Understanding and Informing Early Hospital Antibiotic Prescribing for Potential Infection

了解并告知医院针对潜在感染的早期抗生素处方

• 批准号: 10442396
• 负责人: Vincent Liu
• 金额: $ 46.99万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10442396
• 关键词: Understanding; Informing; Early; Hospital; Antibiotic

Background: Each year, millions of Americans are hospitalized for suspected infection and receive antibiotics. Current guidelines strongly urge broad-spectrum antibiotics be delivered within 1 hour in the most-life- threatening cases: severe sepsis and septic shock. However, unlike other time-sensitive medical emergencies, there is no objective standard for the diagnosis of severe sepsis. Yet, several government initiatives incentivize rapid antibiotic treatment for severe sepsis. There are growing concerns that this intense focus on early antibiotics will increase overall antibiotic use, contributing to antimicrobial resistance. This R01 will measure trade-offs associated with accelerating time-to-antibiotics to inform both individual bedside decision-making and federal policy. Specific Aims: (A1) Hospital antibiotic prescribing behavior: When hospitals accelerate timing of antibiotic delivery for severe sepsis, how often is this change associated with increasing antibiotic use and broader spectrum of coverage among all-comers with potential infection? (A2) Individual harms/benefits: For an individual patient, we will quantify the: (a) risks of harm associated with very short courses of antibiotics (allergic reaction, renal or liver injury, cytopenias, C. difficile infection, isolation of new resistant bacteria, and mortality); (b) mortality benefit associated with earlier delivery of antibiotics. A3) Hospital net harms/benefits: Based on temporal changes in antibiotic prescribing (Aim 1), patient-level harms/benefits (Aim 2), and hospital prevalence of severe sepsis and septic shock, we will estimate the net benefits and harms associated with temporal changes in antibiotic prescribing with simulation. Anticipated Impact: This work will quantify the prescribing trade-offs associated with accelerating time-to- antibiotics, the individual patient-level harms of very short courses of antibiotics, and the benefit of faster antibiotic delivery, across subgroups of patients. Finally, we will quantify the net benefits and harms of accelerating time-to-antibiotics across different types of hospitals with varying sepsis prevalence and magnitude of negative externalities associated with accelerating time-to-antibiotics. Unique Features and Innovation: Using granular, patient-level data from Kaiser Permanente Northern California (KPNC) and nationwide Veterans Affairs (VA), and a novel measure of antimicrobial coverage spectrum (Spectrum Score), we will evaluate the holistic benefits and harms of faster antibiotic timing, making summary judgements about the balance of benefits straightforward and informative. Project Methods: We will examine all KNPC and VA hospitalizations (2013-2018) admitted with potential infection. We will assess hospital-specific temporal trends in antibiotic prescribing patterns using multilevel models. We will assess patient-level harms/benefit using matching and regression. We will estimate the plausible range of trade-offs that result from accelerating time-to-antibiotics with simulation.

背景：每年有数百万美国人因疑似感染住院并接受抗生素治疗。 目前的指南强烈要求广谱抗生素在大多数生命周期内1小时内给药， 危重病例：严重脓毒症和脓毒性休克。然而，与其他时间敏感的医疗紧急情况不同， 严重脓毒症的诊断没有客观标准。然而，一些政府举措鼓励 严重脓毒症的快速抗生素治疗。越来越多的人担心，这种对早期 抗生素将增加抗生素的总体使用，导致抗生素耐药性。此R 01将测量 与加快抗生素使用时间相关的权衡，以告知个体床边决策 和联邦政策。 具体目标：（A1）医院抗生素处方行为：当医院加快抗生素的使用时间时， 严重脓毒症的分娩，这种变化与抗生素使用增加和更广泛的 所有潜在感染者的覆盖范围？(A2)个体危害/受益：对于 对于个别病人，我们将量化：（a）与非常短的抗生素疗程相关的伤害风险 （过敏反应、肾或肝损伤、血细胞减少、C.艰难梭菌感染，分离新的耐药菌， 死亡率）;（B）与抗生素的早期递送相关的死亡率益处。A3）医院净危害/益处： 基于抗生素处方的时间变化（目标1）、患者水平的损害/受益（目标2）和医院 严重脓毒症和脓毒性休克的患病率，我们将估计与 模拟抗生素处方的时间变化。 预期影响：这项工作将量化与加速时间相关的处方权衡， 抗生素，非常短的抗生素疗程的个体患者水平的危害，以及更快的抗生素疗程的益处。 抗生素给药，在患者亚组中。最后，我们将量化的净收益和危害， 加快不同类型医院的抗生素使用时间， 与加速抗生素使用时间相关的负外部性的程度。 独特的功能和创新：使用来自Kaiser Permanente北方医疗机构的患者级细粒度数据 加州（KPNC）和全国退伍军人事务部（VA），以及一种新的抗菌覆盖率措施 频谱（频谱评分），我们将评估更快的抗生素时间的整体效益和危害， 关于利益平衡的简要判断直截了当，内容丰富。 项目方法：我们将检查所有KNPC和VA住院（2013-2018年）， 感染我们将使用多水平的抗生素处方模式评估医院特定的时间趋势， 模型我们将使用匹配和回归评估患者水平的损害/受益。我们将估计 通过模拟加速抗生素使用时间而产生的合理权衡范围。