PDF(Pubmed)
Abstract：
In the last years, there has been an increase of antimicrobial resistance rates around the world with the misuse and overuse of antimicrobials as one of the main leading drivers. In response to this threat, a variety of initiatives have arisen to promote the efficient use of antimicrobials. These initiatives rely on antimicrobial surveillance systems to promote appropriate prescription practices and are provided by national or global health care institutions with limited consideration of the variations within hospitals. As a consequence, physicians\' adherence to these generic guidelines is still limited. To fill this gap, this work presents an automated approach to performing local antimicrobial surveillance from microbiology data. Moreover, in addition to the commonly reported resistance rates, this work estimates secular resistance trends through regression analysis to provide a single value that effectively communicates the resistance trend to a wider audience. The methods considered for trend estimation were ordinary least squares regression, weighted least squares regression with weights inversely proportional to the number of microbiology records available and autoregressive integrated moving average. Among these, weighted least squares regression was found to be the most robust against changes in the granularity of the time series and presented the best performance. To validate the results, three case studies have been thoroughly compared with the existing literature: (i) Escherichia coli in urine cultures; (ii) Escherichia coli in blood cultures; and (iii) Staphylococcus aureus in wound cultures. The benefits of providing local rather than general antimicrobial surveillance data of a higher quality is two fold. Firstly, it has the potential to stimulate engagement among physicians to strengthen their knowledge and awareness on antimicrobial resistance which might encourage prescribers to change their prescription habits more willingly. Moreover, it provides fundamental knowledge to the wide range of stakeholders to revise and potentially tailor existing guidelines to the specific needs of each hospital.
摘要：
在过去的几年里，随着抗菌药物的滥用和过度使用成为主要驱动因素之一,全球抗菌素耐药率不断上升.为了应对这种威胁，为了促进抗菌药物的有效使用，已经出现了各种举措。这些举措依靠抗微生物监测系统来促进适当的处方实践，并由国家或全球医疗机构提供，对医院内部的差异考虑有限。因此,医师对这些通用指南的坚持仍然有限.为了填补这个空白，这项工作提出了一种从微生物学数据进行局部抗菌药物监测的自动化方法.此外，除了通常报道的耐药率，这项工作通过回归分析来估计长期阻力趋势，以提供一个单一的值，有效地将阻力趋势传达给更广泛的受众。趋势估计考虑的方法是普通最小二乘回归，加权最小二乘回归，其权重与可用的微生物学记录数量成反比，并且自回归综合移动平均。其中,加权最小二乘回归被发现是对时间序列的粒度变化最稳健的，并且表现最好。为了验证结果，已将三个案例研究与现有文献进行了彻底比较：（i）尿液培养物中的大肠杆菌；（ii）血液培养物中的大肠杆菌；（iii）伤口培养物中的金黄色葡萄球菌。提供更高质量的本地而非一般抗菌监测数据的好处是双重的。首先,它有可能刺激医生之间的参与，以加强他们对抗菌素耐药性的知识和认识，这可能会鼓励处方者更愿意改变他们的处方习惯。此外，它为广泛的利益相关者提供了基本知识，以根据每家医院的具体需求修订和可能定制现有指南。
