Faculty Bibliography

OBJECTIVE:Risk calculators (RC) to predict clinical outcomes are gaining interest. An RC to estimate risk of bipolar spectrum disorders (BPSD) could help reduce the duration of undiagnosed BPSD and improve outcomes. Our objective was to adapt an RC previously validated in the Pittsburgh Bipolar Offspring Study (BIOS) sample to achieve adequate predictive ability in both familial high-risk and clinical high-risk youths. METHOD/METHODS:Participants (aged 6-12 years at baseline) from the Longitudinal Assessment of Manic Symptoms (LAMS) study (N = 473) were evaluated semi-annually. Evaluations included a Kiddie Schedule for Affective Disorders (K-SADS) interview. After testing an RC that closely approximated the original, we made modifications to improve model prediction. Models were trained in the BIOS data, which included biennial K-SADS assessments, and tested in LAMS. The final model was then trained in LAMS participants, including family history of BPSD as a predictor, and tested in the familial high-risk sample. RESULTS:Over follow-up, 65 youths newly met criteria for BPSD. The original RC identified youths who developed BPSD only moderately well (area under the curve [AUC] = 0.67). Eliminating predictors other than the K-SADS screening items for mania and depression improved accuracy (AUC = 0.73) and generalizability. The model trained in LAMS, including family history as a predictor, performed well in the BIOS sample (AUC = 0.74). CONCLUSION/CONCLUSIONS:The clinical circumstances under which the assessment of symptoms occurs affects RC accuracy; focusing on symptoms related to the onset of BPSD improved generalizability. Validation of the RC under clinically realistic circumstances will be an important next step.

BACKGROUND:Evidence increasingly suggests that ASD manifests differently in females than males. Previous reviews investigating sex/gender differences in social interaction and social communication have focused at the level of broad constructs (e.g. comparing algorithm scores from pre-existing diagnostic instruments) and have typically reported no significant differences between males and females. However, a number of individual studies have found sex/gender differences in narrow construct domains. METHODS:We conducted a systematic review and random effects model meta-analyses (in January 2019 and updated January 2020) that investigated sex/gender differences in narrow construct measures of social communication and interaction in autistic and nonautistic children and adolescents, and adults. Study quality was appraised using the Appraisal Tool for Cross-Sectional Studies (AXIS, BMJ Open, 6, 2016, 1). RESULTS:Across 16 studies (including 2,730 participants), the analysis found that female (vs. male) individuals with ASD had significantly better social interaction and social communication skills (SMD = 0.39, p < .001), which was reflective of a similar sex/gender profile in nonautistic individuals (SMD = 0.35, p < .001). Nonautistic males had significantly better social interaction and communication than males with ASD (SMD = 0.77, p < .001). Nonautistic females also had significantly better social interaction and communication than females with ASD (SMD = 0.72, p  <.001). Nonautistic males had better social interaction and communication than females with ASD, though this difference was not significant (SMD = 0.30, p = .07). CONCLUSIONS:This systematic review and meta-analysis highlighted important sex/gender differences in social interaction and communication for individuals with ASD, likely not captured by pre-existing diagnostic instruments, which potentially contribute to the under recognition of autism in females, and may need to be reflected in the diagnostic process.

OBJECTIVE:To determine whether there are racial/ethnic differences in depression treatment for caregivers investigated by the US child welfare system. METHODS:This cross-sectional study used baseline data from the Second National Survey of Child and Adolescent Well-being, a nationally representative sample of children and caregivers investigated by US child welfare agencies (February 2008-April 2009). We included permanent caregivers who met criteria for major depression and had available covariate data (n = 908). In multivariable logistic regression models, we estimated the associations between caregiver race/ethnicity and past-year receipt of: any depression treatment, minimally adequate depression treatment, and depression treatment from 4 sectors (general medical, psychiatry, nonpsychiatry mental health, and human services). We controlled for clinical need and access variables according to the Institute of Medicine's definition of health care disparities. RESULTS:Black caregivers had the lowest rates of treatment receipt of any racial/ethnic group, with 42.2% receiving any depression treatment and 17.2% receiving minimally adequate depression treatment in the past year. In multivariable analyses controlling for clinical need and access variables, Black caregivers were less likely than White caregivers to receive any depression treatment (odds ratio [OR] = 0.49 [95% CI: 0.24-0.97]), minimally adequate depression treatment (OR = 0.37 [95% CI: 0.16-0.85]), and depression treatment from the general medical sector (OR = 0.40 [95% CI: 0.18-0.89]) in the past year (all P< .05). CONCLUSIONS:Future research should examine the underlying mechanisms of Black-White disparities in depression treatment for caregivers involved with the US child welfare system and develop targeted interventions to promote equitable mental health care for this highly vulnerable population.

