minute Apgar <7; EOCS was higher in 5-minute Apgar <7 group (median 41 vs. 11, p < 0.001). AUC for EOCS in predicting Apgar <7 was 0.72 (95% Confidence Interval (CI) 0.68, 0.75), demonstrating relatively good discrimination. Calibration plot revealed that those in the highest EOCS decile had higher risk of neonatal morbidity (7.6 vs. 1.7%, p < 0.001). When stratified by gestational age, discrimination weakened with advancing gestational age: AUC 0.70 for <28 weeks, 0.63 for 28 to 31 weeks, 0.64 for 32 to 36 weeks, and 0.61 for ≥37 weeks. When limited to term low-risk singletons, EOCS had lower discrimination for predicting neonatal morbidity and was not well calibrated. CONCLUSION: A maternal morbidity risk stratification system does not perform well in most patients giving birth, at low risk for neonatal complications. The findings suggest that the association between EOCS and 5-minute Apgar <7 likely reflects a relationship with prematurity. This study cautions against intentional or unintentional extrapolation of maternal morbidity risk for neonatal risk, especially for term deliveries. KEY POINTS: · EOCS had moderate discrimination for Apgar <7.. · Predictive performance declined when limited to low-risk term singletons.. · Relationship between EOCS and Apgar <7 was likely driven by prematurity..

Experimental and simulation study of self-assembly and adsorption of glycerol monooleate in n-dodecane with varying water content onto iron oxide.

The self-assembly and surface adsorption of glycerol monooleate (GMO) in n-dodecane are studied using a combination of experimental and molecular dynamics simulation techniques. The self-assembly of GMO to form reverse micelles, with and without added water, is studied using small-angle neutron scattering and simulations. A large-scale simulation is also used to investigate the self-assembly kinetics. GMO adsorption onto iron oxide is studied using depletion isotherms, neutron reflectometry, and simulations. The adsorbed amounts of GMO, and any added water, are determined experimentally, and the structures of the adsorbed films are investigated using reflectometry. Detailed fitting and analysis of the reflectometry measurements are presented, taking into account various factors such as surface roughness, and the presence of impurities. The reflectometry measurements are complemented by molecular dynamics simulations, and good consistency between both approaches is demonstrated by direct comparison of measured and simulated reflectivity and scattering length density profiles. The results of this analysis are that in dry systems, GMO adsorbs as self-assembled reverse micelles with some molecules adsorbing directly to the surface through the polar head groups, while in wet systems, the GMO is adsorbed onto a thin layer of water. Only at high surface coverage is some water trapped inside a reverse-micelle structure; at lower surface coverages, the GMO molecules associate primarily with the water layer, rather than self-assemble.

Resilience in Nurses: The Reliability and Validity of the Resilience ScaleTM.

Background and Purpose: The Resilience ScaleTM (RS) RS-25 and shortened RS-14 have measured resilience but not with nurses. Our purpose was to investigate both for nurses. Methods: A random sample of 345 registered nurses from the North Carolina Board of Nursing completed an online questionnaire. Parallel analysis, factor analysis, and receiver operating characteristic analysis were performed. Results: Previous RS factor models had poor confirmatory factor analysis fit. Exploratory factor analysis and item analyses suggested removing items. Good fit was found for an "RS-13" using a bifactor approach. Adequate internal consistency was demonstrated (omega = 0.77-0.90). The RS-13 general factor gave similar accuracy for bullying, physical and mental quality of life, stress, and intent to leave. Conclusions: A shortened RS is comparable in reliability, construct, and convergent validity. It measures nurse resilience well.

Residual symptoms following electroconvulsive therapy: A retrospective cohort study.

