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.
Although combination antiretroviral therapy (ART) with three or more drugs is highly effective in suppressing viral load for people with HIV (human immunodeficiency virus), many ART agents may exacerbate mental health‐related adverse effects including depression. Therefore, understanding the effects of combination ART on mental health can help clinicians personalize medicine with less adverse effects to avoid undesirable health outcomes. The emergence of electronic health records offers researchers' unprecedented access to HIV data including individuals' mental health records, drug prescriptions, and clinical information over time. However, modeling such data is challenging due to high dimensionality of the drug combination space, the individual heterogeneity, and sparseness of the observed drug combinations. To address these challenges, we develop a Bayesian nonparametric approach to learn drug combination effect on mental health in people with HIV adjusting for sociodemographic, behavioral, and clinical factors. The proposed method is built upon the subset‐tree kernel that represents drug combinations in a way that synthesizes known regimen structure into a single mathematical representation. It also utilizes a distance‐dependent Chinese restaurant process to cluster heterogeneous populations while considering individuals' treatment histories. We evaluate the proposed approach through simulation studies, and apply the method to a dataset from the Women's Interagency HIV Study, showing the clinical utility of our model in guiding clinicians to prescribe informed and effective personalized treatment based on individuals' treatment histories and clinical characteristics.
more » « less- Award ID(s):
- 1918854
- NSF-PAR ID:
- 10397039
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 78
- Issue:
- 3
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 988-1000
- Size(s):
- ["p. 988-1000"]
- Sponsoring Org:
- National Science Foundation
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Method We define SSIs, summarize research supporting their utility for ED symptoms and other mental health problems, and recommend future directions for work in this domain.
Results Single‐session interventions may hold promise to reduce some ED symptoms and risk factors, including restrictive eating and negative body image. Steps toward realizing this promise include (1) testing whether existing evidence‐based SSIs (e.g., for depression) can also reduce EDs, risk factors, and symptoms; (2) developing novel SSIs that target modifiable ED risk factors and symptoms largely unaddressed by SSIs, such as purging and binge eating; (3) studying diverse implementation pathways; (4) capitalizing on SSIs' transdiagnostic utility to broaden funding opportunities; and (5) educating ED researchers and clinicians about SSIs.
Discussion Understanding the strengths and limits of mechanism‐targeted SSIs for ED‐related problems could be a low‐risk, high‐reward avenue toward reducing EDs at scale.
Public Significance Most individuals experiencing EDs never access any form of treatment, creating an urgent need to identify ED interventions built to overcome barriers to engaging with care. This Forum article introduces SSIs as a promising path to rapidly developing and testing accessible, evidence‐based ED supports; supplementing existing ED treatment models; and reducing the individual, familial, and societal burdens of EDs at scale.
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Abstract Motivation The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug–drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.
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AN fared considerably more poorly. All patients withBED should be screened for mental health and gastrointestinal comorbidities and offered referral and treatment options.Public Significance Individuals experiencing binge‐eating disorder have severe symptomology, but those who have experienced binge‐eating disorder and anorexia nervosa fare even more poorly. Our study emphasizes that patients with binge‐eating disorder would benefit from being screened for mental health and gastrointestinal comorbidities, and clinicians should consider history of unhealthy weight control behaviors to inform treatment and relapse prevention.
