An Adaptive Strategy for Preventing and Treating Lapses of Retention in Adult HIV Care II (ADAPT-R II)

预防和治疗成人 HIV 护理中保留失效的适应性策略 II (ADAPT-R II)

• 批准号: 10017320
• 负责人: Elvin H. Geng
• 金额: $ 25.1万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-12 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10017320
• 关键词: Address; Adherence; Adult; Africa; Algorithms; Behavior; Caring; Characteristics; Clinical; Clinical Data; Complex; Computer software; Computerized Medical Record; Conflict (Psychology); Counseling; Data; Data Collection; Data Element; Data Science; Effectiveness; Ensure; Environment; Ethics; Face; Family; Focus Groups; Foundations; Funding; Future; HIV; Health Personnel; Health behavior; Health system; Heterogeneity; Individual; Information Systems; Information Technology; Intervention; Interview; Investments; Kenya; Laboratories; Learning; Life; Machine Learning; Measures; Mental Depression; Minority; Nature; Organizational Policy; Outcome; Outcome Study; Patients; Policies; Predictive Analytics; Privacy; Problem behavior; Procedures; Process; Public Health; Publications; Randomized; Recommendation; Reporting; Research; Running; Savings; Sequential Multiple Assignment Randomized Trial; Shapes; Source; Speed; Subgroup; System; Techniques; Testing; Text Messaging; Time; Transportation; Trust; United States National Institutes of Health; Viral; Work; adaptive intervention; antiretroviral therapy; base; clinical care; data quality; effectiveness testing; evidence base; experience; health record; improved outcome; innovation; learning ability; machine learning algorithm; men; new technology; novel; outreach; overtreatment; prediction algorithm; preference; prevent; randomized trial; response; retention rate; service delivery; simulation; social stigma; sociodemographics; standard of care; success; therapy design; trial design

Abstract This R34 application is best understood in the context of both a NIH-funded sequential multiple assignment randomized trial (SMART) “Adaptive Strategies to Prevent and Treat Lapses of Retention (ADAPT-1)” nearing completion and a future trial (ADAPT-3) motivated by observations from the ADAPT-1. Retention in HIV treatment over long periods of time represents an archetypal complex public health problem and requires innovative solutions. The diversity of intensities and types of barriers to engagement mean that no single intervention is needed by all nor will work for all in need. For example, counseling could help a patient experiencing stigma, but will not help an individual who wants to come but cannot afford transportation. To respond to this conundrum, we carried out a SMART (ADAPT-1) to test a family of adaptive retention strategies. By maintaining lower intensity interventions in those doing well, adaptive strategies optimize efficiency, while escalating in those not doing well enhances effectiveness. In ADAPT-1, we initially randomized patients to one of three lower intensity interventions (standard of care (SOC), SMS messages and a conditional cash transfer). Only those who fail to be consistently retained are re-randomized to one of three more intensive interventions (SOC outreach, SMS message with a conditional cash transfer, or a navigator). Emerging ADAPT-1 results (in forthcoming publications) confirm our original hypothesis that pegging the retention intervention to patient behavior improves outcomes, the study also revealed additional opportunities to extend a “precision public health” paradigm. Specifically, we observed that different patients (based on sociodemographic, clinical and laboratory characteristics) respond differentially to different adaptive retention strategies. This observation begs a further hypothesis: use of predictive analytics (optimized with cutting-edge machine learning techniques) to distribute each intervention (e.g., SOC, cash transfer, SMS) to those patients most likely to respond to that intervention can achieve further gains in effectiveness and efficiency over any single sequenced retention strategy, even if strategy is itself already adaptive. We plan a future R01 application to test a machine learning based distribution of retention interventions as compared to best single sequential adaptive interventions (from ADAPT-1). To prepare for the novel trial, we propose this R34, to (1) develop and test the information technology basis for delivering on-demand predictions to health care workers in the field, (2) refine the statistical foundations of machine learning ability to predict through simulations and (3) assess the fit of machine learning based recommendations in the organizational, policy and ethical context of health systems in Kenya.

摘要 该R34应用程序在NIH资助的连续多个应用程序的上下文中最容易理解 分配随机试验(SMART)：预防和治疗失误的适应性策略 保留(适应-1)“接近完成和基于观察结果的未来试验(适应-3) 从Adapter-1。在艾滋病毒治疗中长期留存是一种典型 这是一个复杂的公共卫生问题，需要创新的解决方案。强度和强度的多样性 接触障碍的类型意味着，所有人都不需要单一的干预，也不会 所有人都需要帮助。例如，咨询可以帮助经历耻辱的患者，但无济于事 想来但付不起交通费的人。为了回应这个难题， 我们进行了SMART(Adapt-1)测试，以测试一系列适应性保留策略。通过 在表现良好的人中保持较低的干预强度，适应性策略得到优化 效率，而在那些做得不好的人身上升级会提高效率。在Adapt-1中，我们 最初将患者随机分为三种较低强度干预之一(护理标准(SOC)， 短信和有条件的现金转账)。只有那些未能始终如一地留住的人 被重新随机分配到三种更密集的干预中(SOC外展、短信 使用有条件的现金转移或导航器)。正在出现的Adapt-1结果(即将发布 出版物)证实了我们最初的假设，即将保留干预与患者挂钩 行为可以改善结果，这项研究还揭示了延长 “精准公共卫生”范式。具体来说，我们观察到不同的患者(基于 社会人口学、临床和实验室特征)对不同 自适应保留策略。这一观察结果提出了一个进一步的假设：使用预测性 分析(使用尖端的机器学习技术进行优化)，以分发每个 对那些最有可能对此有反应的患者进行干预(例如，SOC、现金转移、短信) 与任何单一序列相比，干预可以进一步提高有效性和效率 保留战略，即使战略本身已经是适应性的。我们计划未来的R01应用程序 测试基于机器学习的保留干预分布与最佳单一保留干预的比较 循序渐进的适应性干预(来自Adapt-1)。为了为这场新颖的审判做准备，我们提出如下建议 R34，(1)开发和测试提供按需预测的信息技术基础 对现场医护人员来说，(2)细化了机器学习能力的统计基础 通过模拟进行预测，以及(3)评估基于机器学习的推荐的适合性 在肯尼亚卫生系统的组织、政策和道德方面。