Pharmacy refill adherence outperforms self-reported methods in predicting HIV therapy outcome in resource-limited settings.
Pharmacy refill adherence outperforms self-reported methods in predicting HIV therapy outcome in resource-limited settings.
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DOI：
10.1186/1471-2458-14-1035
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发表时间：
2014-10-04
影响因子：
4.5
通讯作者：
Vandamme AM
中科院分区：
文献类型：
--
作者：
Sangeda RZ;Mosha F;Prosperi M;Aboud S;Vercauteren J;Camacho RJ;Lyamuya EF;Van Wijngaerden E;Vandamme AM
最大限度地坚持抗逆转录病毒治疗对于预防艾滋病毒耐药(HIVDR)流行至关重要。这项研究的目的是探讨在资源有限的情况下预测病毒学失败的最佳执行依从性评估方法。这项研究是一项单中心前瞻性队列研究，于2010年在坦桑尼亚达累斯萨拉姆的一家艾滋病毒/艾滋病护理和治疗中心招募了220名艾滋病毒感染的成年患者。进行了药房续药、自我报告(通过视觉模拟量表[VAS]和瑞士艾滋病毒队列研究-依从性问卷)、药片计数和预约依从性测量。单变量Logistic回归(LR)被用来探索在敏感性和特异性之间提供更好折衷的临界值，以及基于接收者操作特征曲线的较高的曲线下面积(AUC)来预测病毒学失败。此外，通过将多变量LR与逐步函数、决策树和随机森林模型进行拟合，评估10倍多重交叉验证(MCV)，对依从性模型进行评估。使用LR确定与病毒学失败相关的患者因素。162名患者在基线和招募后一年进行了病毒载量测量，其中55人(34%)在招募后一年可检测到病毒载量，17人(10.5%)在招募后一年免疫失败。显著预测病毒学失败的最佳分界点分别为VAS、预约、药房补药和服药依从性的95%、80%、95%和90%。这些方法的AUC范围为0.52到0.61，其中药房充填的AUC为0.61时表现最好。多因素Logistic回归分析显示，除药物再充填依从性单变量模型与多因素模型(AUC = 0.64)相当外，其余各单变量依从性模型的AUC(0.64)均高于所有单变量依从性模型。决策树和随机森林模型不如Boost逐步模型。药房再灌装依从性(<；95%)成为预测病毒学失败的最佳方法。在多变量LR中，其他有意义的预测因素包括基线CD4T淋巴细胞计数 <； 200Cells/μL，无法回忆起诊断日期，以及体重较高。药房再灌装有可能预测病毒学失败，并确定需要考虑在RLS中进行病毒载量监测和HIVDR检测的患者。本文的在线版本(DOI：10.1186/1471-2458-14-1035)包含补充材料，可供授权用户使用。
Optimal adherence to antiretroviral therapy is critical to prevent HIV drug resistance (HIVDR) epidemic. The objective of the study was to investigate the best performing adherence assessment method for predicting virological failure in resource-limited settings (RLS). This study was a single-centre prospective cohort, enrolling 220 HIV-infected adult patients attending an HIV/AIDS Care and Treatment Centre in Dar es Salaam, Tanzania, in 2010. Pharmacy refill, self-report (via visual analog scale [VAS] and the Swiss HIV Cohort study-adherence questionnaire), pill count, and appointment keeping adherence measurements were taken. Univariate logistic regression (LR) was done to explore a cut-off that gives a better trade-off between sensitivity and specificity, and a higher area under the curve (AUC) based on receiver operating characteristic curve in predicting virological failure. Additionally, the adherence models were evaluated by fitting multivariate LR with stepwise functions, decision trees, and random forests models, assessing 10-fold multiple cross validation (MCV). Patient factors associated with virological failure were determined using LR. Viral load measurements at baseline and one year after recruitment were available for 162 patients, of whom 55 (34%) had detectable viral load and 17 (10.5%) had immunological failure at one year after recruitment. The optimal cut-off points significantly predictive of virological failure were 95%, 80%, 95% and 90% for VAS, appointment keeping, pharmacy refill, and pill count adherence respectively. The AUC for these methods ranged from 0.52 to 0.61, with pharmacy refill giving the best performance at AUC 0.61. Multivariate logistic regression with boost stepwise MCV had higher AUC (0.64) compared to all univariate adherence models, except pharmacy refill adherence univariate model, which was comparable to the multivariate model (AUC = 0.64). Decision trees and random forests models were inferior to boost stepwise model. Pharmacy refill adherence (<95%) emerged as the best method for predicting virological failure. Other significant predictors in multivariate LR were having a baseline CD4 T lymphocytes count < 200 cells/μl, being unable to recall the diagnosis date, and a higher weight. Pharmacy refill has the potential to predict virological failure and to identify patients to be considered for viral load monitoring and HIVDR testing in RLS. The online version of this article (doi:10.1186/1471-2458-14-1035) contains supplementary material, which is available to authorized users.
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影响因子：
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作者：
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影响因子：
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