Leveraging Molecular Technologies to Improve Diagnosis and Management of Pediatric Acute Respiratory Illness in Resource-Constrained Settings

利用分子技术改善资源有限环境中儿科急性呼吸系统疾病的诊断和管理

• 批准号: 10739603
• 负责人: Emily Jane Ciccone
• 金额: $ 20.38万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-27 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10739603
• 关键词: Acceleration; Acute; Acute respiratory infection; Affect; Africa South of the Sahara; Age; Algorithms; Antibiotic Therapy; Antibiotics; Antimicrobial Resistance; Applications Grants; Bacterial Infections; Biological Markers; Biometry; Biostatistical Methods; Blood; Blood specimen; C-reactive protein; Categories; Child; Childhood; Clinical; Cohort Studies; Data; Derivation procedure; Detection; Development; Diagnosis; Diagnostic; Diagnostic tests; Enrollment; Etiology; Evaluation; Fever; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Goals; Growth; Hospitalization; Immune Response Genes; Immune response; Infection; Knowledge; Laboratories; Malaria; Malnutrition; Measurement; Measures; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Metagenomics; Methodology; Modeling; Molecular; Molecular Profiling; Nasopharynx; Nature; North Carolina; Participant; Performance; Physicians; Polymerase Chain Reaction; Population; Positioning Attribute; Probability; Process; Quality of Care; Reference Standards; Research Personnel; Resource-limited setting; Respiratory Signs and Symptoms; Role; Sampling; Scientist; Seasons; Site; Streptococcus pneumoniae; Symptoms; Syndrome; Technology; Test Result; Testing; Time; Training; Transcript; Uganda; Uncertainty; Universities; Validation; Viral; Virus; Virus Diseases; Work; acceptability and feasibility; adjudication; antimicrobial; clinical biomarkers; clinical predictive model; co-infection; cohort; communicable disease diagnosis; comorbidity; design; diagnostic tool; differential expression; disease diagnostic; evaluation/testing; experience; global health; improved; influenza pneumonia; next generation sequencing; novel; novel diagnostics; pathogen; pediatrician; performance tests; point of care; predictive modeling; procalcitonin; prospective; respiratory; tool; training opportunity

PROJECT SUMMARY/ABSTRACT Antimicrobial resistance (AMR) is a rapidly growing threat to global health that is primarily driven by the overuse and misuse of antimicrobials. The applicant’s preliminary work in Uganda confirmed what has been noted in other resource-limited settings - children who present with febrile illnesses, particularly those with associated respiratory symptoms, are frequently treated with antibiotic therapy despite most of them likely having self-limited viral infections that do not require such treatment. This inappropriate dispensation of antibiotics is in part due to the diagnostic uncertainty inherent in the reliance on clinical symptoms for diagnosis and management of acute respiratory infections (ARI). Therefore, there is an urgent need for novel diagnostic tools that can distinguish children with bacterial ARI from those with non-bacterial infections, thus reducing antibiotic overuse for children presenting with this clinical syndrome. The objective of the scientific aims proposed in this K23 application is to develop a diagnostic model that accurately predicts bacterial infection in children with febrile ARI in Uganda. This work will facilitate the long-term goal of the applicant, which is to become an independent investigator with expertise in diagnostic evaluation and implementation working to improve quality of care and antimicrobial stewardship in both domestic and global contexts. Specifically, under the mentorship of experts in the fields of emerging molecular technologies, diagnostic evaluation, and prediction modeling at the University of North Carolina at Chapel Hill, she will (1) determine the etiology of febrile ARI in a cohort of previously and prospectively enrolled children, enhancing the diagnostic assessment with PCR-based and metagenomic next generation sequencing tools, (2) validate a host immune response gene expression assay, incorporating genes previously identified to be differentially expressed in viral and bacterial ARI, that minimizes the number of genes included to maximize feasibility in resource-constrained settings, and (3) develop novel clinical algorithms that combine host-based classifiers with other clinical and laboratory data to accurately predict bacterial ARI. Through pursuit of the scientific aims, the associated experiential and hands-on laboratory and biostatistical training, and focused didactic coursework, the applicant will address the following gaps in her knowledge: (1) molecular technologies and their role in infectious disease diagnosis and diagnostic development, (2) clinical prediction modeling, and (3) diagnostic tool evaluation and implementation. By the conclusion of the K23 award period, the applicant will have generated key results to inform a competitive R01 application. She will also be well-positioned to transition into an independent physician scientist and leading expert in diagnostic evaluation and antimicrobial stewardship in resource- constrained settings.

项目总结/摘要 抗菌素耐药性（AMR）是对全球健康的一个快速增长的威胁，主要是由以下因素驱动的： 过度使用和滥用抗菌药物。申请人在乌干达的初步工作证实了 注意到在其他资源有限的环境-儿童谁目前与发热性疾病，特别是那些 相关的呼吸道症状，经常用抗生素治疗，尽管大多数可能 具有不需要这种治疗的自限性病毒感染。这种不适当的分配 抗生素的使用部分是由于依赖临床症状进行诊断所固有的诊断不确定性 急性呼吸道感染（ARI）的治疗。因此，迫切需要新的诊断方法， 可以区分细菌性ARI儿童与非细菌性感染儿童的工具，从而减少 抗生素过度使用的儿童表现出这种临床综合征。科学目标的目的 在这个K23应用中提出的是开发一种诊断模型，可以准确预测细菌感染， 乌干达的发热性ARI儿童。这项工作将有助于申请人的长期目标，即 成为一名独立的调查员，具有诊断评估和实施方面的专业知识， 在国内和全球范围内提高护理质量和抗菌剂管理。特别是根据 新兴分子技术、诊断评估和 在查佩尔山的北卡罗来纳州大学的预测模型中，她将（1）确定 既往和前瞻性入组儿童队列中的发热性ARI，增强了诊断评估 利用基于PCR和宏基因组的下一代测序工具，（2）验证宿主免疫应答 基因表达测定，将先前鉴定为在病毒和 细菌ARI，最大限度地减少基因的数量，以最大限度地提高资源受限的可行性， （3）开发新的临床算法，该算法将联合收割机基于主机的分类器与其他临床和 实验室数据来准确预测细菌ARI。通过追求科学目标， 经验和动手实验室和生物统计培训，以及重点教学课程，申请人 我将解决她的知识中的以下差距：（1）分子技术及其在传染病中的作用 诊断和诊断开发，（2）临床预测建模，以及（3）诊断工具评估和 实施.到K23奖励期结束时，申请人将产生关键成果， 通知竞争R 01应用程序。她也将有能力过渡到一个独立的 医生科学家和领先的专家在诊断评估和抗菌管理的资源， 约束设置。