Centre for the AIDS Programme of Research in South Africa-CAPRISA, University of KwaZulu-Natal, Durban, South Africa.
PLoS One. 2013 Apr 30;8(4):e62928. doi: 10.1371/journal.pone.0062928. Print 2013.
Prompt diagnosis of acute HIV infection (AHI) benefits the individual and provides opportunities for public health intervention. The aim of this study was to describe most common signs and symptoms of AHI, correlate these with early disease progression and develop a clinical algorithm to identify acute HIV cases in resource limited setting.
245 South African women at high-risk of HIV-1 were assessed for AHI and received monthly HIV-1 antibody and RNA testing. Signs and symptoms at first HIV-positive visit were compared to HIV-negative visits. Logistic regression identified clinical predictors of AHI. A model-based score was assigned to each predictor to create a risk score for every woman.
Twenty-eight women seroconverted after a total of 390 person-years of follow-up with an HIV incidence of 7.2/100 person-years (95%CI 4.5-9.8). Fifty-seven percent reported ≥1 sign or symptom at the AHI visit. Factors predictive of AHI included age <25 years (OR = 3.2; 1.4-7.1), rash (OR = 6.1; 2.4-15.4), sore throat (OR = 2.7; 1.0-7.6), weight loss (OR = 4.4; 1.5-13.4), genital ulcers (OR = 8.0; 1.6-39.5) and vaginal discharge (OR = 5.4; 1.6-18.4). A risk score of 2 correctly predicted AHI in 50.0% of cases. The number of signs and symptoms correlated with higher HIV-1 RNA at diagnosis (r = 0.63; p<0.001).
Accurate recognition of signs and symptoms of AHI is critical for early diagnosis of HIV infection. Our algorithm may assist in risk-stratifying individuals for AHI, especially in resource-limited settings where there is no routine testing for AHI. Independent validation of the algorithm on another cohort is needed to assess its utility further. Point-of-care antigen or viral load technology is required, however, to detect asymptomatic, antibody negative cases enabling early interventions and prevention of transmission.
急性 HIV 感染（AHI）的及时诊断有利于个体，并为公共卫生干预提供机会。本研究旨在描述 AHI 最常见的症状和体征，将其与早期疾病进展相关联，并在资源有限的环境下开发一种用于识别急性 HIV 病例的临床算法。
对 245 名南非高危 HIV-1 感染的女性进行 AHI 评估，并接受每月 HIV-1 抗体和 RNA 检测。比较首次 HIV 阳性就诊时的症状和体征与 HIV 阴性就诊时的症状和体征。采用逻辑回归法确定 AHI 的临床预测因素。为每个预测因素分配一个基于模型的评分，为每位女性创建一个风险评分。
在总共 390 人年的随访中，有 28 名女性发生血清转换，HIV 发病率为 7.2/100 人年（95%CI 4.5-9.8）。57%的女性在 AHI 就诊时报告了≥1 种症状或体征。预测 AHI 的因素包括年龄<25 岁（OR=3.2；1.4-7.1）、皮疹（OR=6.1；2.4-15.4）、咽痛（OR=2.7；1.0-7.6）、体重减轻（OR=4.4；1.5-13.4）、生殖器溃疡（OR=8.0；1.6-39.5）和阴道分泌物（OR=5.4；1.6-18.4）。风险评分为 2 分可正确预测 50.0%的 AHI 病例。症状和体征的数量与诊断时 HIV-1 RNA 水平较高相关（r=0.63；p<0.001）。
准确识别 AHI 的症状和体征对于 HIV 感染的早期诊断至关重要。我们的算法可能有助于对 AHI 患者进行风险分层，尤其是在没有常规 AHI 检测的资源有限的环境中。需要在另一个队列中对该算法进行独立验证，以进一步评估其效用。然而，需要即时检测抗原或病毒载量技术，以发现无症状、抗体阴性的病例，从而进行早期干预和预防传播。
