Document Type
Article
Abstract
Background: Given the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a statewide cohort of people living with HIV (PLWH) in South Carolina (SC), the overall goal of the proposed research is to examine the dynamic patterns of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to a beta version of service-ready tools for clinical decision support.
Methods: The PLWH cohort will be identifed through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Ofce of Revenue and Fiscal Afairs (RFA) will extract longitudinal EHR clinical data of all PLWH in SC from multiple health systems, obtain data from other state agencies, and link the patient-level data with county-level data from multiple publicly available data sources. Using the deidentifed data, the proposed study will consist of three operational phases: Phase 1: “Pattern Analysis” to identify the longitudinal dynamics of viral suppression using multiple viral load indicators; Phase 2: “Model Development” to determine the critical predictors of multiple viral load indicators through artifcial intelligence (AI)-based modeling accounting for multilevel factors; and Phase 3: “Translational Research” to develop a multifactorial clinical decision system based on a risk prediction model to assist with the identifcation of the risk of viral failure or viral rebound when patients present at clinical visits.
Discussion: With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related efects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.
Digital Object Identifier (DOI)
Publication Info
Published in BMC Infectious Diseases, Volume 22, 2022.
Rights
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APA Citation
Zhang, J., Olatosi, B., Yang, X., Weissman, S., Li, Z., Hu, J., & Li, X. (2022). Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol. BMC Infectious Diseases, 22, 122. https://doi.org/10.1186/s12879-022-07047-5