Open AccessDevelopment and Validation of a Risk Prediction Tool to Identify People with HIV Infection Likely Not to Achieve Viral Suppression
Author(s) -
Merhawi T. Gebrezgi,
Kristopher Fennie,
Diana M. Sheehan,
Boubakari Ibrahimou,
Sandra Gracia Jones,
Petra Brock,
Robert Ladner,
Mary Jo Trepka
Publication year - 2020
Publication title -
aids patient care and stds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.504
H-Index - 85
eISSN - 1557-7449
pISSN - 1087-2914
DOI - 10.1089/apc.2019.0224
Subject(s) - medicine , logistic regression , quartile , framingham risk score , risk assessment , receiver operating characteristic , odds , confidence interval , computer science , disease , computer security
Identifying people with HIV infection (PHIV), who are at risk of not achieving viral suppression, is important for designing targeted intervention. The aim of this study was to develop and test a risk prediction tool for PHIV who are at risk of not achieving viral suppression after a year of being in care. We used retrospective data to develop an integer-based scoring method using backward stepwise logistic regression. We also developed risk score categories based on the quartiles of the total risk score. The risk prediction tool was internally validated by bootstrapping. We found that nonviral suppression after a year of being in care among PHIV can be predicted using seven variables, namely, age group, race, federal poverty level, current AIDS status, current homelessness status, problematic alcohol/drug use, and current viral suppression status. Those in the high-risk category had about a 23 increase in the odds of nonviral suppression compared with the low-risk group. The risk prediction tool has good discriminative performance and calibration. Our findings suggest that nonviral suppression after a year of being in care can be predicted using easily available variables. In settings with similar demographics, the risk prediction tool can assist health care providers in identifying high-risk individuals to target for intervention. Follow-up studies are required to externally validate this risk prediction tool.