Tuberculosis in households with infectious cases in Kampala city: Harnessing health data science for new insights on TB transmission and treatment response (DS-IAFRICA-TB)

坎帕拉市感染病例家庭中的结核病：利用健康数据科学获得有关结核病传播和治疗反应的新见解 (DS-IAFRICA-TB)

• 批准号: 10713181
• 负责人: David Patrick Kateete
• 金额: $ 25万
• 依托单位: MAKERERE UNIVERSITY COLLEGE OF HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713181
• 关键词: Age; Algorithms; Antibiotics; Antitubercular Agents; Artificial Intelligence; Body mass index; Capital; Caring; Cities; Clinical; Colony-forming units; Communicable Diseases; Communities; Computers; Consumption; Data; Data Science; Data Scientist; Data Set; Databases; Diagnostic; Disease; Drug resistance; Early identification; Ensure; Environment; Epidemiologist; Exposure to; Feces; Gender; Grouping; HIV; HIV/AIDS; HIV/TB; Health Priorities; High-Throughput DNA Sequencing; Hospitals; Household; Image; Individual; Integration Host Factors; International; Intervention; Interviewer; Laboratories; Machine Learning; Medicine; Methods; Modeling; Molecular; Mycobacterium tuberculosis; Outcome; Participant; Patients; Pattern; Peripheral Blood Mononuclear Cell; Persons; Pharmaceutical Preparations; Physicians; Population; Process; Questionnaires; Research; Research Personnel; Resistance; Ribosomal RNA; Risk; Risk Factors; Sampling; Science; Scientist; Sputum; Structure; Symptoms; Techniques; Thoracic Radiography; Treatment Failure; Treatment outcome; Trees; Triage; Tuberculosis; Uganda; Universities; Whole Blood; X-Ray Medical Imaging; case finding; clinically relevant; co-infection; cohort; comorbidity; computer infrastructure; cost; cytokine; deep learning model; diverse data; feature selection; health data; high risk; indexing; innovation; insight; learning algorithm; low income country; machine learning algorithm; machine learning model; microbiome; multidisciplinary; non-compliance; pathogen; social; social factors; supervised learning; support vector machine; transmission process; treatment response; tuberculosis treatment; unsupervised learning; whole genome

Abstract: Tuberculosis (TB) is prevalent in Uganda, and overlaps with an already high burden of HIV/TB coinfection. While almost all hospital-based TB cases in Kampala city, the capital of Uganda, have clear TB symptoms, 30% or more of the people with undiagnosed TB, identified through active case finding, are asymptomatic for TB; moreover, the host risk factors for TB in Kampala cannot be distinguished from risk factors associated with the environment. Complicating this further is the fact that anti-TB treatment failure rates are higher in Uganda by several order of magnitude, compared to global estimates (17% vs. 10%). These TB-specific challenges depict only a fraction of the complexity underlying the disease, especially in endemic settings with a high burden of HIV/AIDS. Data science methods, especially Artificial Intelligence (AI) and/or Machine Learning algorithms, can unravel such complexity and untangle factors of the host, pathogen and environment underlying TB, which hitherto, have been difficult to explain or predict with conventional approaches. In this proposal, we will harness health data science and elucidate factors underlying transmission of TB in a household, as well as anti-TB treatment failure. We will leverage the computational infrastructure at Makerere, and available demographic, clinical and laboratory data sets from TB patients and their contacts, and develop AI/Machine Learning algorithms that identify: (1) Patients at baseline (month 0) who would not sputum and/or culture convert at months 2 and 5, hence are at risk of failing TB treatment, (2) Contacts of index-TB cases who are at risk of developing household TB disease, as well as contacts who could be resistant to TB infection despite persistent and/or multiple exposure to M. tuberculosis in a household. Answering these aims provides the required evidence that data science methods are effective at early identification of potential TB cases and high-cost patients, hence contribute to halting of TB transmission in the community.

抽象的： 结核病 (TB) 在乌干达很流行，并且与艾滋病毒/结核病合并感染的负担本已很高。尽管 乌干达首都坎帕拉市几乎所有医院结核病例都有明显的结核症状，30%或 通过主动病例发现发现的未确诊结核病患者中有更多没有结核病症状； 此外，无法将坎帕拉结核病宿主风险因素与与结核病相关的风险因素区分开来。 环境。使情况进一步复杂化的是，乌干达的抗结核治疗失败率更高 与全球估计相比（17% 比 10%）高出几个数量级。这些结核病特有的挑战描述了 只是该疾病潜在复杂性的一小部分，特别是在疾病负担高的地方病环境中 艾滋病毒/艾滋病。数据科学方法，尤其是人工智能 (AI) 和/或机器学习算法，可以 解开结核病宿主、病原体和环境的复杂性并理清这些因素， 迄今为止，很难用传统方法解释或预测。在本提案中，我们将利用 健康数据科学并阐明家庭中结核病传播的潜在因素以及抗结核病 治疗失败。我们将利用麦克雷雷的计算基础设施和可用的人口统计数据， 来自结核病患者及其接触者的临床和实验室数据集，并开发人工智能/机器学习 算法可识别：(1) 基线（第 0 个月）的患者，几个月后痰液和/或培养物不会发生转变 2 和 5，因此有结核病治疗失败的风险，(2) 有发展风险的索引结核病病例的接触者 家庭结核病，以及尽管持续和/或可能对结核感染具有抵抗力的接触者 家庭中多次接触结核分枝杆菌。回答这些目标提供了所需的证据： 数据科学方法可以有效地早期识别潜在的结核病病例和高费用患者，因此 为阻止社区结核病传播做出贡献。