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明确，实际上，包括将长达数十年的大流行带入终点。总统的紧急计划艾滋病救济（PEPFAR）是一项持续数十亿美元的投资低收入和中等收入国家（LMIC），许多人都被视为最成功的公众现代历史上的健康干预措施大大降低了在过去的二十年中，艾滋病毒。通过临床试验和观察性研究证明抗逆转录病毒疗法（ART）立即治疗是最有效的方法既治疗艾滋病毒，也可以防止新感染的传播，保留在艾滋病毒护理和抑制中通过遵守艺术的病毒负荷可以说是可用于的最有效方法使大流行末尾有95％的诊断病例； 95％的案件诊断出案件启动并保留在ART上；和95％的案件接受病毒抑制的治疗个体。根据LMICS的要求，已证明了临床决策支持系统（CDSS）提高一系列医疗保健人员的准则和护理质量的依从性。使用机器学习算法允许开发用于临床并发症的预测模型和结果，可以指导医疗保健人员早期确定问题和适当的问题干预措施。因此，该提案的具体目的是（1）使用大型电子健康记录（EHR）数据库以开发和验证统计机器学习模型以识别患者有损失的高风险跟踪和病毒衰竭；（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；（3）到评估决策支持工具的效率，以改善患者保留和减少病毒衰竭，使用临床水平的随机比较。我们的项目将在肯尼亚西部的艾滋病毒护理计划提供医疗保健（AMPATH）的学术模型为近20万名艾滋病毒的人提供服务。