Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals’ treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women’s Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.
Positive psychological attributes are associated with better health outcomes, yet few studies have identified their underlying constructs and none have examined their temporal trajectories in clinical vs. non-clinical samples. From data collected over 4 years from people with HIV (PWH) and HIV-uninfected (HIV−) participants, we identified two latent factors (internal strengths; socioemotional support) based on responses to seven positive psychological attributes. Internal strengths increased over 4 years for PWH, but not for HIV− comparisons. Socioemotional support did not change significantly in either group. Lower internal strengths and worse socioemotional support were related to greater depressive symptoms. We speculate that improvement in internal strengths in PWH could reflect their being in care, but this requires further study to include PWH not in care. Given the apparent malleability of internal strengths and their association with improved health outcomes, these attributes can serve as promising intervention targets for PWH.
more » « less- NSF-PAR ID:
- 10380491
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- AIDS and Behavior
- Volume:
- 27
- Issue:
- 2
- ISSN:
- 1090-7165
- Page Range / eLocation ID:
- p. 628-640
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary -
Psychosocial states significantly impact T1D care and management for youth and their families. As part of a clinical pragmatic study, we report the number of elevated Patient Reported Outcomes (PROs) in newly diagnosed families and track their progress to psychological care. Parents/guardians (PG, n=125/133 4T Study 1 participants) and youth ≥11 years (n=60) were eligible to complete baseline, 3-, and 6-month PROs. PG completed Diabetes Distress Scale - Parent (DDS-P) and youth completed Diabetes Distress Scale (DDS-2) and PROMIS Pediatric Global Health Scale (PGH). Elevated PROs were based on published guidelines and were referred to the clinic's psychological services. Survey completeness was verified by staff to identify false flags. Staff reapproached the participant's psychologist for re-flagged PROs >3 months after the last visit. Over the three study time periods, a total of 99 PROs flags were evaluated (Table). At baseline, there were 32% flagged PROs, which decreased to 27% and 23% at 3 and 6 months, respectively. Elevated DDS-P was the most common reason for referral (75%). Early psychological intervention may explain the reduction in elevated PROs over the study period. With the implementation of systematic PROs in this new onset population, we observed it was common to have diabetes distress and families were receptive to psychological services. Disclosure A.Schneider-utaka: None. F.K.Bishop: None. D.P.Zaharieva: Advisory Panel; Dexcom, Inc., Research Support; Hemsley Charitable Trust, International Society for Pediatric and Adolescent Diabetes, Insulet Corporation, Speaker's Bureau; American Diabetes Association, Ascensia Diabetes Care, Medtronic. P.Prahalad: None. M.Desai: None. D.M.Maahs: Advisory Panel; Medtronic, LifeScan Diabetes Institute, MannKind Corporation, Consultant; Abbott, Research Support; Dexcom, Inc. K.K.Hood: Consultant; Cecelia Health. A.Addala: None. E.Pang: None. A.L.Cortes-navarro: None. I.Balistreri: None. A.Loyola: None. N.Arrizon-ruiz: None. S.A.Alamarie: None. V.Ritter: None. B.Shaw: None. Funding National Institutes of Health (R18DK122422)more » « less
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Predicting polycystic ovary syndrome with machine learning algorithms from electronic health recordsmore » « less
Introduction Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis.
Methods This is a retrospective cohort study from a SafetyNet hospital’s electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound.
Results We developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG.
Conclusion Machine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary.
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Background Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown.
Purpose To test whether WMT affects PWH brain functional connectivity in resting‐state fMRI (rsfMRI).
Study Type Prospective.
Population A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53
HIV ‐seronegative controls (SN , ages 49.5 ± 1.6 years, six women).Field Strength/Sequence Axial single‐shot gradient‐echo echo‐planar imaging at 3.0 T was performed at baseline (TL1), at 1‐month (TL2), and at 6‐months (TL3), after WMT.
Assessment All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training,
n = 58: 28 PWH, 30 SN; nonadaptive training,n = 48: 25 PWH, 23 SN), 25 sessions over 5–8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2.Statistical Tests Two‐way analyses of variance (ANOVA) on GT metrics and two‐sample
t ‐tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set atP < 0.05 after false discovery rate correction.Results The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (
P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = −0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%).Data Conclusion ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH.
Evidence Level 1
Technical Efficacy 1
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null (Ed.)Background The COVID-19 pandemic has caused several disruptions in personal and collective lives worldwide. The uncertainties surrounding the pandemic have also led to multifaceted mental health concerns, which can be exacerbated with precautionary measures such as social distancing and self-quarantining, as well as societal impacts such as economic downturn and job loss. Despite noting this as a “mental health tsunami”, the psychological effects of the COVID-19 crisis remain unexplored at scale. Consequently, public health stakeholders are currently limited in identifying ways to provide timely and tailored support during these circumstances. Objective Our study aims to provide insights regarding people’s psychosocial concerns during the COVID-19 pandemic by leveraging social media data. We aim to study the temporal and linguistic changes in symptomatic mental health and support expressions in the pandemic context. Methods We obtained about 60 million Twitter streaming posts originating from the United States from March 24 to May 24, 2020, and compared these with about 40 million posts from a comparable period in 2019 to attribute the effect of COVID-19 on people’s social media self-disclosure. Using these data sets, we studied people’s self-disclosure on social media in terms of symptomatic mental health concerns and expressions of support. We employed transfer learning classifiers that identified the social media language indicative of mental health outcomes (anxiety, depression, stress, and suicidal ideation) and support (emotional and informational support). We then examined the changes in psychosocial expressions over time and language, comparing the 2020 and 2019 data sets. Results We found that all of the examined psychosocial expressions have significantly increased during the COVID-19 crisis—mental health symptomatic expressions have increased by about 14%, and support expressions have increased by about 5%, both thematically related to COVID-19. We also observed a steady decline and eventual plateauing in these expressions during the COVID-19 pandemic, which may have been due to habituation or due to supportive policy measures enacted during this period. Our language analyses highlighted that people express concerns that are specific to and contextually related to the COVID-19 crisis. Conclusions We studied the psychosocial effects of the COVID-19 crisis by using social media data from 2020, finding that people’s mental health symptomatic and support expressions significantly increased during the COVID-19 period as compared to similar data from 2019. However, this effect gradually lessened over time, suggesting that people adapted to the circumstances and their “new normal.” Our linguistic analyses revealed that people expressed mental health concerns regarding personal and professional challenges, health care and precautionary measures, and pandemic-related awareness. This study shows the potential to provide insights to mental health care and stakeholders and policy makers in planning and implementing measures to mitigate mental health risks amid the health crisis.more » « less
