Faculty Bibliography

The 2024 revised McDonald criteria for multiple sclerosis recognize the optic nerve as a topography for dissemination in space. Optical coherence tomography-derived inter-eye differences in peri-papillary retinal nerve fiber layer or ganglion cell-inner plexiform layer thicknesses (≥6μm or ≥4μm, respectively) are proposed for identifying unilateral optic nerve involvement. However, the value of combining inter-eye difference measures and optimal temporal-quadrant peri-papillary retinal nerve fiber layer inter-eye differences remains unclear. We investigated the diagnostic performance of combined inter-eye differences, optimal temporal-quadrant peri-papillary retinal nerve fiber layer inter-eye differences, and examined the effects of time, prior optic neuritis frequency, sex, and race on inter-eye differences. Retinal optical coherence tomography images from all study participants underwent rigorous quality control. Receiver operating characteristic analyses and area under the receiver operating characteristic curves (AUC) were used to determine optimal inter-eye differences of individual and combined measures to distinguish eyes with, from without, prior optic neuritis in people with multiple sclerosis. Mixed-effects models were used to assess impact of time, prior optic neuritis events, sex, and race on inter-eye differences. An independent multiple sclerosis cohort from a second center was examined for external validation. Among 1854 people with multiple sclerosis, optimal inter-eye difference thresholds for identifying unilateral optic nerve involvement were 6μm for peri-papillary retinal nerve fiber layer (AUC=0.80), 4μm for ganglion cell-inner plexiform layer (AUC=0.83), and 8μm for temporal-quadrant peri-papillary retinal nerve fiber layer (AUC=0.71) thicknesses. Peri-papillary retinal nerve fiber layer inter-eye differences ≥6μm or ganglion cell-inner plexiform layer inter-eye differences ≥4μm yielded 87.6% sensitivity, 70.0% specificity, and 64.0% positive predictive value. Concurrent inter-eye differences at lower thresholds (≥5μm peri-papillary retinal nerve fiber layer, ≥3μm ganglion cell-inner plexiform layer) reduced sensitivity to 72.5%, but improved specificity (86.6%) and positive predictive value (76.7%), while maintaining accuracy and negative predictive value. Temporal-quadrant peri-papillary retinal nerve fiber layer inter-eye differences did not improve diagnostic performance. Over a median of 5.1 years, ganglion cell-inner plexiform layer and peri-papillary retinal nerve fiber layer inter-eye differences remained stable. Prior optic neuritis counts and sex did not affect inter-eye differences. Although Black Americans had higher inter-eye differences than White Americans, optimal thresholds were comparable across races. The validation cohort comprising 254 people with multiple sclerosis confirmed these findings. In conclusion, concurrent peri-papillary retinal nerve fiber layer (≥5μm) and ganglion cell-inner plexiform layer inter-eye differences (≥3μm) improve unilateral optic nerve involvement detection versus either alone (≥6μm or ≥4μm, respectively), while temporal-quadrant peri-papillary retinal nerve fiber layer inter-eye differences offer limited benefit. Inter-eye differences remain stable longitudinally and unaffected by prior optic neuritis frequency.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Vascular dysfunction contributes to Alzheimer disease (AD) and related dementias (ADRDs), but the underlying mechanisms remain unclear. Previous studies link midlife hemostasis and platelet aggregation measures to late-life dementia risk. We aimed to determine whether platelet aggregation in midlife is associated with imaging markers of AD pathology. METHODS:F-flortaucipir) PET uptake in dementia-free, middle-aged adults from the Framingham Heart Study. Co-primary outcomes included amyloid and tau uptake in AD-vulnerable regions. We also examined an MRI-based cortical thickness signature of AD risk as a secondary outcome. We used multivariable regression models adjusted for demographic and clinical factors, considering potential nonlinear associations. RESULTS:< 0.035), consistent with a neurodegenerative pattern. DISCUSSION/CONCLUSIONS:Our findings indicate that platelet aggregation is linked to PET and MRI markers of AD pathology as early as midlife. These findings support further investigation of platelet-mediated mechanisms in AD pathogenesis.

