Abstract
BackgroundGiven 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.MethodsThe PLWH cohort will be identified through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Office of Revenue and Fiscal Affairs (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 deidentified 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 artificial 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 identification of the risk of viral failure or viral rebound when patients present at clinical visits.DiscussionWith both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects 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.
Highlights
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 a prolonged life expectancy of people living with HIV (PLWH), routine monitoring of viral load (VL) status becomes more important over their life course, with the longitudinal VL information collected over time potentially adding to the predictability of subsequent virologic failure (VF) or mortality
Using a data science approach, this study aims to examine the longitudinal dynamic pattern of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to serviceready tools for clinical support and decision-making
Summary
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. Over the past few years, a small but increasing number of longitudinal studies have explored the dynamics of VL patterns using sustained viral suppression, viral rebound, viral blips, or low-level viremia (LLV) [6,7,8]. These different VL measures are interrelated, affect each other, and predict, to some extent, virologic failure [9]. Some studies suggest a threshold of > 200 copies/ ml as being associated with VF; yet other studies suggest a higher threshold (i.e., VL > 400 copies/ml) [16, 17]
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