Am I Positive? Improving Human Immunodeficiency Virus Testing in the Era of Preexposure Prophylaxis and Immediate Antiretroviral Therapy Using Machine Learning. (18th May 2022)

• Record Type: Journal Article
• Authors: Zucker, Jason; Carnevale, Caroline; Gordon, Peter; Sobieszczyk, Magdalena E; Rai, Alex J
• Is Part Of: Open forum infectious diseases. Volume 9:Number 7(2022)
• Publication Date: 2022-05-18
• Subjects: HIV prevention -- HIV testing -- immediate ART -- machine learning -- preexposure prophylaxis; Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofac259
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Human immunodeficiency virus (HIV) testing is the first step in the HIV prevention cascade. The Centers for Disease Control and Prevention HIV laboratory diagnostic testing algorithm was developed before preexposure prophylaxis (PrEP) and immediate antiretroviral therapy (iART) became standards of care. PrEP and iART have been shown to delay antibody development and affect the performance of screening HIV assays. Quantitative results from fourth-generation HIV testing may be helpful to disambiguate HIV testing. Methods: We retrospectively reviewed 38 850 results obtained at an urban, academic medical center. We assessed signal-to-cutoff (s/co) distribution among positive and negative tests, in patients engaged and not engaged in an HIV prevention program, and evaluated changes in patients with multiple results. Classification and regression tree (CART) analysis was used to determine a threshold cutoff, and logistic regression was used to identify predictors of true positive tests. Results: Ninety-seven percent of patients with a negative HIV test had a result that was ≤0.2 s/co. For patients tested more than once, we found differences in s/co values did not exceed 0.2 s/co for 99.2% of results. CART identified an s/co value, 38.78, that in logistic regression on a unique validation cohort remained associated with the likelihood of a true-positive HIV result (odds ratio, 2.49). Conclusions: Machine-learning methods may be used to improve HIV screening by automating and improving interpretations, incorporating them into robust algorithms, and improving disease prediction. Further investigation is warranted to confirm if s/co values combined with a patient's risk profile will allow for better clinical decision making for individuals on PrEP or eligible for iART.