BACKGROUND:Current primary prevention guidelines recommend estimation of long-term cardiovascular disease (CVD) risk among younger adults to enable preventive efforts earlier in the life course. However, conceptualizing absolute risk estimates over this time horizon is challenging for both clinicians and patients. Framing risk relative to peers (ie, "population-based percentiles") as complementary information may have utility for risk communication. OBJECTIVES/OBJECTIVE:We sought to develop population-based age- and sex-specific percentiles of 30-year absolute risk estimates for CVD, atherosclerotic cardiovascular disease (ASCVD), and heart failure (HF) with the PREVENT (Predicting Risk of CVD EVENTs) equations. METHODS:We used data from a nationally representative sample of U.S. adults aged 30 to 59 years without prevalent CVD in the National Health and Nutrition Examination Survey (NHANES) 2011 to March 2020. We calculated 30-year risk estimates with PREVENT for eligible participants and derived age- and sex-specific percentiles at each age (using rolling windows of ±5 years) for each outcome of CVD, ASCVD, and HF. RESULTS:Among 8,686 NHANES participants, representing approximately 91 million U.S. adults aged 30 to 59 years in the final sample, mean age was 44.8 ± 0.2 years among female participants and 44.2 ± 0.2 years among male participants. The median 30-year absolute CVD risk in the overall sample was 13.1%. Then, corresponding percentile values were developed and evaluated for each age among women and men aged 30 to 59 years for 30-year risk of CVD, ASCVD, and HF. For example, at age 45 years, the 50th percentile of 30-year CVD risk for was 9.9% for women and 16.2% for men, and the 75th percentile was 14.7% for women and 21.2% for men at that age. Similar patterns were observed for ASCVD and HF, with higher 30-year absolute risk in men compared with women. An online tool was developed to present risk percentiles alongside absolute 30-year risk for CVD, ASCVD, and HF. CONCLUSIONS:Population-based age- and sex-specific percentiles for 30-year CVD risk with PREVENT may offer a complementary tool for clinicians and patients in addition to communicating absolute CVD risk estimates. Prospective studies should test whether this approach improves risk perception or decision-making in younger adults.

BACKGROUND:Effective interpersonal communication is associated with improved health-related outcomes, yet it is unclear to what extent this occurs in triadic clinic visits for persons living with dementia (PLWD) and few tools exist to characterize triadic interpersonal communication and assess its effectiveness. The objective of this project is to characterize the interpersonal communication that occurs during triadic visits for PLWD, examine how interpersonal communication is related to health outcomes and use this understanding to adapt an innovative clinic visit audio recording intervention, HealthPAL (Personal Audio Library) for use in this setting. METHODS:Following the NIH Stage Model, we will redesign a visit recording platform, HealthPAL, which leverages natural language processing to structure visit information. In Aim 1, we will use an explanatory sequential mixed methods design. Informed by the Behavior Change Wheel, targets for behavior change will be identified using quantitative assessment of interpersonal communication during triadic visits (200 dyads, 3 visits annually; ∼600 visits), supplemented by semi-structured interviews with a purposive sample of triads (n = 42); In Aim 2, we will use participatory design methods (n = 60) to redesign HealthPAL using findings from Aim 1; and in Aim 3, we will use an open label, single-arm, multi-site pilot trial (n = 30) to determine usability, feasibility and acceptability of HealthPAL and gather preliminary data on its impact on interpersonal communication in triadic AD/ADRD visits. We hypothesize: (1) Constructs from models of interpersonal communication will be associated with health-related outcomes; (2) HealthPAL will surpass usability, feasibility, and acceptability metrics for dyads and clinicians. DISCUSSION/CONCLUSIONS:This work is a necessary first step to improving PLWD triadic care by identifying behaviors that impact triadic interpersonal communication and their associations with health-related outcomes. The novel intervention that we will develop--the use of visit recordings--and the diverse and extensive data we will collect will serve as a unique resource that can be leveraged to address other gaps in clinical knowledge related to the care of PLWD.

BACKGROUND:Children from migrant families with absent parents are more likely to have poorer physical and mental health than children from non-migrant families. The impact of labor migration on left-behind family members in South Asian countries is not well-known. This study aimed to examine the patterns of labor migration and its impact on the health and development of children and their caregivers in rural Nepal. METHODS:Baseline family data collected from a school-based violence prevention program were utilized. Parents/caregivers (N = 346) with school-aged children (aged 3 to 15 years attending nursery to primary grades) from the rural Arghakhanchi district of Nepal were included in the study. A series of descriptive and chi-square analyses were carried out to explore the pattern of labor migration and differences between labor-migrant and non-labor-migrant families. Multivariate linear and logistic regression analyses were applied to explore the correlates and moderators involved. RESULTS:Labor migration has been a common practice in rural Nepal, with an estimated 49% of families having parents working overseas, mostly in India (57%) and Gulf countries (39%) on low-skill labor jobs. Labor migration was significantly associated with left-behind caregivers' and children's mental health. Left-behind caregivers in father-only labor-migrant families reported higher levels of depression than did parents in non-labor migrant families and left-behind children from labor-migrant families reported greater anger than did children from non-labor migrant families. The impact of labor migration on families was moderated by social class. For low social-class father migrant families, left-behind children were at greater risk for developmental delay and behavioral problems, but there seems to be a protective effect for high social-class father migrant families (with lower risk of developmental delay and problem behaviors compared to all other groups). CONCLUSIONS:Labor migration has a substantial impact on the mental health of left-behind families and children. The impact of labor migration may vary by living social-cultural context. Understanding the complex dynamics of labor migration has important implications for local and global migration-related health service planning.

