Esra Rachel T, Carstens Jacques, Estill Janne, Stoch Ricky, Le Roux Sue, Mabuto Tonderai, Eisenstein Michael, Keiser Olivia, Maskew Mhari, Fox Matthew P, De Voux Lucien, Sharpey-Schafer Kieran
Institute of Global Health, University of Geneva, Geneva, Switzerland.
Imperial College of London, London, United Kingdom.
PLOS Glob Public Health. 2023 Jul 19;3(7):e0002105. doi: 10.1371/journal.pgph.0002105. eCollection 2023.
Retention of antiretroviral (ART) patients is a priority for achieving HIV epidemic control in South Africa. While machine-learning methods are being increasingly utilised to identify high risk populations for suboptimal HIV service utilisation, they are limited in terms of explaining relationships between predictors. To further understand these relationships, we implemented machine learning methods optimised for predictive power and traditional statistical methods. We used routinely collected electronic medical record (EMR) data to evaluate longitudinal predictors of lost-to-follow up (LTFU) and temporal interruptions in treatment (IIT) in the first two years of treatment for ART patients in the Gauteng and North West provinces of South Africa. Of the 191,162 ART patients and 1,833,248 visits analysed, 49% experienced at least one IIT and 85% of those returned for a subsequent clinical visit. Patients iteratively transition in and out of treatment indicating that ART retention in South Africa is likely underestimated. Historical visit attendance is shown to be predictive of IIT using machine learning, log binomial regression and survival analyses. Using a previously developed categorical boosting (CatBoost) algorithm, we demonstrate that historical visit attendance alone is able to predict almost half of next missed visits. With the addition of baseline demographic and clinical features, this model is able to predict up to 60% of next missed ART visits with a sensitivity of 61.9% (95% CI: 61.5-62.3%), specificity of 66.5% (95% CI: 66.4-66.7%), and positive predictive value of 19.7% (95% CI: 19.5-19.9%). While the full usage of this model is relevant for settings where infrastructure exists to extract EMR data and run computations in real-time, historical visits attendance alone can be used to identify those at risk of disengaging from HIV care in the absence of other behavioural or observable risk factors.
对抗逆转录病毒治疗（ART）患者的留存是南非实现艾滋病疫情控制的一项优先任务。虽然机器学习方法越来越多地被用于识别艾滋病服务利用欠佳的高风险人群，但在解释预测因素之间的关系方面存在局限性。为了进一步理解这些关系，我们实施了针对预测能力进行优化的机器学习方法和传统统计方法。我们使用常规收集的电子病历（EMR）数据，评估了南非豪登省和西北省接受抗逆转录病毒治疗的患者在治疗的头两年中失访（LTFU）和治疗期间暂时中断（IIT）的纵向预测因素。在分析的191,162名接受抗逆转录病毒治疗的患者和1,833,248次就诊中，49%的患者经历了至少一次治疗期间暂时中断，其中85%的患者随后返回进行了后续临床就诊。患者反复进出治疗，这表明南非的抗逆转录病毒治疗留存率可能被低估了。使用机器学习、对数二项回归和生存分析表明，既往就诊情况可预测治疗期间暂时中断。使用先前开发的分类增强（CatBoost）算法，我们证明仅既往就诊情况就能预测近一半的下次漏诊就诊。加上基线人口统计学和临床特征后，该模型能够预测高达60%的下次漏诊抗逆转录病毒治疗就诊，敏感性为61.9%（95%CI：61.5 - 62.3%），特异性为66.5%（95%CI：66.4 - 66.7%），阳性预测值为19.7%（95%CI：19.5 - 19.9%）。虽然该模型的全面应用适用于存在提取电子病历数据并实时运行计算基础设施的环境，但在没有其他行为或可观察到的风险因素的情况下，仅既往就诊情况就可用于识别那些有脱离艾滋病护理风险的人群。
