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; 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）在乌干达普遍存在，重叠，与HIV/TB共同感染的负担很高。尽管 乌干达首都坎帕拉市几乎所有基于医院的结核病病例都有明确的结核病症状，30％或 通过活跃的病例发现发现的更多未诊断结核病的人是无症状的TB。 此外，坎帕拉（Kampala）中结核病的宿主风险因素不能与与之相关的风险因素区分开 环境。进一步使这一事实复杂化的是，乌干达的抗TB治疗失败率较高 与全球估计相比，几个数量级（17％比10％）。这些特定于结核病的挑战描绘了 疾病的基础上只有一小部分，尤其是在高负担的地方性环境中 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 揭示宿主，病原体和环境的这种复杂性和解开特征的障碍因素， 迄今为止，很难用常规方法来解释或预测。在此提案中，我们将利用 健康数据科学和阐明家庭中结核病的基本因素以及抗结核 治疗失败。我们将利用Makerere的计算基础架构以及可用的人口统计 来自结核病患者及其联系的临床和实验室数据集，并开发AI/机器学习 识别的算法：（1）基线（第0个月）的患者不会在几个月内转换痰和/或培养 2和5，因此有可能失败的结核病治疗，（2）有可能开发的索引-TB案件的联系 家庭结核病以及尽管持续和/或可能对结核病感染有抵抗力的接触 家庭中多次暴露于结核分枝杆菌。回答这些目标提供了所需的证据 数据科学方法在早期鉴定潜在的结核病病例和高成本患者时有效，因此 有助于在社区中停止结核病传播。