基于文本挖掘技术分析抗生素后效应及抗生素的临床合理应用
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篇名: | 基于文本挖掘技术分析抗生素后效应及抗生素的临床合理应用 |
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摘要: | 目的:利用文本挖掘技术分析抗生素后效应(PAE),为抗生素的合理应用提供参考。方法:以“抗生素后效应”“抗菌后效应”“抗菌药物后效应”为关键词,检索1996年1月1日-2017年10月13日在中国知网、万方数据、维普网等数据库中发表的有关具有PAE的抗生素及其作用的细菌、联合用药对PAE的影响、影响PAE持续时间的因素,分析临床合理应用等信息,提出其合理应用的建议。结果:共检索出文献349篇,涉及到62种具有PAE的抗生素及其复合剂。具有PAE的抗生素种类主要包括β-内酰胺类、氨基糖苷类、大环内酯类、喹诺酮类、酰胺醇类和其他类等;频数排名前10位的抗生素依次为环丙沙星、阿米卡星、奈替米星、氧氟沙星、阿奇霉素、庆大霉素、加替沙星、头孢他啶、头孢哌酮和磷霉素;具有PAE的抗生素作用的细菌频数排名前8位的依次是大肠埃希菌、金黄色葡萄球菌、铜绿假单胞菌、粪肠球菌、肺炎克雷伯菌、表皮葡萄球菌、耐药鲍曼不动杆菌、溶血葡萄球菌;共涉及27种联合用药,其中常见的是环丙沙星+磷霉素、奈替米星+头孢他啶、阿莫西林+奈替米星。影响PAE的持续时间因素频数由高到低依次为抗生素种类、抗生素浓度、联用、细菌种类、细菌浓度,机体状态,药物接触时间,药动学参数,药-时曲线下面积(AUC);分析指导临床合理应用抗生素的主要措施包括设计科学合理的给药方案,调整、制订治疗方案和评价新的抗生素。结论:文本挖掘技术可以比较客观、系统地总结出PAE的发生规律,为临床提供参考。临床医师、药师应根据各类抗生素PAE的长短、结合药动学参数设计和调整治疗方案。 |
ABSTRACT: | OBJECTIVE: To analyze the post antibiotics effect (PAE) by using text mining technology and to provide reference for rational application of antibiotics. METHODS: Using “PAE” “post antibacterial effect” “post antibacterial drug effect” as keywords, PAE antibiotics and related bacterial and the effect of drug combination on PAE, influential factors of PAE duration and clinical application were retrieved from CNKI, Wanfang database, VIP during Jan. 1st, 1996-Oct. 13th, 2017. The suggestions were put for ward rational use of PAE antibiotics. RESULTS: A totally of 349 literatures were included, involving 62 kinds of PAE antibiotics and compounds. PAE antibiotics mainly included β-lactam, aminoglycosides, macrolides, quinolones, amphenicol and other types. Top 10 antibiotics in the list of frequency were ciprofloxacin, amikacin, netilmicin, ofloxacin, azithromycin, gentamycin, gatifloxacin, ceftazidime, cefoperazone and fosfomycin. Top 8 bacterials treated with PAE antibiotics were Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Enterococcus faecalis, Klebsiella pneumoniae, Staphylococcus epidermidis, drug-resistant Acinetobacter baumanii and Staphylococcus haemolyticus; 27 kinds of drug combination were involved, among which ciprofloxacin+fosfomycin, netilmicin+ceftazidime, amoxicillin+netilmicin were commonly used. Influential factors of PAE duration were in descending order were types of antibiotics, concentration of antibiotics and combined use of antibiotics, types of bacterial and concentration of bacterial, body condition, drug contact time, pharmacokinetic parameters and AUC. Main measures of rational drug use guidance were designing scientific and rational medication plan, adjusting and formulating therapy plan, evaluating new antibiotics. CONCLUSIONS: Text mining technology can be a more objective and systematic summary of PAE regularity, so as to provide a reference for clinical application. Clinicians and pharmacists should design and adjust the treatment plan according to the duration of PAE and pharmacokinetic parameters. |
期刊: | 2018年第29卷第19期 |
作者: | 唐金凤,杨武斌,王松,刘海林,周春巧 |
AUTHORS: | TANG Jinfeng,YANG Wubin,WANG Song,LIU Hailin,ZHOU Chunqiao |
关键字: | 文本挖掘技术;抗生素;抗生素后效应;细菌;合理应用 |
KEYWORDS: | Text mining technology; Antibiotics; Post antibiotics effect; Bacterial; Rational application |
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