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.

项目摘要/摘要 下呼吸道感染(LRTI)是全球最常见的住院原因之一。病毒式传播 细菌性LRTI也存在类似的情况，导致临床医生过度开出抗菌药物，以免错过 致死性细菌感染或双重感染。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往往比细菌性LRTI更普遍。在低收入或中等收入国家 (LMICs)，由于诊断能力有限，过度使用抗菌药物治疗病毒性LRTI往往更糟糕。通达点- 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力 在LMIC中过度使用LRTI。局部相关的、基于证据的、经济有效的LRTI诊断算法 在LMIC中没有得到系统的开发。这项建议的目标是整合多个低成本的 开发和开发成本诊断工具(临床预测、POC病原体测试和POC生物标志物测试) 在LMIC环境中评估LRTI诊断和治疗算法。我们将使用现有的大型布景- LRTI患者的特定生物库，以指导算法开发。建议的目标如下： 1)通过集成临床预测因子、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)建立对以下各项的理解、可接受性和实施障碍 用于本地医生的LRTI管理的临床算法；以及3)评估LRTI管理 在LMIC中的一家医院进行的阶梯楔形、整群随机试验中的算法。我们将完成金牌- 对我们生物储存库中的样本进行标准测试和临床裁决，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来构建决策树 生物标记物测试以确定可能具有成本效益的算法，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，以确定障碍和促进者。 使用临床算法。在算法开发之后，我们将再次召集焦点小组迭代 算法，并确定用于通信和实现该算法的适当方法。我们会 然后进行阶梯楔形聚类随机试验，对算法进行评估。因下呼吸道感染入院的病人 将接受1)以算法为导向的护理，或2)常规护理。评估临床结果和抗菌药物 持续时间在本试验中，我们将使用针对抗生素风险持续时间进行调整的创新反应 (雷达)由抗菌药物耐药性领导小组(ARLG)开发的临床试验设计。这个 这项工作的预期结果是开发和评估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 这些方法适用于其他低资源环境，以应对日益严重的全球抗菌素耐药性危机。