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从电子病历中挖掘出的治疗算法提高了门诊尿路感染的理论经验治疗效果。

Data mining derived treatment algorithms from the electronic medical record improve theoretical empirical therapy for outpatient urinary tract infections.

机构信息

Department of Urology, Northwestern University, Feinberg School of Medicine, Chicago, Illinois 60611, USA.

出版信息

J Urol. 2011 Dec;186(6):2257-62. doi: 10.1016/j.juro.2011.07.092. Epub 2011 Oct 19.

Abstract

PURPOSE

We determined whether data mining derived algorithms from electronic databases can improve empirical antimicrobial therapy in outpatients with a urinary tract infection.

MATERIALS AND METHODS

The electronic medical records from 3,308 visits associated with a positive urine culture at Northwestern's outpatient Urology and Internal Medicine clinics and Emergency Department from 2005 to 2009 were interrogated. Bacterial species and susceptibility rates for trimethoprim-sulfamethoxazole, ciprofloxacin and nitrofurantoin were compared. Using data mining techniques we created algorithms for empirical therapy of urinary tract infections and compared the theoretical outcomes from data mining derived therapy to those from conventional therapy.

RESULTS

Patients were significantly older in the Department of Urology vs Internal Medicine vs Emergency Department, and more patients in the Department of Urology were male. During the 5-year period the susceptibility rates for ciprofloxacin in the Department of Urology and trimethoprim-sulfamethoxazole in Internal Medicine decreased significantly. In the Department of Urology the susceptibility rate for nitrofurantoin was greater than for ciprofloxacin, which was greater than for trimethoprim-sulfamethoxazole. In all departments, bacteria were more resistant to trimethoprim-sulfamethoxazole than to ciprofloxacin or nitrofurantoin. All drugs were more effective in the Emergency Department and Internal Medicine than the Department of Urology. Prior resistance patterns were the strongest predictor of current susceptibility profiles. In the Department of Urology the algorithms for patients with or without prior cultures theoretically outperformed conventional therapy in men (13.2%) and women (10.1%).

CONCLUSIONS

Antimicrobial resistance patterns in outpatient urinary tract infections are time dependent, and drug and site specific. Data mining directed therapy significantly improved theoretical outcomes compared to conventional therapy for Department of Urology outpatients and for female patients in the Emergency Department.

摘要

目的

我们旨在确定从电子数据库中提取的数据挖掘算法是否可以改善门诊尿路感染患者的经验性抗菌治疗。

材料与方法

我们对 2005 年至 2009 年期间西北大学门诊泌尿科和内科诊所以及急诊科与阳性尿液培养相关的 3308 次就诊的电子病历进行了查询。比较了复方磺胺甲噁唑、环丙沙星和呋喃妥因的细菌种类和药敏率。我们使用数据挖掘技术为尿路感染的经验性治疗创建了算法,并将数据挖掘衍生的治疗理论结果与常规治疗进行了比较。

结果

与内科相比,泌尿科患者年龄明显较大,且泌尿科患者中男性更多。在 5 年期间,泌尿科的环丙沙星药敏率和内科的复方磺胺甲噁唑药敏率显著下降。在泌尿科,呋喃妥因的药敏率大于环丙沙星,而环丙沙星大于复方磺胺甲噁唑。在所有科室中,细菌对复方磺胺甲噁唑的耐药性均大于环丙沙星或呋喃妥因。所有药物在急诊科和内科的疗效均优于泌尿科。既往耐药模式是当前药敏谱的最强预测因素。在泌尿科,对于有无既往培养史的患者,理论上算法治疗优于常规治疗,男性(13.2%)和女性(10.1%)均如此。

结论

门诊尿路感染中的抗菌耐药模式是时间依赖性的,并且具有药物和部位特异性。与常规治疗相比,数据挖掘指导的治疗策略可显著改善理论治疗结果,尤其是在泌尿科门诊患者和急诊科女性患者中。

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