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: 10631986
• 负责人: Susan S. Huang
• 金额: $ 153.68万
• 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/10631986
• 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; Hospitalization; Hospitals; Infection; Intelligence; Intensive Care Units; Intervention; Intra-abdominal; Knowledge; Length of Stay; Measures; Methods; Organism; Outcome; Patient Admission; 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; 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.

摘要 INSPIRE-ASP试验（智能管理促使改进实时经验性抗生素 腹部和皮肤软组织感染的患者选择 INSPIRE-ASP试验（智能管理系统改善实时经验性抗生素 腹部和皮肤软组织感染的患者选择）是一项随机分组试验， 改善非危重住院腹部感染患者的抗生素处方， 皮肤和软组织感染。目前，超过一半的非重症患者患有这些感染之一， 当少于5%的人有抗药性病原体时，接受广谱抗生素治疗。的目标 这项试验是建议医生处方标准与扩展谱经验性抗生素的基础上， 该算法估计每个患者的个性化的耐药感染概率。这 个性化概率是基于电子健康记录中的随机收集的患者信息 以及腹部或皮肤和软组织感染中耐药微生物的局部流行。 本试验将比较基于医院的抗生素管理计划下的常规护理与 使用预测算法加上审计和反馈的增强程序，以减少不必要的经验 开超广谱抗生素的处方在我们的第一个目标中，我们将开发疾病特异性预测 用于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， 一种算法将被集成到计算机化的供应商订单输入（CPOE）系统中，以提示医生 当选择的抗生素与估计的抗生素需求不一致时。医生将被提示 当耐药性感染的风险较低时，使用标准谱抗生素。60家医院将 随机分为常规护理组或CPOE即时干预加反馈组。这项为期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加上审计和反馈的能力， 不必要的广谱抗生素，同时保持良好的临床结果，如长度测量， 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。