Jonathan E. Alpert - US grants

[unreadable] DESCRIPTION (provided by applicant): This is a revision of our multi-site collaborative Ancillary Study to the STAR*D Project, in which we propose to study biomarkers that may help guide treatment by prospectively identifying patients with treatment resistant depression (TRD). Pilot data from both randomized clinical trials and naturalistic treatment show that a quantitative electroencephalographic (QEEG) measure, cordance, is associated with response and nonresponse to pharmacotherapy; changes in prefrontal activity early in treatment are predictive of later response. Preliminary data suggest that a biomarker model using the cordance indicator can identify (a) patients who will be resistant to treatment prescribed in Level 2 of the STAR*D protocol, and (b) patients who will have an initial but non-sustained, "placebo-like" response. Such prospective identification would allow physicians to undertake more sophisticated regimens earlier and attain improved clinical outcome. The revisions in this proposal include expansion of the rationale for using the cordance biomarker, additional detail about the STAR*D protocol, additional pilot data, and subject flow projections that have been refined to reflect recent STAR*D enrollment figures. The three specific aims of this project are: (1) to evaluate the use of neurophysiologic biomarkers in an effectiveness treatment trial setting for acceptability and cost effectiveness; (2) to ascertain the validity of cordance indicators in prospectively identifying depressed patients who will be resistant to their Level 2 treatment choices; and (3) to examine the use of these indicators in identifying patients who will not have a sustained response to treatment. We will test specific hypotheses: (1) assessment with QEEG will be acceptable and offer cost effective guidance in an effectiveness treatment trial; (2) acute treatment response in Level 2 to specific switching or augmentation strategies will be predicted by changes in prefrontal cordance in the first two weeks of Level 2 treatment; and (3) sustained treatment response will be predicted by changes in prefrontal values after two weeks of treatment. 72 subjects with depression will be recruited when they enter treatment in Level 2 of the protocol at either of two STAR*D sites, 36 each at UCLA's Neuropsychiatric Institute and Harvard's Massachusetts General Hospital. QEEG data will be recorded at the start of Level 2 and after two additional weeks. Treating clinicians and subjects will be blinded to physiologic data, and outcomes will be assessed using the instruments integral to the STAR*D protocol. Subjects will be followed-up at 3 and 6 months after completing Level 2 to assess whether responses are sustained. The test of proportion and t-tests will be used to evaluate acceptability and cost effectiveness. Prediction of acute response will be tested with chi square and linear regression models. Independent examination of the data at the two sites will be used to assess the generalizability of the cordance biomarker method. The relationship of our biomarkers to sustained response will be tested by chi square analyses. Secondary analyses will use general linear models and growth mixture modeling to examine how early cordance changes relate to patient and illness factors and to functional outcomes. [unreadable] [unreadable]

PROJECT SUMMARY / ABSTRACT In response to PAR-20-237, ?community interventions to address the consequences of the COVID-19 pandemic among health disparity and vulnerable populations,? we propose to study a parent-focused mentalizing intervention with a smartphone health platform in vulnerable populations in the Bronx. The Bronx?the poorest borough in New York City (NYC) with 84% of its population comprised of minorities?has been hit disproportionally by the COVID-19 pandemic. For caregivers in the Bronx, the pandemic has caused unprecedented psychological distress; in addition to combating social determinants of health (SDOH), these families now face greater financial insecurity and challenges related to their school-aged children. Furthermore, social distancing requirements and limited telehealth resources for Bronx families have posed greater barriers to healthcare. Such parental distress contributes to heightened risk of transgenerational cycles of psychological stress, trauma and maltreatment. Addressing these challenges, we propose a multimodal study tackling both parental vulnerability and healthcare access. a) Targeting parental vulnerability, we propose to build upon our 12-session therapeutic CARE program, which utilizes Mentalization Based Therapy and designed for parents of children in treatment in a community psychiatry clinic in our health system. Multiple randomized controlled trials (RCTs) of attachment-based parenting interventions, including from our group in disadvantaged cohorts from the Bronx. b) Targeting healthcare access, we propose to utilize the HIPAA-compliant and IRB-approved Valera Health smartphone application (?app?), which has been studied in our health system in selected cohorts and resulted in greater access to healthcare. Building upon our work and clinical infrastructure, we hypothesize that both the CARE program and the Valera app will mitigate the psychological effects of COVID-19 while enhancing access to healthcare in the Bronx. The study will take place at Montefiore Medical Center (MMC), the largest health system in the Bronx, which serves predominantly racial and ethnic minority individuals from underserved families. We will focus on three highly vulnerable cohorts: a) caregivers of children with psychiatric conditions (N=130); b) caregivers of children with autoimmune illnesses (N=130), and; c) caregivers who are frontline healthcare workers at MMC (N=100); all presenting with moderate stress. A RCT with 4 arms (2 x 2 design) will test our hypothesis: a) CARE program alone; b) CARE program + Valera app; c) Parent Education (PE) alone; d) PE + Valera app. Smartphones and connectivity will be provided as needed. Surveys at study enrollment, 6-, 12- and 24-weeks will assess parental stress, mentalizing capacity (reflective functioning), access to healthcare, mood and anxiety and additional parent and child clinical measures. Machine learning approaches will explore complex patterns as predictors of outcome including COVID-19 illness/deaths, medical comorbidity, housing, poverty, psychopathology, age, family functioning, and trauma. Impact. This multimodal study addresses key strategies to mitigate the psychological and health impact of COVID-19 in vulnerable populations.