A randomized controlled trial of a novel, evidence-based algorithm for managing lower respiratory tract infection in a resource-limited setting

一项基于证据的新型算法的随机对照试验，用于在资源有限的环境中管理下呼吸道感染

• 批准号: 10419987
• 负责人: GAYANI TILLEKERATNE
• 金额: $ 63.44万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-25 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10419987
• 关键词: Adult; Algorithms; Anti-Bacterial Agents; Antibiotics; Antimicrobial Resistance; Bacterial Drug Resistance; Bacterial Infections; Biological Markers; C-reactive protein; COVID-19; COVID-19 pandemic; Caring; Cessation of life; Child; Clinical; Clinical Trials Design; Cluster randomized trial; Country; Data; Decision Trees; Development; Diagnostic; Diagnostic tests; Enrollment; Etiology; Evaluation; Focus Groups; Fright; Goals; Gold; Hospitalization; Hospitalized Child; Hospitals; Incidence; Income; Infection; Influenza; Infrastructure; Intervention; Laboratories; Leadership; Lower Respiratory Tract Infection; Mentored Patient-Oriented Research Career Development Award; Methods; Modeling; Outcome; Patients; Physicians; Probability; Randomized; Randomized Controlled Trials; Research; Resource-limited setting; Risk; Sampling; Sri Lanka; System; Teaching Hospitals; Testing; Time Study; Translating; Viral; Work; adjudication; algorithm development; base; biobank; clinical implementation; clinical predictors; clinical risk; cohort; combat; cost; cost effective; design; diagnostic algorithm; diagnostic tool; epidemiologic data; evidence base; hospital bed; implementation barriers; improved; infection management; innovation; novel; pathogen; point of care; point of care testing; point-of-care diagnostics; procalcitonin; prototype; response; superinfection; treatment as usual; trial design; ward

Project Summary/ Abstract Lower respiratory tract infection (LRTI) is one of the most common reasons for hospitalization globally. Viral and bacterial LRTI present similarly, leading clinicians to overprescribe antibacterials for fear of missing a lethal bacterial infection or superinfection. However, emerging data from global cohorts indicate that viral LRTI is frequently more prevalent than bacterial LRTI in both children and adults. In low- or middle-income countries (LMICs), antibacterial overuse for viral LRTI is often worse given limited diagnostic capacity. Access to point- of-care (POC) diagnostic tests, which do not require laboratory infrastructure, may decrease antibacterial overuse for LRTI in LMICs. Locally relevant, evidence-based, cost-effective diagnostic algorithms for LRTI have not been systematically developed in LMICs. The objective of this proposal is to integrate multiple low- cost diagnostic tools (clinical predictors, POC pathogen tests, and POC biomarker tests) to develop and evaluate an LRTI diagnostic and treatment algorithm in a LMIC setting. We will use a large, existing, setting- specific biorepository of patients with LRTI to guide algorithm development. The following aims are proposed: 1) create an evidence-based algorithm for LRTI management by integrating clinical predictors, POC pathogen tests, and POC biomarker tests; 2) establish understanding, acceptability, and barriers to implementation of clinical algorithms for LRTI management among local physicians; and 3) evaluate an LRTI management algorithm in a stepped-wedge, cluster randomized trial at a single hospital in a LMIC. We will complete gold- standard testing and clinical adjudications of samples in our biorepository to identify etiology of infection. We will then construct decision trees by inputting 1) clinical predictors, 2) POC pathogen tests, and 3) POC biomarker tests to identify a potentially cost-effective algorithm that would reduce inappropriate antibacterial prescriptions. We will conduct focus group discussions with local physicians to identify barriers and facilitators to using clinical algorithms. Following algorithm development, we will reconvene focus groups to iterate on the algorithm and to determine appropriate methods for communicating and implementing the algorithm. We will then conduct a stepped-wedge cluster randomized trial to evaluate the algorithm. Patients admitted with LRTI will receive either 1) algorithm-directed care, or 2) usual care. To assess clinical outcomes and antibacterial duration concurrently in this trial, we will use the innovative Response Adjusted for Duration of Antibiotic Risk (RADAR) clinical trial design developed by the Antibacterial Resistance Leadership Group (ARLG). The expected outcome of this work is the development and evaluation of a LRTI diagnostic algorithm that uses local evidence and integrates multiple low-cost diagnostic tools. The long-term goal of this work is to translate these methods to other low-resource settings to combat the growing global crisis of antimicrobial resistance.

项目概要/摘要 下呼吸道感染（LRTI）是全球住院最常见的原因之一。病毒性的 细菌性 LRTI 也存在类似情况，导致临床医生因担心错过了时机而过度开出抗菌药物。 致命的细菌感染或二重感染。然而，来自全球队列的新数据表明，病毒性 LRTI 在儿童和成人中，LRTI 通常比细菌性 LRTI 更为普遍。在低收入或中等收入国家 （中低收入国家），由于诊断能力有限，病毒性 LRTI 的抗菌药物过度使用往往会更糟。访问点- 不需要实验室基础设施的护理 (POC) 诊断测试可能会降低抗菌效果 中低收入国家过度使用 LRTI。本地相关、基于证据、经济有效的 LRTI 诊断算法 中低收入国家尚未得到系统开发。该提案的目标是整合多个低 成本诊断工具（临床预测因子、POC 病原体测试和 POC 生物标志物测试）来开发和 评估 LMIC 环境中的 LRTI 诊断和治疗算法。我们将使用一个大型的、现有的环境—— LRTI 患者的特定生物存储库以指导算法开发。建议实现以下目标： 1) 通过整合临床预测因子、POC 病原体，创建基于证据的 LRTI 管理算法 测试和 POC 生物标志物测试； 2) 建立理解、可接受性和实施障碍 当地医生进行 LRTI 管理的临床算法； 3) 评估 LRTI 管理 在中低收入国家的一家医院进行的阶梯式楔形、整群随机试验中的算法。我们将完成金- 对我们生物样本库中的样本进行标准测试和临床判定，以确定感染的病因。我们 然后将通过输入 1) 临床预测因子、2) POC 病原体测试和 3) POC 构建决策树 生物标志物测试以确定一种潜在的具有成本效益的算法，可以减少不适当的抗菌药物 处方。我们将与当地医生进行焦点小组讨论，以确定障碍和促进因素 使用临床算法。算法开发完成后，我们将重新召集焦点小组来迭代 算法并确定通信和实现算法的适当方法。我们将 然后进行阶梯楔形聚类随机试验来评估算法。因 LRTI 入院的患者 将接受 1) 算法指导的护理，或 2) 常规护理。评估临床结果和抗菌效果 在本试验中，我们将同时使用针对抗生素风险持续时间调整的创新响应 (RADAR) 临床试验设计由抗菌素耐药性领导小组 (ARLG) 开发。这 这项工作的预期成果是开发和评估 LRTI 诊断算法，该算法使用 本地证据并集成多种低成本诊断工具。这项工作的长期目标是翻译 这些方法可用于其他资源匮乏的环境，以应对日益严重的全球抗菌素耐药性危机。