Rahbé Eve, Kovacevic Aleksandra, Opatowski Lulla, Leclerc Quentin J
Epidemiology and Modelling of Bacterial Escape to Antimicrobials (EMEA), Institut Pasteur, Université Paris Cité, Paris, Île-de-France, 75015, France.
Team Echappement aux Anti-infectieux et Pharmacoépidémiologie U1018 CESP, INSERM, Université Paris-Saclay, Université de Versailles St-Quentin-en-Yvelines, Versailles, Île-de-France, 78000, France.
Wellcome Open Res. 2024 Oct 2;9:248. doi: 10.12688/wellcomeopenres.21181.2. eCollection 2024.
Efforts to estimate the global burden of antimicrobial resistance (AMR) have highlighted gaps in existing surveillance systems. Data gathered from hospital networks globally by pharmaceutical industries to monitor antibiotic efficacy in different bacteria represent an underused source of information to complete our knowledge of AMR burden.. We analysed available industry monitoring systems to assess to which extent combining them could help fill the gaps in our current understanding of AMR levels and trends.
We analysed six industry monitoring systems (ATLAS, GEARS, SIDERO-WT, KEYSTONE, DREAM, and SOAR) obtained from the Vivli platform and reviewed their respective isolates collection and analysis protocols. Using the R software, we designed a pipeline to harmonise and combine these into a single dataset. We assessed the reliability of resistance estimates from these sources by comparing the combined dataset to the publicly available subset of WHO GLASS for shared bacteria-antibiotic-country-year combinations.
Combined, the industry monitoring systems cover 18 years (4 years for GLASS), 85 countries (71), 412 bacterial species (8), and 75 antibiotics (25). Although all industry systems followed a similar centralised testing approach, the patient selection protocol and associated sampling period were unclear. Over all reported years and countries, E.coli, K. pneumoniae and S. aureus resistance rates were in >65% of cases within 0.1 of the corresponding estimate in GLASS. We did not identify systemic bias towards resistance in industry systems compared to GLASS.
High agreement values for available comparisons with GLASS suggest that data for other bacteria-antibiotic-country-year combinations only present in industry systems could complement GLASS; however, for this purpose patient and isolate selection criteria must first be clarified to understand the representativeness of industry systems. This additional source of information on resistance levels could help clinicians and stakeholders prioritize testing and select appropriate antibiotics in settings with limited surveillance data.
评估全球抗菌药物耐药性（AMR）负担的努力凸显了现有监测系统存在的差距。制药行业从全球医院网络收集的数据用于监测不同细菌对抗生素的疗效，这是一个未得到充分利用的信息来源，有助于完善我们对抗菌药物耐药性负担的认识。我们分析了现有的行业监测系统，以评估将它们结合起来在多大程度上有助于填补我们目前对抗菌药物耐药性水平和趋势认识上的空白。
我们分析了从Vivli平台获得的六个行业监测系统（ATLAS、GEARS、SIDERO-WT、KEYSTONE、DREAM和SOAR），并审查了它们各自的分离株收集和分析方案。使用R软件，我们设计了一个流程，将这些系统进行协调并合并成一个单一数据集。通过将合并后的数据集与世界卫生组织全球抗菌药物耐药性和使用监测系统（WHO GLASS）中公开可用的共享细菌-抗生素-国家-年份组合子集进行比较，我们评估了这些来源的耐药性估计的可靠性。
综合来看，行业监测系统覆盖了18年（GLASS为4年）、85个国家（GLASS为71个）、412种细菌（GLASS为8种）和75种抗生素（GLASS为25种）。尽管所有行业系统都采用了类似的集中检测方法，但患者选择方案和相关采样期并不明确。在所有报告的年份和国家中，大肠杆菌、肺炎克雷伯菌和金黄色葡萄球菌的耐药率在超过65%的情况下与GLASS中的相应估计值相差在0.1以内。与GLASS相比，我们未发现行业系统存在对耐药性的系统性偏差。
与GLASS进行的现有比较中较高的一致性值表明，仅在行业系统中存在的其他细菌-抗生素-国家-年份组合的数据可以补充GLASS；然而，为此目的，必须首先明确患者和分离株选择标准，以了解行业系统的代表性。这种关于耐药性水平的额外信息来源可以帮助临床医生和利益相关者在监测数据有限的环境中优先进行检测并选择合适的抗生素。
