# Patterns and predictors of viral suppression: A Big Data approach

> **NIH NIH R01** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2022 · $710,090

## Abstract

Abstract
Viral suppression is the final stage of the HIV treatment cascade, which serves as the framework for UNAIDS’
90-90-90 goals. Sustained viral suppression is one of four strategic areas of the “Ending the HIV Epidemic
(EtHE): A Plan for America” federal campaign, launched in February 2019, which aims for the reduction of new
HIV infections in the United States (US) by 75% and 90% by 2025 and 2030, respectively. The EtHE campaign
focuses on 48 US counties and 7 states, including South Carolina (SC). Given the importance of viral
suppression in ending the US HIV epidemic, 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. Various
indicators to characterize the longitudinal virologic outcomes have been proposed in the literature such as
sustained viral suppression, viral rebound, low-level viraemia (LLV), persistent LLV, and virologic blips.
However, some critical gaps still exist in our efforts to develop an optimal predictive model of viral suppression.
These gaps include the use of limited indicators of virologic outcomes, limited duration of follow-up, limited
data sources, lack of consideration of structural and socioenvironmental data, small or unrepresentative
samples of people living with HIV (PLWH), and limited efforts to translate research findings into service-ready
tools for clinical use. With NIH support (R01AI127203) since 2017, we have utilized a Big Data approach to
examine treatment gaps (e.g., missed opportunities for diagnosis and linkage to care) among a statewide
cohort of PLWH in SC. This ongoing research extracted longitudinal electronic health records data from six
state agencies and then linked the patient-level data with county-level data (e.g., socioeconomic indicators,
number of health care professionals, hospitals, and health care facilities) from multiple publicly available data
sources. The resultant integrated database has enabled us to successfully “track” 11,470 patients who were
diagnosed with HIV from 2005 to 2016 in SC and identify the gaps in HIV treatment linkage and retention.
Based on the experience and accomplishment of the R01AI127203, we submit this application to examine the
longitudinal dynamic pattern of viral suppression, develop optimal predictive models of various viral
suppression indicators, and translate the models to service-ready tools for clinical use. In the proposed
research, we will: 1) continue to “follow” our cohort for another five years (and also expand the cohort by
adding PLWH diagnosed between 2016-2020); 2) expand our database to include additional data on critical
predictors of viral suppression (e.g., treatment and laboratory data, alcohol and substance use data) from two
newly participating statewide data sources; 3) employ artificial intelligence (AI)-based modeling to understand
the dynamic viral load patterns and their predictors; and 4) develop and pilot...

## Key facts

- **NIH application ID:** 10425449
- **Project number:** 5R01AI164947-02
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Bankole Olatosi
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $710,090
- **Award type:** 5
- **Project period:** 2021-06-09 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10425449

## Citation

> US National Institutes of Health, RePORTER application 10425449, Patterns and predictors of viral suppression: A Big Data approach (5R01AI164947-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10425449. Licensed CC0.

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