DSpace: Utilizing Data Science to Predict and Improve Health Outcomes in Pediatric HIV

DSpace：利用数据科学预测和改善儿童艾滋病毒的健康结果

• 批准号: 10749123
• 负责人: Samuel Kyobe
• 金额: $ 25万
• 依托单位: UNIVERSITY OF BOTSWANA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10749123
• 关键词: 15 year old; Acquired Immunodeficiency Syndrome; Address; Adolescence; Adolescent; Adult; Advanced Development; Africa; Africa South of the Sahara; African; Age; Algorithms; Artificial Intelligence; Bacteriology; Biochemical Pathway; Biological Markers; Botswana; Cardiovascular Diseases; Cause of Death; Characteristics; Child; Childhood; Classification; Clinical; Collaborations; Communities; Community Healthcare; Complex; Complications of Diabetes Mellitus; Continuity of Patient Care; Data; Data Science; Detection; Development; Diabetes Mellitus; Diagnosis; Diagnostic; Disease; Disease Progression; Early Intervention; Electronic Health Record; Future; Generations; Genes; Genetic; Genetic Predisposition to Disease; Genomics; Goals; HIV; HIV/TB; Health; Heterogeneity; Hypertension; Impairment; Incidence; Individual; Infection; International; Intervention; Knowledge; Laboratories; Learning; Life Expectancy; Link; Longitudinal Studies; Machine Learning; Mentors; Metabolic; Metabolic syndrome; Methodology; Methods; Modeling; Molecular Profiling; Morbidity - disease rate; Multiomic Data; Nature; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Patients; Perinatal; Persons; Phenotype; Population; Positioning Attribute; Prevention; Probability; Radiology Specialty; Research; Research Personnel; Resources; Risk; Risk Factors; Role; Subgroup; Technology; Testing; Thoracic Radiography; Translating; Tuberculosis; Tuberculosis diagnosis; Uganda; Validation; Vertical Disease Transmission; Wait Time; adverse outcome; antiretroviral therapy; clinical center; clinical decision-making; clinical heterogeneity; clinically relevant; co-infection; cohort; diagnostic algorithm; diagnostic assay; diagnostic biomarker; diagnostic tool; digital; electronic health database; ethnic diversity; genomic data; high risk; improved; improved outcome; machine learning algorithm; metabolic profile; mortality; mortality risk; multi-ethnic; multidisciplinary; multimodal data; multiple omics; neural network algorithm; novel; pediatric human immunodeficiency virus; pediatric human immunodeficiency virus infection; pharmacologic; point-of-care diagnostics; predictive modeling; predictive tools; prevent; programs; public health intervention; risk prediction; risk stratification; screening; skills; standard of care; tool; transcriptomics; translational potential; tuberculosis diagnostics

Abstract Metabolic Syndrome (MetS) is rapidly increasing in children infected with HIV in sub-Saharan Africa (SSA). According to our preliminary data, 1 in 30 children infected with HIV between the age of 16 and 19 are diagnosed with MetS. In addition, to MetS, tuberculosis (TB) remains a leading cause of morbidity and mortality among HIV-infected children. Moreover, children with HIV have a 30-fold risk of developing TB and a significantly higher risk of death compared to non-HIV-infected children. Clinically, TB in HIV-infected children manifests with extensive heterogeneity (latent TB or active TB [probable, definite, or possible]), which poses a significant diagnostic challenge. The paucibacillary nature of pediatric TB means that only a small fraction of children with a compatible clinical presentation can be bacteriologically confirmed. There have been various efforts to develop data science tools to address patient classification and risk stratification of MetS and improve the diagnosis of TB in adult Western populations. However, these technologies have not been deployed and evaluated in Africa, which bears the biggest burden of people infected with HIV and TB and where the burden of non-communicable diseases is growing rapidly. Furthermore, MetS is a known risk factor for the early development of diabetes mellitus (DM) and cardiovascular disease (CVD) in adulthood. Unfortunately, interventions (either pharmacological or non- pharmacological) that improve metabolic risk factors for children with long-term metabolic impairment (MetS) do not completely prevent or reverse CVD or DM complications, which may be the result of the current timing of interventions which are implemented after metabolic risk factors have been present for many years. Thus, the determination of the longitudinal risk of MetS becomes imperative. Similarly, the availability of multi-omics data presents a valuable opportunity to investigate the host genetics of TB disease in SSA children to advance the development of highly sensitive TB diagnostic algorithms that are much needed. Therefore, the overarching goal of this application is to utilize data science approaches to integrate large temporal electronic health records (EHR) with multi-omics data to predict and improve health outcomes of HIV-infected children in Africa. This retrospective, descriptive longitudinal study will leverage existing data on ~118,000 HIV-infected children from the Baylor International Pediatric AIDS Initiative (BIPAI) programs in Uganda, Botswana and Eswatini. In Aim 1, we will use machine learning to identify informative features within longitudinal EHRs and genomic data to predict MetS in HIV-infected children. We shall also develop composite risk scores for the development of MetS associated with dolutegravir-based combination antiretroviral therapy. Aim 2 of this proposal will focus on the use of explainable machine learning to uncover molecular signatures in multi-omics data as well as characteristic features in temporal EHR that improve the power of predictive models for the diagnosis of TB in HIV-infected children. This effort will translate into developing clinically relevant composite risk scores for the diagnosis of TB and the future development and validation of non-sputum TB diagnostic biomarkers. This application provides a model methodological framework that can be applied to multimodal data in HIV-infected children and improves our understanding of how to effectively use artificial intelligence to target personalized or public health interventions that improve outcomes across the entire spectrum of the HIV continuum care in Africa.

