Data Science for Decision Support in the HIV Care Cascade

HIV 护理级联中决策支持的数据科学

• 批准号: 10516747
• 负责人: Hamish SF Fraser
• 金额: $ 69.07万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2026-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10516747
• 关键词: Acquired Immunodeficiency Syndrome; Address; Adult; Aftercare; Algorithms; Antiviral Therapy; Architecture; Benchmarking; Caring; Case-Mix Adjustments; Client; Clinic; Clinical; Clinical Data; Clinical Decision Support Systems; Clinical Trials; Communication; Country; Data; Data Science; Data Set; Data Sources; Databases; Decision Making; Development; Diagnosis; Drug resistance; Early identification; Electronic Health Record; Emergency Situation; Environment; Failure; Fast Healthcare Interoperability Resources; Feedback; Geographic Locations; Guidelines; HIV; HIV/AIDS; Health; Health care facility; Health system; Healthcare; Impact evaluation; Incidence; Individual; Infection; Intervention; Investments; Kenya; Learning; Measures; Methods; Modeling; Modern 1601-history; Motivation; Observational Study; Outcome; Output; Patients; Persons; Phase; Physicians; Predictive Value; Prevalence; Process; Public Health; Quality of Care; Randomized; Research; Research Personnel; Schedule; Social support; Specific qualifier value; System; Technology; Testing; Time; Uncertainty; Update; Viral; Viral Load result; Viral load measurement; Visualization software; Work; antiretroviral therapy; care outcomes; clinical decision support; clinical implementation; clinical predictive model; data visualization; design; efficacy evaluation; electronic health data; electronic health record system; field study; follow-up; health care availability; high risk; improved; low and middle-income countries; machine learning algorithm; machine learning model; pandemic disease; patient engagement; patient retention; point of care; predictive modeling; prevent; programs; public health intervention; research clinical testing; response; retention rate; statistical and machine learning; support tools; tool; tool development; transmission process

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.

抽象的 艾滋病毒和艾滋病仍然是重大的公共卫生问题，但随着治疗的最新进展， 技术、临床和社会支持、研究和治疗议程现在已明确并 实际上包括结束长达数十年的大流行。总统的紧急计划 艾滋病救援 (PEPFAR) 是一项持续数十亿美元的投资，旨在为艾滋病患者提供抗病毒治疗 低收入和中等收入国家（LMIC），并被许多人视为最成功的公共 现代史上的健康干预极大地降低了疾病的患病率和发病率 过去二十年的艾滋病毒。通过临床试验和观察性研究得出结论 证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，保留艾滋病毒护理和抑制 通过遵守 ART 来控制病毒载量可以说是最有效的方法 结束这一流行病，并且确实被编码在联合国艾滋病规划署 95-95-95 基准中 95%的病例得到诊断； 95% 的确诊病例开始并保留 ART；和 95% 治疗个体实现病毒抑制。 根据中低收入国家的要求量身定制的临床决策支持系统 (CDSS) 已被证明可以 提高一系列医护人员对指南的遵守和护理质量。机器的使用 学习算法可以开发临床并发症的预测模型 结果，可以指导医护人员及早发现问题并采取适当的措施 干预措施。因此，该提案的具体目标是（1）使用大型电子健康 记录 (EHR) 数据库，用于开发和验证统计机器学习模型以识别患者 失访和病毒性失败的风险很高； (2) 制定并现场试验实施临床 基于这些模型的决策支持工具将在护理点实施； (3) 至 评估决策支持工具在提高患者保留率和减少 病毒性失败，使用临床水平的随机比较。我们的项目将在 提供医疗保健的学术模型 (AMPATH)，肯尼亚西部的艾滋病毒护理计划 为近 200,000 名艾滋病毒感染者提供服务。