Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol

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 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. Discussion With 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.

COVID-19 vaccine effectiveness among people living with and without HIV in South Carolina, USA: protocol of a population-based cohort study

“…Details of the SC eHARS information have been described elsewhere. 23 Briefly, the individual variables we will derive from SC eHARS include date of HIV diagnosis, sociodemographic (eg, age, race/ethnicity, risk exposure (men who have sex with men (MSM), drug user), CD4 count and viral load). This HIV cohort will be updated through at least December 2022.…”

“…In our ongoing project, around 15 years (2005–2020) of healthcare encounter data are extracted from longitudinal EHR from a variety of state agencies (figure 2). Details of the SC eHARS information have been described elsewhere 23. Briefly, the individual variables we will derive from SC eHARS include date of HIV diagnosis, sociodemographic (eg, age, race/ethnicity, risk exposure (men who have sex with men (MSM), drug user), CD4 count and viral load).…”

“…Since 1986, the South Carolina (SC) Department of Health and Environmental Control (DHEC) has been collecting the statewide EHR of HIV diagnosis, risk factors and laboratory tests via the Enhanced HIV/ AIDS Reporting System (e-HARS), a statewide confidential name-based reporting of HIV/AIDS. 21 22 Based on our ongoing project, 23 we already established a 'HIV cohort', which consists of a total of 12 203 PLWH who were diagnosed with HIV during 2005-2020 in SC. To conduct the proposed study, we will continue work with SC Revenue and Fiscal Affairs Office (RFA), who serve as the honest broker for the data linkage and integration for our ongoing project, to merge the SIMON and the S3C datasets.…”

“…Based on our ongoing project, 23 we already established a ‘HIV cohort’, which consists of a total of 12 203 PLWH who were diagnosed with HIV during 2005–2020 in SC. To conduct the proposed study, we will continue work with SC Revenue and Fiscal Affairs Office (RFA), who serve as the honest broker for the data linkage and integration for our ongoing project, to merge the SIMON and the S3C datasets.…”

Association of CD4 count and HIV viral load with risk of Non-AIDS-defining cancers

“…The de-identified EHR data from SC DHEC's eHARS and all payers' claim data were linked by SC Revenue and Fiscal Affairs Office (SC RFA). Details of data sources and data linkage are described elsewhere [41,42].…”