Like many chronic diseases, human immunodeficiency virus (HIV) is managed over time at regular clinic visits. At each visit, patient features are assessed, treatments are prescribed, and a subsequent visit is scheduled. There is a need for data-driven methods for both predicting retention and recommending scheduling decisions that optimize retention. Prediction models can be useful for estimating retention rates across a range of scheduling options. However, training such models with electronic health records (EHR) involves several complexities. First, formal causal inference methods are needed to adjust for observed confounding when estimating retention rates under counterfactual scheduling decisions. Second, competing events such as death preclude retention, while censoring events render retention missing. Third, inconsistent monitoring of features such as viral load and CD4 count lead to covariate missingness. This paper presents an all-in-one approach for both predicting HIV retention and optimizing scheduling while accounting for these complexities. We formulate and identify causal retention estimands in terms of potential return-time under a hypothetical scheduling decision. Flexible Bayesian approaches are used to model the observed return-time distribution while accounting for competing and censoring events and form posterior point and uncertainty estimates for these estimands. We address the urgent need for data-driven decision support in HIV care by applying our method to EHR from the Academic Model Providing Access to Healthcare (AMPATH) - a consortium of clinics that treat HIV in Western Kenya.

翻译：与许多慢性疾病类似，人类免疫缺陷病毒（HIV）需要通过定期门诊就诊进行长期管理。在每次就诊时，评估患者特征、制定治疗方案并安排后续随访。当前亟需数据驱动的方法来预测患者保留率，并优化随访决策以提升保留效果。预测模型可用于评估不同随访方案下的保留率。然而，利用电子健康记录（EHR）训练此类模型面临多重复杂性：首先，在估计反事实随访决策下的保留率时，需采用正式因果推断方法校正已观测的混杂因素；其次，死亡等竞争事件会导致患者失访，而删失事件则造成保留数据缺失；再者，病毒载量和CD4计数等特征监测的不连续性导致协变量信息缺失。本文提出一种一体化方法，在应对上述复杂性的同时实现HIV保留率预测与随访方案优化。我们通过假设随访决策下的潜在返回时间，构建并识别因果保留估计量。采用灵活的贝叶斯方法对观测返回时间分布进行建模，同时处理竞争事件与删失事件，并形成这些估计量的后验点估计与不确定性估计。通过将本方法应用于来自"学术医疗可及性模型"（AMPATH）——肯尼亚西部HIV治疗诊所联盟的电子健康记录，我们回应了HIV护理领域对数据驱动决策支持的迫切需求。