INSPIRE (INtelligent Stewardship Prompts to Improve Real-time Empiric Antibiotic Selection for Patients): The INSPIRE-ASP Trial for Intra-Abdominal and Skin and Soft Tissue Infections

INSPIRE（智能管理提示改善患者实时经验性抗生素选择）：针对腹内、皮肤和软组织感染的 INSPIRE-ASP 试验

• 批准号: 10418669
• 负责人: Susan S. Huang
• 金额: $ 155.07万
• 依托单位: HARVARD PILGRIM HEALTH CARE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-19 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10418669
• 关键词: Abdomen; Abdominal Infection; Algorithms; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Bacterial Antibiotic Resistance; Characteristics; Clinical; Cluster randomized trial; Community Hospitals; Data; Disease; Electronic Health Record; Epidemiology; Feedback; Goals; Hospitals; Infection; Intelligence; Intensive Care Units; Intervention; Intra-abdominal; Knowledge; Length of Stay; Measures; Methods; Organism; Outcome; Patient Selection; Patient-Focused Outcomes; Patients; Physicians; Prevalence; Probability; Provider; Randomized; Resistance; Risk; Risk Estimate; Skin Tissue; Soft Tissue Infections; System; Time; antibiotic resistant infections; base; computerized; drug resistant pathogen; improved; pathogen; precision medicine; prediction algorithm; programs; randomized trial; routine care; soft tissue; trial comparing

Abstract The INSPIRE-ASP Trial (INtelligent Stewardship Prompts to Improve Real-time Empiric Antibiotic Selection for Patients) for Abdominal and Skin and Soft Tissue Infections The INSPIRE-ASP Trial (INtelligent Stewardship Prompts to Improve Real-time Empiric Antibiotic Selection for Patients) for Abdominal and Skin and Soft Tissue Infections is a cluster-randomized trial to improve judicious antibiotic prescribing for non-critically ill hospitalized patients with abdominal infections or skin and soft tissue infections. Currently, over half of non-critically ill patients with one of these infections receive extended-spectrum antibiotics when less than 5% have an antibiotic-resistant pathogen. The goal of this trial is to advise physicians to prescribe standard- vs extended spectrum empiric antibiotics based on an algorithm that estimates each patient’s personalized probability of having an antibiotic-resistant infection. This personalized probability is based upon routinely-collected patient information in the electronic health record and local prevalence of resistant organisms in abdominal or skin and soft tissue infections. This trial will compare routine care under hospital-based antibiotic stewardship programs to the enhanced program using the predictive algorithm plus audit and feedback to reduce unnecessary empiric prescribing of extended-spectrum antibiotics. In our first aim, we will develop disease-specific prediction algorithms for abdominal infections and for skin and soft tissue infections. In our second aim, this predictive algorithm will be integrated into the computerized provider order entry (CPOE) system to prompt physicians when selected antibiotics are discordant with the estimated need for that antibiotic. Physicians will be prompted to use standard-spectrum antibiotics when the risk of an antibiotic-resistant infection is low. Sixty hospitals will be randomized to either routine care or the CPOE prompt intervention plus feedback. This 18-month study will evaluate ~53,000 patients with abdominal infections and ~37,000 patients with skin and soft tissue infections. This trial will evaluate the ability of a real-time risk calculator plus audit and feedback to reduce unnecessary extended-spectrum antibiotics while maintaining good clinical outcomes as measured by length- of-stay and transfer to an intensive care unit. These methods will be readily applicable to other electronic health record prescribing systems.

抽象的 Inspire-Asp试验（智能管理提示改善实时经验性抗生素 患者的选择）腹部和皮肤以及软组织感染 Inspire-Asp试验（智能管理提示改善实时经验性抗生素 选择患者）腹部和皮肤以及软组织感染是一项集群随机试验 改善针对腹部感染或 皮肤和软组织感染。目前，超过一半的患有其中一种感染的非犯罪患者 当少于5％的病原体持续抗生素时，接受延长的谱抗生素。目标 该试验是建议医生根据一个基于 估计每个患者具有抗生素抗性感染的个性化可能性的算法。这 个性化概率基于电子健康记录中常规收集的患者信息 以及腹部或皮肤和软组织感染中抗性生物的局部患病率。 该试验将将基于医院的抗生素管理计划的常规护理与 使用预测算法加审核和反馈来增强程序，以减少不必要的经验性 处方扩展谱抗生素。在我们的第一个目标中，我们将发展特定疾病的预测 腹部感染以及皮肤和软组织感染的算法。在我们的第二个目标中，这种预测性 算法将集成到计算机化提供商订单输入（CPOE）系统中，以提示医生 当选定的抗生素与估计对该抗生素的需求不一致时。医师将被提示 当抗生素耐药性感染的风险很低时，使用标准光谱抗生素。六十医院将 随机分配常规护理或CPOE及时干预以及反馈。这项18个月的研究将 评估约53,000例腹部感染患者和约37,000例皮肤和软组织感染患者。 该试验将评估实时风险计算器以及审核和反馈的能力以减少 不必要的扩展光谱抗生素，同时保持良好的临床结局，如长度 - 安静并转移到重症监护室。这些方法将很容易适用于其他电子 健康记录处方系统。