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: 10239002
• 负责人: Elvin H. Geng
• 金额: $ 21.6万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10239002
• 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.

抽象的 在NIH资助的顺序倍数的背景下，最好理解此R34应用程序 分配随机试验（智能）“预防和治疗失误的自适应策略 保留（Aftapt-1）”接近完成和未来的试验（Aftapt-3），该试验是由观察到的 来自Adapt-1。长期保留艾滋病毒治疗代表原型 复杂的公共卫生问题，需要创新的解决方案。强度的多样性和 参与障碍的类型意味着所有人都不需要一项干预措施 所有需要。例如，咨询可以帮助患者遭受污名，但不会帮助 一个想来但负担不起运输的个人。为了应对这个难题， 我们进行了智能（Adapt-1），以测试一个自适应保留策略的家庭。经过 保持较低的强度干预措施在表现良好的人中，自适应策略优化 有效性，同时在不良好的人中升级会增强有效性。在Apapt-1中，我们 最初将随机患者用于三种较低强度干预措施之一（SOC标准（SOC）， SMS消息和有条件的现金转移）。只有那些未能持续保留的人 被重新融合到另外三项密集干预措施之一（SOC Outreach，SMS消息 带有有条件的现金转移或导航员）。新兴的Adapt-1结果（即将到来 出版物）证实了我们最初的假设，即将保留干预固定在患者身上 行为改善了结果，该研究还揭示了更多的机会扩展 “精密公共卫生”范式。具体而言，我们观察到不同的患者（基于 社会人口统计学，临床和实验室特征）对不同的反应不同 自适应保留策略。该观察结果提出了进一步的假设：使用预测 分析（使用尖端机器学习技术优化）以分发每种 干预（例如SOC，现金转移，SMS）向最有可能对此做出反应的患者 干预措施可以在任何单个测序中取得进一步提高的有效性和效率 保留策略，即使策略本身已经自适应。我们计划未来的R01申请 与最佳单一相比，测试基于机器学习的保留干预措施的分布 顺序自适应干预措施（来自自适应1）。为了为新颖的审判做准备，我们建议 R34，（1）开发和测试提供按需预测的信息技术基础 （2）完善机器学习能力的统计基础 通过模拟和（3）评估基于机器学习建议的拟合 在肯尼亚卫生系统的组织，政策和道德背景下。