Zucker Jason, Carnevale Caroline, Gordon Peter, Sobieszczyk Magdalena E, Rai Alex J
Department of Internal Medicine, Division of Infectious Diseases, Columbia University Irving Medical Center, New York, New York, USA.
Comprehensive Health Center HIV Prevention Program, New York Presbyterian Hospital, New York, New York, USA.
Open Forum Infect Dis. 2022 May 18;9(7):ofac259. doi: 10.1093/ofid/ofac259. eCollection 2022 Jul.
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.
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.
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).
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.
人类免疫缺陷病毒（HIV）检测是HIV预防流程的第一步。美国疾病控制与预防中心的HIV实验室诊断检测算法是在暴露前预防（PrEP）和即时抗逆转录病毒疗法（iART）成为标准治疗方法之前制定的。PrEP和iART已被证明会延迟抗体产生并影响HIV筛查检测的性能。第四代HIV检测的定量结果可能有助于消除HIV检测的歧义。
我们回顾性分析了在一家城市学术医疗中心获得的38850份检测结果。我们评估了阳性和阴性检测中信号与临界值（s/co）的分布情况，这些检测来自参与和未参与HIV预防项目的患者，并评估了多次检测患者的结果变化。使用分类与回归树（CART）分析来确定临界值，并使用逻辑回归来识别真阳性检测的预测因素。
97%的HIV检测阴性患者的结果≤0.2 s/co。对于多次检测的患者，我们发现99.2%的结果中s/co值差异不超过0.2 s/co。CART确定了一个s/co值为38.78，在一个独特的验证队列中进行逻辑回归分析时，该值仍与HIV真阳性结果的可能性相关（优势比为2.49）。
机器学习方法可用于通过自动化和改进解释、将其纳入强大的算法以及改善疾病预测来提高HIV筛查水平。有必要进一步研究以确认s/co值与患者风险概况相结合是否能为接受PrEP或符合iART条件的个体提供更好的临床决策依据。
