Chen Shixing, Li Zepeng, Shi Jiping, Zhou Wanqing, Zhang Haixia, Chang Haiyan, Cao Xiaoli, Gu Changgui, Chen Guangmei, Kang Yi, Chen Yuxin, Wu Chao
Department of Infectious Diseases, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, Jiangsu, People's Republic of China.
Business School, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
Infect Dis Ther. 2022 Jun;11(3):1019-1032. doi: 10.1007/s40121-022-00608-w. Epub 2022 Mar 15.
Balancing the benefits and risks of antimicrobials in health care requires an understanding of their effects on antimicrobial resistance at the population scale. Therefore, we aimed to investigate the association between the population antibiotics use and resistance rates and further identify their critical thresholds.
Data for monthly consumption of six antibiotics (daily defined doses [DDDs]/1000 inpatient-days) and the number of cases caused by five common drug-resistant bacteria (occupied bed days [OBDs]/10,000 inpatient-days) from inpatients during 2009-2020 were retrieved from the electronic prescription system at Nanjing Drum Tower Hospital, a tertiary hospital in Jiangsu Province, China. Then, a nonlinear time series analysis method, named generalized additive models (GAM), was applied to analyze the pairwise relationships and thresholds of these antibiotic consumption and resistance.
The incidence densities of carbapenem-resistant Acinetobacter baumannii (CRAB), carbapenem-resistant Klebsiella pneumoniae (CRKP), and aminoglycoside-resistant Pseudomonas aeruginosa were all strongly synchronized with recent hospital use of carbapenems and glycopeptides. Besides, the prevalence of carbapenem-resistant Escherichia coli was also highly connected the consumption of carbapenems and fluoroquinolones. To lessen resistance, we determined a threshold for carbapenem and glycopeptide usage, where the maximum consumption should not exceed 31.042 and 25.152 DDDs per 1000 OBDs, respectively; however, the thresholds of fluoroquinolones, third-generation cephalosporin, aminoglycosides, and β-lactams have not been identified.
The inappropriate usage of carbapenems and glycopeptides was proved to drive the incidence of common drug-resistant bacteria in hospitals. Nonlinear time series analysis provided an efficient and simple way to determine the thresholds of these antibiotics, which could provide population-specific quantitative targets for antibiotic stewardship.
在医疗保健中平衡抗菌药物的益处和风险需要了解其在人群层面对抗菌药物耐药性的影响。因此，我们旨在研究人群抗生素使用与耐药率之间的关联，并进一步确定其临界阈值。
从中国江苏省一家三级医院——南京鼓楼医院的电子处方系统中检索了2009 - 2020年期间住院患者六种抗生素的月消耗量（每日限定剂量[DDD]/1000住院日）以及五种常见耐药菌引起的病例数（占用床日数[OBD]/10000住院日）的数据。然后，应用一种名为广义相加模型（GAM）的非线性时间序列分析方法来分析这些抗生素消耗与耐药性之间的成对关系和阈值。
耐碳青霉烯鲍曼不动杆菌（CRAB）、耐碳青霉烯肺炎克雷伯菌（CRKP）和耐氨基糖苷铜绿假单胞菌的发病密度均与近期医院碳青霉烯类和糖肽类药物的使用密切同步。此外，耐碳青霉烯大肠埃希菌的流行率也与碳青霉烯类和氟喹诺酮类药物的消耗高度相关。为了降低耐药性，我们确定了碳青霉烯类和糖肽类药物使用的阈值，即每1000个OBD的最大消耗量分别不应超过31.042和25.152 DDD；然而，氟喹诺酮类、第三代头孢菌素、氨基糖苷类和β-内酰胺类药物的阈值尚未确定。
事实证明，碳青霉烯类和糖肽类药物的不当使用会导致医院常见耐药菌的发生。非线性时间序列分析为确定这些抗生素的阈值提供了一种高效且简单的方法，可为抗生素管理提供针对特定人群的定量目标。
