Maskew Mhairi, Parrott Shantelle, De Voux Lucien, Sharpey-Schafer Kieran, Crompton Thomas, Govender Ashley Christopher, Pisa Pedro Terrence, Rosen Sydney
Health Economics and Epidemiology Research Office, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Palindrome Data, Cape Town, South Africa.
Risk Manag Healthc Policy. 2025 May 16;18:1601-1619. doi: 10.2147/RMHP.S510666. eCollection 2025.
To reach South Africa's targets for HIV treatment and viral suppression, retention on antiretroviral therapy (ART) must increase. Here, we aim to successfully identify ART clients at risk of loss from care prior to disengagement.
We applied a previously developed machine learning and predictive modelling algorithm (PREDICT) to ART client data from SLATE I and II trials. The primary outcome was interruption in treatment (IIT), defined as missing the next scheduled clinic visit by >28 days. We tested two risk triaging approaches: 1) threshold approach classifying individuals into low, moderate, or high risk of IIT; and 2) archetype approach identifying subgroups with characteristics associated with risk of ITT. We report associations between risk category groups and subsequent IIT at the next scheduled visit using crude risk differences and relative risks with 95% confidence intervals.
SLATE datasets included 7199 client visits for 1193 clients over ≤14 months of follow-up. The threshold approach consistently and accurately assigned levels of IIT risk for multiple stages of the care cascade. The archetype approach identified several subgroups at increased risk of IIT, including those late to previous appointments, returning after a period of disengagement, living alone or without a treatment supporter. Behavioural elements of the archetypes tended to drive the risk of treatment interruption more consistently than demographics; eg adolescent boys/young men who attended visits on time experienced the lowest rates of treatment interruption (10% PREDICT datasets; 7% SLATE datasets), while adolescent boys/young men returning after previously disengaging had the highest rates of subsequent treatment interruption (31% PREDICT datasets; 40% SLATE datasets).
Routinely collected medical record data can be combined with basic demographic and socioeconomic data to assess individual risk of future treatment disengagement. This approach offers an opportunity to prevent disengagement from HIV care, rather than responding only after it has occurred.
SLATE I trial: Clinicaltrials.gov NCT02891135, registered September 1, 2016. First participant enrolled March 6, 2017, in South Africa and July 13, 2017, in Kenya. SLATE II trial: Clinicaltrials.gov NCT03315013, registered 19 October 2017. First participant enrolled 14 March 2018.
为实现南非的艾滋病病毒治疗和病毒抑制目标，必须提高抗逆转录病毒疗法（ART）的留存率。在此，我们旨在成功识别在脱离治疗前有失访风险的接受抗逆转录病毒治疗的患者。
我们将先前开发的机器学习和预测建模算法（PREDICT）应用于SLATE I和II试验的抗逆转录病毒治疗患者数据。主要结局是治疗中断（IIT），定义为错过下次预定门诊就诊超过28天。我们测试了两种风险分类方法：1）阈值法，将个体分为低、中、高IIT风险组；2）原型法，识别具有与治疗中断风险相关特征的亚组。我们使用粗略风险差异和95%置信区间的相对风险报告风险类别组与下次预定就诊时后续IIT之间的关联。
SLATE数据集包括1193名患者在≤14个月随访期间的7199次就诊。阈值法始终如一地准确为护理级联的多个阶段分配IIT风险水平。原型法识别出几个治疗中断风险增加的亚组，包括那些之前预约迟到、在一段时间脱离治疗后返回、独居或没有治疗支持者的患者。原型的行为因素往往比人口统计学因素更一致地驱动治疗中断风险；例如，按时就诊的青少年男孩/青年男性治疗中断率最低（PREDICT数据集为10%；SLATE数据集为7%），而之前脱离治疗后返回的青少年男孩/青年男性后续治疗中断率最高（PREDICT数据集为31%；SLATE数据集为40%）。
常规收集的医疗记录数据可与基本人口统计学和社会经济数据相结合，以评估个体未来治疗脱离的风险。这种方法提供了一个预防脱离艾滋病护理的机会，而不是仅在脱离发生后才做出反应。
SLATE I试验：Clinicaltrials.gov NCT02891135，2016年9月1日注册。第一名参与者于2017年3月6日在南非入组，2017年7月13日在肯尼亚入组。SLATE II试验：Clinicaltrials.gov NCT03315013，2017年10月19日注册。第一名参与者于2叭8年3月14日入组。