BACKGROUND: Residual depressive symptoms following treatment are a burden for patients and are associated with increased risk of relapse. While this phenomenon has been explored following pharmacotherapy, there is little research into residual symptoms following electroconvulsive therapy (ECT). This study quantifies the frequency and type of residual symptoms following ECT treatment. METHODS: This study used retrospective data from patients receiving ECT as part of routine clinical care. Depressive symptomatology was assessed using the Quick Inventory of Depressive Symptomatology - Self-Report 16 item scale (QIDS), which includes 9 symptom domains graded from 0 to 3. We quantified the frequency of mild or greater (QIDS≥1) and moderate or greater (QIDS ≥ 2) residual symptoms following treatment among patients responding to ECT (QIDS decrease ≥50 % from baseline) and non-responders (QIDS decrease <50 %). RESULTS: Among 1799 patients, 1015 (56.4 %) responded to ECT and 784 (43.6 %) did not. Among responders, 99.5 % had at least one residual symptom of mild severity or greater (median = 5, IQR = 3-6) and 83.3 % had at least one residual symptom of moderate severity or greater (median = 1, IQR = 1-2). Among non-responders, 100 % had residual symptoms of mild severity or greater (median = 8, IQR = 7-9), and 99.2 % had a residual symptom of moderate severity or greater (median = 4, IQR = 3-5). The most common residual symptoms among both responders and non-responders were sleep disturbances (93.1 % and 98.7 %, respectively) and sadness (68.9 % and 96.4 %, respectively). LIMITATIONS: Retrospective data from a single freestanding psychiatric hospital. CONCLUSION: Among patients with depression receiving ECT, there were high rates of residual symptoms even among patients responding to treatment.

Dimensional clinical phenotyping using post-mortem brain donor medical records: post-mortem RDoC profiling is associated with Alzheimer's disease neuropathology.

Introduction: Transdiagnostic dimensional phenotypes are essential to investigate the relationship between continuous symptom dimensions and pathological changes. This is a fundamental challenge to post-mortem work, as assessments of phenotypic concepts need to rely on existing records. Methods: We adapted well-validated methodologies to compute National Institute of Mental Health Research Domain Criteria (RDoC) scores using natural language processing (NLP) from electronic health records (EHRs) obtained from post-mortem brain donors and tested whether cognitive domain scores were associated with Alzheimer's disease neuropathological measures. Results: Our results confirm an association of EHR-derived cognitive scores with neuropathological findings. Notably, higher neuropathological load, particularly neuritic plaques, was associated with higher cognitive burden scores in the frontal (ß = 0.38, P = 0.0004), parietal (ß = 0.35, P = 0.0008), temporal (ß = 0.37, P = 0.0004) and occipital (ß = 0.37, P = 0.0003) lobes. Discussion: This proof-of-concept study supports the validity of NLP-based methodologies to obtain quantitative measures of RDoC clinical domains from post-mortem EHR. The associations may accelerate post-mortem brain research beyond classical case-control designs.

Changes in self-reported suicidal ideation during treatment with electroconvulsive therapy: A retrospective cohort study.

OBJECTIVE: Suicidal ideation (SI) is common in patients with depressive symptoms, who are the most common recipients of electroconvulsive therapy (ECT). We sought to quantify changes in self-reported SI occurring during treatment with ECT, and to identify factors associated with persistence of SI in patients beginning treatment with SI. METHOD: Retrospective, single-center cohort study of patients receiving ECT and who self-reported symptoms using Quick Inventory of Depressive Symptomatology (QIDS) prior to ECT and after treatment #5 or #10. Changes in QIDS-reported SI over the course of ECT were calculated, and logistic regression models were performed to assess factors associated with reporting SI at the end of treatment. RESULTS: 2554 provided baseline and follow-up SI scores, of whom, 1931 (75.6%) endorsed SI at baseline. There was a reduction in SI with ECT treatment (McNemar's test; df = 1, Χ2 = 803.7; p < 0.001), and in adjusted models 64.0% of individuals with baseline SI reported resolution of SI with ECT treatment, while 3.3% without baseline SI reported SI at the end of treatment. Higher baseline SI severity and outpatient treatment were associated with a higher odds of persistent SI among individuals beginning treatment with SI. CONCLUSION: Electroconvulsive therapy treatment was associated with reductions in self-reported SI. These results support the use of ECT in the treatment of patients with SI, but further research is needed to determine the effects of ECT on suicidal behavior.