Data Science for Decision Support in the HIV Care Cascade

摘要 艾滋病毒和艾滋病仍然是重大的公共卫生问题，但随着治疗方面的最新进展， 技术，临床和社会支持，研究和治疗议程现在明确， 现实地包括结束长达数十年的大流行病。总统的紧急计划 艾滋病救援计划（PEPFAR）是一项正在进行的数十亿美元的投资，为那些在 低收入和中等收入国家（LMIC），并已被许多人视为最成功的公众 现代历史上的健康干预，大大降低了流行率和发病率， 在过去的二十年里，艾滋病毒。临床试验和观察性研究都得出结论， 这表明，立即进行抗逆转录病毒治疗（ART）是最有效的方法， 既治疗艾滋病毒，又预防新感染的传播， 通过遵守ART的病毒载量可以说是最有效的方法， 艾滋病规划署95-95-95的基准， 95%的病例得到诊断; 95%的诊断病例开始并保留抗逆转录病毒治疗; 95%的 治疗个体达到病毒抑制。 临床决策支持系统（CDSS）根据LMIC的要求量身定制， 提高一系列医护人员对指南的依从性和护理质量。使用机器 学习算法允许开发用于临床并发症的预测模型， 结果，可以指导医护人员及早识别问题， 干预措施。因此，该建议的具体目标是（1）使用大型电子健康 EHR数据库，用于开发和验证统计机器学习模型，以识别患者 失访和病毒失败的高风险;（2）开发和现场测试实施临床 基于这些模型的决策支持工具，将在护理点实施;以及（3） 评估决策支持工具在改善患者保留和减少 病毒治疗失败，使用临床水平的随机比较。我们的项目将在 提供医疗保健机会的学术模式（AMPATH），肯尼亚西部的一个艾滋病毒护理项目 为近20万艾滋病毒感染者提供服务。

Abstract HIV and AIDS continue to be significant public health issues, but with recent advances in treatment, technology, clinical and social support, the research and treatment agenda now explicitly and realistically includes bringing the decades-long pandemic to an end. The President’s Emergency Plan for AIDS Relief (PEPFAR) is an ongoing multi-billion investment to deliver antiviral therapy to those in low- and middle-income countries (LMIC), and has been regarded by many as the most successful public health intervention in modern history, having dramatically reduced both prevalence and incidence of HIV over the past two decades. With both clinical trials and observational studies conclusively demonstrating that immediate treatment with antiretroviral therapy (ART) is the mosteffective way to both treat HIV and prevent the transmission of new infections, retention in HIV care and suppression of viral load through compliance with ART are arguably the most effective methods available for bringing the pandemic to an end, and indeed are encoded in the UNAIDS 95-95-95 benchmarks of having 95% of cases diagnosed; 95% of diagnosed cases initiated and retained on ART; and 95% of treated individuals achieving viral suppression. Clinical decision support systems (CDSS) tailored to the requirements of LMICs have been shown to improve compliance with guidelines and quality of care by a range of healthcare staff. Use of machine learning algorithms allows the development of prediction models for clinical complications and outcomes, which can guide health care staff in early identification of problems and appropriate interventions. The Specific Aims of this proposal therefore are (1) to use a large electronic health record (EHR) database to develop and validate statistical machine learning models to identify patient at high risk for loss to follow up and viral failure; (2) to develop and field test implementation of clinical decision support tools based on these models that will be implemented at the point of care; and (3) to evaluate the efficacy of the decision support tools, in terms of improving patient retention and reducing viral failure, using a randomized comparison at the clinic level. Our project will be implemented at the Academic Model Providing Access to Healthcare (AMPATH), an HIV care program in western Kenya serving nearly 200,000 people with HIV.