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

基本信息

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
项目类别：
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

项目摘要

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病。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临床试验和观察研究得出结论。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 来控制病毒载量是有争议的，但最有效的方法是结束这一流行病，并且确实被编码在联合国艾滋病规划署 95-95-95 基准中 95% 的确诊病例已启动并保留了 ART；接受治疗的个体实现了病毒抑制。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疗保健人员对机器使用的指导方针和护理质量的遵守情况。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 (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) 评估决策支持工具在提高患者保留率和减少病毒性失败，在临床层面进行随机比较，我们的项目将在临床层面实施。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。