抽象的 在撒哈拉以南非洲 (SSA) 地区，感染艾滋病毒的儿童中，代谢综合征 (MetS) 的发病率正在迅速增加。 根据我们的初步数据，每 30 名 16 岁至 19 岁之间感染艾滋病毒的儿童中就有 1 人是 诊断患有 MetS。此外，对于 MetS 来说，结核病 (TB) 仍然是发病率和死亡率的主要原因 艾滋病毒感染儿童的死亡率。此外，感染艾滋病毒的儿童患结核病和结核病的风险是普通儿童的 30 倍。 与未感染艾滋病毒的儿童相比，死亡风险明显更高。临床上，艾滋病毒感染者中的结核病 儿童表现出广泛的异质性（潜伏性结核病或活动性结核病[很可能、确定或可能]）， 提出了重大的诊断挑战。儿童结核病的少杆菌性质意味着只有一小部分 部分具有相容临床表现的儿童可以通过细菌学方法得到证实。有 人们努力开发数据科学工具来解决患者分类和风险分层问题 MetS 并改善西方成人人群结核病的诊断。然而这些技术还没有 已在非洲部署和评估，该地区是艾滋病毒和结核病感染者负担最重的地区 非传染性疾病负担迅速增加的地区。 此外，MetS 是糖尿病 (DM) 早期发展的已知危险因素， 成年后患心血管疾病（CVD）。不幸的是，干预措施（无论是药物还是非 药理学）可改善患有长期代谢障碍（MetS）儿童的代谢危险因素 不能完全预防或逆转 CVD 或 DM 并发症，这可能是当前时机的结果 代谢危险因素已经存在多年后实施的干预措施。因此， 确定 MetS 的纵向风险势在必行。 同样，多组学数据的可用性为研究宿主遗传学提供了宝贵的机会 结核病 SSA 儿童的疾病，以推进高度敏感的结核病诊断算法的开发 非常需要。因此，该应用程序的总体目标是利用数据科学方法 将大量时态电子健康记录 (EHR) 与多组学数据相集成，以预测和改善健康状况 非洲艾滋病毒感染儿童的结果。这项回顾性、描述性纵向研究将利用 来自贝勒国际儿科艾滋病倡议 (BIPAI) 的约 118,000 名 HIV 感染儿童的现有数据 乌干达、博茨瓦纳和斯威士兰的项目。在目标 1 中，我们将使用机器学习来识别信息丰富的 纵向 EHR 和基因组数据中的特征可预测 HIV 感染儿童的 MetS。我们还将 制定与基于多替拉韦的组合相关的 MetS 发展的综合风险评分 抗逆转录病毒治疗。该提案的目标 2 将重点关注使用可解释的机器学习来发现 多组学数据中的分子特征以及时态 EHR 中的特征特征可改善 预测模型在艾滋病毒感染儿童中诊断结核病的能力。这项努力将转化为 为结核病的诊断和未来的发展制定临床相关的综合风险评分 非痰结核病诊断生物标志物的验证。该应用程序提供了一个模型方法 可应用于艾滋病毒感染儿童的多模式数据并提高我们的理解的框架 如何有效地利用人工智能来针对个性化或公共卫生干预措施，以改善 非洲艾滋病毒连续护理的各个方面的结果。