Division of Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA, USA.
Global Health Collaborative, Mbarara University of Science and Technology, Mbarara, Uganda.
J Int AIDS Soc. 2020 Mar;23(3):e25467. doi: 10.1002/jia2.25467.
Real-time electronic adherence monitoring (EAM) systems could inform on-going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real-time EAM (transferred via cellular signal) and standard EAM (downloaded via USB cable) in rural Uganda to inform individually differentiated viral load testing strategies by applying machine learning approaches.
We evaluated an observational cohort of persons living with HIV and treated with antiretroviral therapy (ART) who were monitored longitudinally with standard EAM from 2005 to 2011 and real-time EAM from 2011 to 2015. Super learner, an ensemble machine learning method, was used to develop a tool for targeting viral load testing to detect viraemia (>1000 copies/ml) based on clinical (CD4 count, ART regimen), viral load and demographic data, together with EAM-based adherence. Using sample-splitting (cross-validation), we evaluated area under the receiver operating characteristic curve (cvAUC), potential for EAM data to selectively defer viral load tests while minimizing delays in viraemia detection, and performance compared to WHO-recommended testing schedules.
In total, 443 persons (1801 person-years) and 485 persons (930 person-years) contributed to standard and real-time EAM analyses respectively. In the 2011 to 2015 dataset, addition of real-time EAM (cvAUC: 0.88; 95% CI: 0.83, 0.93) significantly improved prediction compared to clinical/demographic data alone (cvAUC: 0.78; 95% CI: 0.72, 0.86; p = 0.03). In the 2005 to 2011 dataset, addition of standard EAM (cvAUC: 0.77; 95% CI: 0.72, 0.81) did not significantly improve prediction compared to clinical/demographic data alone (cvAUC: 0.70; 95% CI: 0.64, 0.76; p = 0.08). A hypothetical testing strategy using real-time EAM to guide deferral of viral load tests would have reduced the number of tests by 32% while detecting 87% of viraemia cases without delay. By comparison, the WHO-recommended testing schedule would have reduced the number of tests by 69%, but resulted in delayed detection of viraemia a mean of 74 days for 84% of individuals with viraemia. Similar rules derived from standard EAM also resulted in potential testing frequency reductions.
Our machine learning approach demonstrates potential for combining EAM data with other clinical measures to develop a selective testing rule that reduces number of viral load tests ordered, while still identifying those at highest risk for viraemia.
实时电子依从性监测（EAM）系统可以为 HIV 病毒血症的持续风险评估提供信息，并可用于个性化病毒载量检测方案。我们评估了实时 EAM（通过蜂窝信号传输）和标准 EAM（通过 USB 电缆下载）在乌干达农村地区的潜在应用，通过应用机器学习方法为个性化病毒载量检测策略提供信息。
我们评估了一个观察性队列，其中包括接受抗逆转录病毒治疗（ART）的 HIV 感染者，他们在 2005 年至 2011 年期间接受标准 EAM 监测，在 2011 年至 2015 年期间接受实时 EAM 监测。使用超级学习者（一种集成机器学习方法），我们开发了一种基于临床（CD4 计数、ART 方案）、病毒载量和人口统计学数据以及基于 EAM 的依从性的工具，用于有针对性地进行病毒载量检测，以检测病毒血症（>1000 拷贝/ml）。通过样本拆分（交叉验证），我们评估了接收者操作特征曲线下的面积（cvAUC）、EAM 数据选择性推迟病毒载量检测的潜力，同时最大限度地减少病毒血症检测的延迟，以及与世界卫生组织（WHO）推荐的检测方案的比较。
共有 443 人（1801 人年）和 485 人（930 人年）分别参与了标准和实时 EAM 分析。在 2011 年至 2015 年的数据集中，与仅基于临床/人口统计学数据相比，实时 EAM 的加入（cvAUC：0.88；95%CI：0.83，0.93）显著提高了预测能力（cvAUC：0.78；95%CI：0.72，0.86；p=0.03）。在 2005 年至 2011 年的数据集中，标准 EAM 的加入（cvAUC：0.77；95%CI：0.72，0.81）与仅基于临床/人口统计学数据相比，并未显著提高预测能力（cvAUC：0.70；95%CI：0.64，0.76；p=0.08）。使用实时 EAM 来指导病毒载量检测的延迟，该检测策略假设可将检测次数减少 32%，同时不会延迟检测到 87%的病毒血症病例。相比之下，世界卫生组织推荐的检测方案将使检测次数减少 69%，但会导致 84%的病毒血症患者平均延迟 74 天检测到病毒血症。从标准 EAM 中得出的类似规则也可能导致潜在的检测频率降低。
我们的机器学习方法表明，将 EAM 数据与其他临床指标结合起来，开发一种有针对性的检测规则具有潜力，该规则可以减少所订购的病毒载量检测次数，同时仍能识别出病毒血症风险最高的人群。