Early childhood is a critical period for overall development and well-being, yet children from low-income and low-resourced families, such as Chinese American immigrant families, often have unmet mental health needs as they face additional barriers like limited English proficiency and health literacy. Cultural and linguistic adaptation is essential for equitable access to resources and care. Despite the need, early childhood mental health among Chinese American families remains significantly understudied. A digital mental health solution may pose greater access and convenience to address the mental health needs of this community. Thus, this study aims to collaboratively develop a web-based app called OurChild, which provides culturally and linguistically adapted early childhood mental health and development resources for Chinese American immigrant families in New York City. Using the Participatory Cultural Adaptation Framework for Implementation Research (PCAFIR), the project involves a multiphased participatory co-design process: 1. understanding community needs through formative research and engagement; 2. building a digital library with evidence-based and culturally tailored content; 3. designing a culturally tailored web-based app using a participatory approach; and 4. refining and validating the design through user testing. Informed by formative data from existing studies and programs; focus groups and interviews with community experts (n = 6) and parents (n = 11); user testing with parents (n = 11), and through an iterative re-design process, the app was designed to be user-friendly, culturally relevant, and evidence-based. This study described the co-design process and highlighted the lessons learned in developing culturally tailored digital health tools to promote digital health equity for underserved communities.

BACKGROUND:The Enhanced Quality in Primary Care for Elders with Diabetes-ADRD (EQUIPED-ADRD) is a quality improvement and pragmatic cluster-randomized controlled trial that uses clinical decision guidelines to streamline the care of older adults with diabetes mellitus and Alzheimer's disease/Alzheimer's disease-related Dementia (DM-AD/ADRD). This study tests whether the EQUIPED-ADRD intervention will increase the proportion of older adults with DM and AD/ADRD with desirable glycemic ranges, and reduce treatment burden, dementia severity, and healthcare utilization among participants and their care partners in the intervention arm compared to those in the control arm. METHODS:We will recruit older adults (≥65 years) with both DM and AD/ADRD diagnoses, who have care partners, and receive care at the enrolled New York University clinics. The intervention involves the use of panel managers to streamline the integration of clinical decision guidelines among primary care providers and improve the experiences of care partners and patients. Those in the control arm will have no panel management. We will conduct surveys and interviews, and extract data from EMR and Medicare claims to assess the association between the intervention and primary and secondary outcomes. The primary outcome is achieving within-range HbA1c, while the secondary outcomes include measures of healthcare utilization. Patient and care partner treatment burden, dementia symptoms, and care partner diabetes care distress. CONCLUSIONS:The EQUIPED-ADRD intervention (implemented between 2018 and 2021) will assess the effect of an institutional guideline on the quality of life and health outcomes of older adults with DM-AD/ADRD and their care partners. Clinical Trial NumberNCT03723707.

BACKGROUND/UNASSIGNED:With rising patient volumes and a focus on quality, our health system had the objective to create a more efficient way to ensure accurate documentation of colorectal cancer (CRC) screening intervals from inbound colonoscopy reports to ensure timely follow-up. We developed an integrated end-to-end workflow solution using machine learning (ML) and robotic process automation (RPA) to extract and update electronic health record (EHR) follow-up dates from unstructured data. OBJECTIVE/UNASSIGNED:This study aimed to automate data extraction from external, free-text colonoscopy reports to identify and document recommended follow-up dates for CRC screening in structured EHR fields. METHODS/UNASSIGNED:As proof of concept, we outline the process development, validity, and implementation of an approach that integrates available tools to automate data retrieval and entry within the EHR of a large academic health system. The health system uses Epic Systems as its EHR platform, and the ML model used was trained on health system patient colonoscopy reports. This proof-of-concept process study consisted of six stages: (1) identification of gaps in documenting recommendations for follow-up CRC screening from external colonoscopy reports, (2) defining process objectives, (3) identification of technologies, (4) creation of process architecture, (5) process validation, and (6) health system-wide implementation. A chart review was performed to validate process outcomes and estimate impact. RESULTS/UNASSIGNED:We developed an automated process with 3 primary steps leveraging ML and RPA to create a fully orchestrated workflow to update CRC screening recall dates based on colonoscopy reports received from external sources. Process validity was assessed with 690 scanned colonoscopy reports. During process validation, the overall automated process achieved an accuracy of 80.7% (557/690, 95% CI 77.8%-83.7%) for correctly identifying the presence or absence of a valid follow-up date and a follow-up date false negative identification rate of 32.9% (130/395, 95% CI 29.4%-36.4%). From the organization-wide implementation to go-live until December 31, 2024, the system processed 16,563 external colonoscopy reports. Of these, 35.3% (5841/16,563) had a follow-up date meeting the relevant ML model threshold and thus were identified as ready for RPA processing. CONCLUSIONS/UNASSIGNED:Implementation of an automated workflow to extract and update CRC screening follow-up dates from colonoscopy reports is feasible and has the potential to improve accuracy in patient recall while reducing documentation burden. By standardizing data ingestion, extending this approach to various unstructured data types can address deficiencies in structured EHR documentation and solve for a lack of data integration and reporting for quality measures. Automated workflows leveraging ML and RPA offer practical solutions to overcome interoperability challenges and the use of unstructured data within health care systems.