Evans Denise H, Fox Matthew P, Maskew Mhairi, McNamara Lynne, MacPhail Patrick, Mathews Christopher, Sanne Ian
Health Economics and Epidemiology Research Unit, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa;
Health Economics and Epidemiology Research Unit, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Boston University, Center for Global Health & Development, Boston, MA, USA.
J Int AIDS Soc. 2014 Sep 15;17(1):19139. doi: 10.7448/IAS.17.1.19139. eCollection 2014.
Several studies from resource-limited settings have demonstrated that clinical and immunologic criteria are poor predictors of virologic failure, confirming the need for viral load monitoring or at least an algorithm to target viral load testing. We used data from an electronic patient management system to develop an algorithm to identify patients at risk of viral failure using a combination of accessible and inexpensive markers.
We analyzed data from HIV-positive adults initiated on antiretroviral therapy (ART) in Johannesburg, South Africa, between April 2004 and February 2010. Viral failure was defined as ≥ 2 consecutive HIV-RNA viral loads >400 copies/ml following suppression ≤ 400 copies/ml. We used Cox-proportional hazards models to calculate hazard ratios (HR) and 95% confidence intervals (CI). Weights for each predictor associated with virologic failure were created as the sum of the natural logarithm of the adjusted HR and dichotomized with the optimal cut-off at the point with the highest sensitivity and specificity (i.e. ≤ 4 vs. >4). We assessed the diagnostic accuracy of predictor scores cut-offs, with and without CD4 criteria (CD4 <100 cells/mm(3); CD4 < baseline; >30% drop in CD4), by calculating the proportion with the outcome and the observed sensitivity, specificity, positive and negative predictive value of the predictor score compared to the gold standard of virologic failure.
We matched 919 patients with virologic failure (1:3) to 2756 patients without. Our predictor score included variables at ART initiation (i.e. gender, age, CD4 count <100 cells/mm(3), WHO stage III/IV and albumin) and laboratory and clinical follow-up data (drop in haemoglobin, mean cell volume (MCV) <100 fl, CD4 count <200 cells/mm(3), new or recurrent WHO stage III/IV condition, diagnosis of new condition or symptom and regimen change). Overall, 51.4% had a score 51.4% had a score ≥ 4 and 48.6% had a score <4. A predictor score including CD4 criteria performed better than a score without CD4 criteria and better than WHO clinico-immunological criteria or WHO clinical staging to predict virologic failure (sensitivity 57.1% vs. 40.9%, 25.2% and 20.9%, respectively).
Predictor scores or risk categories, with CD4 criteria, could be used to identify patients at risk of virologic failure in resource-limited settings so that these patients may be targeted for focused interventions to improve HIV treatment outcomes.
来自资源有限地区的多项研究表明,临床和免疫学标准并不能很好地预测病毒学失败,这证实了需要进行病毒载量监测或至少采用一种算法来确定病毒载量检测的对象。我们利用电子患者管理系统中的数据开发了一种算法,通过结合可获取且成本低廉的指标来识别有病毒学失败风险的患者。
我们分析了2004年4月至2010年2月期间在南非约翰内斯堡开始接受抗逆转录病毒治疗(ART)的HIV阳性成年人的数据。病毒学失败定义为在病毒载量抑制至≤400拷贝/毫升后,连续≥2次HIV-RNA病毒载量>400拷贝/毫升。我们使用Cox比例风险模型计算风险比(HR)和95%置信区间(CI)。将与病毒学失败相关的每个预测指标的权重设定为调整后HR的自然对数之和,并以敏感度和特异度最高的点(即≤4与>4)的最佳临界值进行二分法划分。我们通过计算有该结果的比例以及预测指标得分与病毒学失败金标准相比的观察到的敏感度、特异度、阳性和阴性预测值,评估了有无CD4标准(CD4<100个细胞/立方毫米;CD4<基线水平;CD4下降>30%)时预测指标得分临界值的诊断准确性。
我们将919例病毒学失败患者(1:3)与2756例未出现病毒学失败的患者进行匹配。我们的预测指标得分包括ART开始时的变量(即性别、年龄、CD4计数<100个细胞/立方毫米、世界卫生组织(WHO)III/IV期和白蛋白)以及实验室和临床随访数据(血红蛋白下降、平均红细胞体积(MCV)<100飞升、CD4计数<200个细胞/立方毫米、新出现或复发的WHO III/IV期病情、新病情或症状的诊断以及治疗方案改变)。总体而言,51.4%的患者得分≥4,48.6%的患者得分<4。包含CD4标准的预测指标得分在预测病毒学失败方面比不包含CD4标准的得分表现更好,也比WHO临床免疫学标准或WHO临床分期表现更好(敏感度分别为57.1%、40.9%、25.2%和20.9%)。
包含CD4标准的预测指标得分或风险类别可用于在资源有限地区识别有病毒学失败风险的患者,以便针对这些患者进行有针对性的干预,从而改善HIV治疗效果。