Antimicrobial resistance associations with national primary care antibiotic stewardship policy: Primary care-based, multilevel analytic study.
- 作者列表："Hammond A","Stuijfzand B","Avison MB","Hay AD
BACKGROUND:Recent UK antibiotic stewardship policies have resulted in significant changes in primary care dispensing, but whether this has impacted antimicrobial resistance is unknown. AIM:To evaluate associations between changes in primary care dispensing and antimicrobial resistance in community-acquired urinary Escherichia coli infections. METHODS:Multilevel logistic regression modelling investigating relationships between primary care practice level antibiotic dispensing for approximately 1.5 million patients in South West England and resistance in 152,704 community-acquired urinary E. coli between 2013 and 2016. Relationships presented for within and subsequent quarter drug-bug pairs, adjusted for patient age, deprivation, and rurality. RESULTS:In line with national trends, overall antibiotic dispensing per 1000 registered patients fell 11%. Amoxicillin fell 14%, cefalexin 20%, ciprofloxacin 24%, co-amoxiclav 49% and trimethoprim 8%. Nitrofurantoin increased 7%. Antibiotic reductions were associated with reduced within quarter same-antibiotic resistance to: amoxicillin, ciprofloxacin and trimethoprim. Subsequent quarter reduced resistance was observed for trimethoprim and amoxicillin. Antibiotic dispensing reductions were associated with increased within and subsequent quarter resistance to cefalexin and co-amoxiclav. Increased nitrofurantoin dispensing was associated with reduced within and subsequent quarter trimethoprim resistance without affecting nitrofurantoin resistance. CONCLUSIONS:This evaluation of a national primary care stewardship policy on antimicrobial resistance in the community suggests both hoped-for benefits and unexpected harms. Some increase in resistance to cefalexin and co-amoxiclav could result from residual confounding. Randomised controlled trials are urgently required to investigate causality.
背景: 最近英国的抗生素管理政策导致初级保健分配发生了重大变化，但这是否影响了抗菌药物耐药性尚不清楚。 目的: 评估社区获得性尿大肠埃希菌感染中基层医疗机构分配变化与抗菌药物耐药性之间的关系。 方法: 多水平逻辑回归模型调查了2013年至150万年间英格兰西南部约152,704例患者的初级保健实践水平抗生素分配与2016例社区获得性尿大肠埃希菌耐药性之间的关系。根据患者年龄、剥夺和乡村性进行校正，呈现了季度内和随后的药物-bug对的关系。 结果: 与全国趋势一致，每1000名注册患者的总抗生素分配下降了11%。阿莫西林下降14%，头孢氨苄下降20%，环丙沙星下降24%，阿莫西林下降49%，甲氧苄啶下降8%。呋喃妥因升高7%。抗生素的减少与对阿莫西林、环丙沙星和甲氧苄啶的抗生素耐药性在四分之一之内的减少相关。随后观察到甲氧苄啶和阿莫西林的耐药性降低。抗生素分配减少与头孢氨苄和共阿莫西林的季内及随后耐药性增加相关。呋喃妥因分配增加与内及随后的甲氧苄氨嘧啶抗性降低相关，而不影响呋喃妥因抗性。 结论: 对社区抗微生物药物耐药性国家初级保健管理政策的评估表明，这两种政策都是希望的益处和意想不到的危害。残余混杂可能导致对头孢氨苄和阿莫西林的耐药性增加。迫切需要随机对照试验来研究因果关系。
METHODS:PURPOSE:In utero myelomeningocele closure is a valid alternative to postnatal repair with unclear benefits to bladder function. We compared bladder status in patients who underwent fetal myelomeningocele surgery versus postnatal repair. MATERIALS AND METHODS:We retrospectively reviewed our database, with group 1 consisting of in utero surgery and group 2 consisting of postnatal repair. Group 3 was a subgroup of group 2, including patients initially presenting at age less than 12 months. We recorded medical history, radiological investigation with renal ultrasonography, voiding cystourethrography, urodynamic evaluation and clinical outcome of the bladder pattern after treatment. RESULTS:We identified 88 patients in group 1, 86 in group 2 and 38 in group 3. The incidence of urinary tract infection was higher in the postnatal period (45% vs 20%). Hydronephrosis occurred in 20.7%, 22.6% and 28.9% of patients in groups 1, 2 and 3, respectively. Vesicoureteral reflux was diagnosed in 15% in all groups. Urodynamic data showed a higher prevalence of detrusor overactivity in group 1 and no difference in other urodynamic parameters. The high risk bladder pattern at initial evaluation occurred in 56%, 50% and 46% of patients in groups 1, 2 and 3, respectively. There was a trend to decrease the percentages of the high risk bladder pattern and to increase the normal pattern after treatment in all groups. CONCLUSIONS:In utero repair did not improve urological parameters compared to repair in the postnatal period.
METHODS::The development and evolution of antimicrobial resistance (AMR) in pathogens has been reported to be one of the major issues confronting the global health community. The aim of this study was to examine the period prevalence of antibiotic resistance, as well as the trends and patterns in sensitivity profile of enteric bacteria isolated from urine samples of patients with UTIs in a teaching Hospital in south west Nigeria. Urine samples were collected from 77 patients with UTIs from February 2017 to October 2018. Standard laboratory methods were used for urine sample culture and bacterial identification. The Kirby-Bauer disk diffusion method was used to evaluate antimicrobial sensitivity. Predominant enteric bacteria isolates were Escherichia coli (24, 39.3%), Salmonella species (12, 19.7%), Klebsiella species (4, 6.6%), Providencia species (6, 9.8%), Proteus species (8, 13.1%), Serratia species (2, 3.3%), Yersinia species (1, 1.6%) and Morganella species (4, 6.6%). A large proportion (90.2%) of isolates obtained were multi-drug resistant. High resistance in amoxycillin-clavulanate (98%), cefuroxime (92%), erythromycin (90%) and ceftazidime (84%) were recorded. These results emphasize the importance of continuous screening and surveillance programmes for detection of AMR in enteric bacteria of public health importance.
METHODS::Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics. The purpose of this article is to explain a knowledge discovery and data mining approach that was employed to discover knowledge about hospital-acquired catheter-associated urinary tract infections from multiple data sources, including electronic health records and nurse staffing data. Three different machine learning techniques are described: decision trees, logistic regression, and support vector machines. The decision tree model created rules to interpret relationships among associated factors of hospital-acquired catheter-associated urinary tract infections. The logistic regression model showed what factors were related to a higher risk of hospital-acquired catheter-associated urinary tract infections. The support vector machines model was included to compare performance with the other two interpretable models. This article introduces the examples of cutting-edge machine learning approaches that will advance secondary use of electronic health records and integration of multiple data sources as well as provide evidence necessary to guide nursing professionals in practice.