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.

项目总结/摘要 下呼吸道感染（LRTI）是全球最常见的住院原因之一。病毒 和细菌性LRTI的表现相似，导致临床医生过度开抗菌药物，因为担心错过一个有效的治疗方案。 致死性细菌感染或重复感染。然而，来自全球队列的新数据表明， 在儿童和成人中，LRTI通常比细菌性LRTI更普遍。在低收入或中等收入国家 （LMIC），考虑到有限的诊断能力，病毒性LRTI的抗菌药物过度使用往往更糟。接入点- 不需要实验室基础设施的护理（POC）诊断测试可能会减少抗菌 LRTI在中低收入国家的过度使用。LRTI的本地相关、循证、成本效益高的诊断算法 还没有在中低收入国家得到系统的发展。该提案的目的是整合多个低- 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），以开发和 在LMIC环境中评估LRTI诊断和治疗算法。我们将使用一个大的，现有的，设置- LRTI患者的特定生物储存库，以指导算法开发。建议的目标如下： 1)通过整合临床预测因子、POC病原体、 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LRTI管理的临床算法;以及3）评估LRTI管理 在LMIC的一家医院进行的阶梯楔形、整群随机试验中，我们将完成黄金- 在我们的生物储存库中对样本进行标准检测和临床裁定，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病原体检测和3）POC 生物标志物测试，以确定一种潜在的成本效益的算法，将减少不适当的抗菌 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 to use clinical临床algorithms算法.在算法开发之后，我们将重新召集焦点小组， 算法，并确定适当的方法来传达和实施该算法。我们将 然后进行阶梯楔形聚类随机试验来评估该算法。因LRTI入院的患者 将接受1）算法指导的护理，或2）常规护理。评估临床结局和抗菌 在本试验中，我们将使用创新的抗生素风险持续时间调整反应 （RADAR）临床试验设计由抗菌素耐药性领导小组（ARLG）开发。的 这项工作的预期成果是开发和评估一种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 这些方法可以推广到其他低资源环境，以应对日益严重的全球抗菌素耐药性危机。