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2017年至2023年德国急性护理医院抗生素使用的动态模式

Kinetic Patterns of Antibiotic Consumption in German Acute Care Hospitals from 2017 to 2023.

作者信息

Schweickert Birgitta, Willrich Niklas, Feig Marcel, Schneider Marc, Behnke Michael, Peña Diaz Luis Alberto, Geffers Christine, Wieters Imke, Gröschner Karin, Richter Doreen, Hoffmann Alexandra, Eckmanns Tim, Abu Sin Muna

机构信息

Healthcare-Associated Infections, Surveillance of Antimicrobial Resistance and Antimicrobial Consumption, Robert Koch Institute, 13353 Berlin, Germany.

Methods Development, Research Infrastructure and Information Technology, Koch-Institute, 13353 Berlin, Germany.

出版信息

Antibiotics (Basel). 2025 Mar 18;14(3):316. doi: 10.3390/antibiotics14030316.

Abstract

Antimicrobial consumption (AMC) patterns, besides prescribing behaviors, reflect the changing epidemiology of infectious diseases. Routine surveillance data have been used to investigate the development of AMC from 2017 to 2023 and the impact of COVID-19 within the context of the framing time periods. Data from 112 hospitals, continuously participating from 2017 to 2023 in the national surveillance system of hospital antimicrobial consumption based at the Robert Koch Institute, were analyzed according to the WHO ATC (Anatomical Therapeutic Chemical)/DDD (Defined Daily Dose) method and categorized according to the WHO AWaRe-classification. AMC was quantified by consumption density (CD) expressed in DDD/100 patient days (PD) and DDD/100 admissions (AD). The time period was subdivided into three phases: pre-pandemic phase (2017-2019), main pandemic phase (2020-2021) and transition phase (2022-2023). Linear regression models have been used to determine the presence of an overall trend, the change in intra-phasic trends and phase-specific mean consumption levels over time. From 2017 to 2023 total antibiotic consumption decreased by 7% from 57.1 to 52.9 DDD/100 PD. Four main kinetic patterns emerged across different antibiotic classes: Pattern 1 displays a decreasing pre-pandemic trend, which slowed down throughout the pandemic and transition phase and was exhibited by second-generation cephalosporins and fluoroquinolones. Pattern 2 reveals a rising pre-pandemic trend, which decelerated in the pandemic phase and accelerated again in the transition phase and was expressed by aminopenicillins/beta-lactamase inhibitors, beta-lactamase sensitive pencillins, azithromycin and first-generation cephalosporins. Pattern 3 shows elevated mean consumption levels in the pandemic phase exhibited by carbapenems, glycopeptides, linezolid and third-generation cephalosporins. Pattern 4 reveals a rising trend throughout the pre-pandemic and pandemic phase, which reversed in the transition phase without achieving pre-pandemic levels and was expressed by beta-lactamase resistant penicillins, daptomycin, fosfomycin (parenteral) and ceftazidime/avibactam. Kinetic consumption patterns across different antibiotic classes might reflect COVID-19-related effects and associated changes in the epidemiology of co-circulating pathogens and health care supply. Broad-spectrum antibiotics with persisting elevated consumption levels throughout the transition phase require special attention and focused antimicrobial stewardship activities.

摘要

抗菌药物消费(AMC)模式除了反映处方行为外,还反映了传染病流行病学的变化。常规监测数据已被用于调查2017年至2023年AMC的发展情况以及在设定时间段背景下COVID-19的影响。对2017年至2023年持续参与罗伯特·科赫研究所医院抗菌药物消费国家监测系统的112家医院的数据,按照世界卫生组织解剖学治疗学化学分类(ATC)/限定日剂量(DDD)方法进行分析,并根据世界卫生组织药物应用的解剖学、治疗学和化学分类(AWaRe)进行分类。AMC通过以DDD/100患者日(PD)和DDD/100住院人次(AD)表示消费密度(CD)来量化。该时间段分为三个阶段:大流行前阶段(2017 - 2019年)、大流行主要阶段(2020 - 2021年)和过渡阶段(2022 - 2023年)。线性回归模型已被用于确定总体趋势的存在、阶段内趋势的变化以及各阶段特定时间的平均消费水平。从2017年到2023年,总抗生素消费量从57.1降至52.9 DDD/100 PD,下降了7%。不同抗生素类别出现了四种主要的动态模式:模式1显示在大流行前呈下降趋势,在整个大流行和过渡阶段放缓,第二代头孢菌素和氟喹诺酮类药物呈现这种模式。模式2显示在大流行前呈上升趋势,在大流行阶段减速,在过渡阶段再次加速,氨基青霉素/β-内酰胺酶抑制剂、β-内酰胺酶敏感青霉素、阿奇霉素和第一代头孢菌素呈现这种模式。模式3显示碳青霉烯类、糖肽类、利奈唑胺和第三代头孢菌素在大流行阶段平均消费水平升高。模式4显示在整个大流行前和大流行阶段呈上升趋势,在过渡阶段逆转但未达到大流行前水平,β-内酰胺酶耐药青霉素、达托霉素、磷霉素(肠胃外)和头孢他啶/阿维巴坦呈现这种模式。不同抗生素类别的动态消费模式可能反映了与COVID-患者共同感染病原体的流行病学和医疗保健供应的相关变化。在整个过渡阶段消费量持续升高的广谱抗生素需要特别关注并开展有针对性的抗菌药物管理活动。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da37/11939389/b583c798a0ea/antibiotics-14-00316-g001.jpg

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