[This corrects the article DOI: 10.1007/s40670-021-01320-z.].

Infants' free-play behavior is highly variable. However, in developmental science, traditional analysis tools for modeling and understanding variable behavior are limited. Here, we used Hidden Markov Models (HMMs) to capture behavioral states that govern infants' toy selection during 20 minutes of free play in a new environment. We demonstrate that applying HMMs to infant data can identify hidden behavioral states and thereby reveal the underlying structure of infant toy selection and how toy selection changes in real time during spontaneous free play. More broadly, we propose that hidden-state models provide a fruitful avenue for understanding individual differences in spontaneous infant behavior.

Investments in autism spectrum disorder (ASD) research, guided by the Interagency Autism Coordinating Committee (IACC), have focused disproportionately on etiology over a well-established stakeholder priority area: research to improve accessibility and quality of community-based services. This study analyzed National Institutes of Health ASD services research funding from 2008 to 2018 to examine funding patterns, evaluate the impact of IACC objectives, and identify future directions. Approximately 9% of total funds were allocated to services research. This investment remained relatively stable across time and lacked diversity across domains (e.g., area of focus, ages sampled, implementation strategies used). While advancements were observed, including increased prevalence of projects focused on adult samples and on dissemination/implementation and prevention areas, greater investment in service research is critically needed.

INTRODUCTION:COVID-19 affects multiple organ systems causing substantial long-term morbidity. The implications of the Post-Acute Sequelae of SARS-CoV-2 infection, particularly for primary care, remain unknown. This cross-sectional study examines new symptoms reported at primary care encounters during three post-acute follow-up intervals after initial SARS-CoV-2 infection. METHODS:Electronic health record data from the NYU Langone COVID Deidentified Dataset were queried for adults with a positive SARS-CoV-2 PCR test, and then restricted to those with a new ICD-10-CM code documented at a post-acute COVID-related primary care follow-up >14 days after testing positive. New diagnoses and the corresponding Clinical Classifications Software Refined categories were assessed at the following intervals: 0.5-3 months ("subacute"), 3-6 months ("prolonged"), and 6-9 months ("persistent"). RESULTS:Out of 3,154 patients, a new ICD-10-CM code was documented among 499 patients (∼16%). Respiratory complaints, including cough, shortness of breath, dyspnea, and hypoxemia, were most common. Malaise and fatigue were reported consistently among 10-13% of patients at all three time-intervals. Musculoskeletal pain, circulatory symptoms, and sleep-wake disorders were also observed at primary care follow-up. CONCLUSION:This cross-sectional study provides support of a post-acute COVID syndrome, demonstrating that patients continue to experience symptoms after the acute infection period. Extensive follow-up data allowed for examining new symptoms up to 9 months after initial SARS-CoV-2 infection. Understanding of the course of multi-organ post-acute sequelae is restricted by cross-sectional study design limitations. Standardized, sequelae-related ICD-10-CM codes to specify the type and duration of post-acute COVID-related symptoms would enable better monitoring of the growing number of SARS-CoV-2 infection survivors.

The purpose of this study was to develop a greater understanding of the factors influencing the adoption of evidence-based interventions in outpatient mental health clinics serving youth. An improved understanding of these factors can potentially improve efforts to ensure effective adoption, implementation, and sustainment of evidence-based interventions, and thus improve treatment for youth in mental health settings. This explanatory cross-sectional study involves secondary data analysis of a longitudinal randomized control intervention trial. The SEM- based model that was tested supported the primary hypothesis that a more supportive organizational climate with greater readiness for change is more likely to improve the chances for the adoption of evidence-based interventions in outpatient mental health clinics serving youths.

INTRODUCTION/BACKGROUND:Electronic flashcards allow repeated information exposure over time along with active recall. It is increasingly used for self-study by medical students but remains poorly implemented for graduate medical education. The primary goal of this study was to determine whether a flashcard system enhances preparation for the in-training examination in obstetrics and gynecology (ob-gyn) conducted by the Council on Resident Education in Obstetrics and Gynecology (CREOG). METHODS:Ob-gyn residents at Duke University were included in this study. A total of 883 electronic flashcards were created and distributed. CREOG scores and flashcard usage statistics, generated internally by interacting with the electronic flashcard system, were collected after the 2019 exam. The primary outcome was study aid usage and satisfaction. The secondary outcome was the impact of flashcard usage on CREOG exam scores. RESULTS: = 0.06). DISCUSSION/CONCLUSIONS:This flashcard resource was well received by ob-gyn residents for in-training examination preparation, though it was not significantly correlated with improvement in CREOG scores after adjusting for post-graduate year.

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.