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首页> 外文期刊>Pharmacoepidemiology and drug safety >Development of an algorithm to detect methotrexate wrong frequency error using computerized health care data
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Development of an algorithm to detect methotrexate wrong frequency error using computerized health care data

机译:使用计算机化医疗保健数据检测甲基发出错误频率错误的算法的开发

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摘要

Purpose We validated an algorithm to detect frequency errors in computerized healthcare data and estimated the incidence of these errors in an integrated healthcare system. Methods We applied Sentinel System analytic tools on the electronic health records of Kaiser Permanente, Northern California, January 1, 2010, through May 30, 2015,to identify rheumatoid arthritis (RA) patients with new use of methotrexate (365-day baseline period). We identified potential methotrexate frequency errors using ICD-9 code 995.20 (adverse drug event), Current Procedural Terminology (CPT) code 96409 for injection of leucovorin and prescription refill patterns. We performed chart review to confirm the frequency errors, assessed performance for detecting frequency errors, and estimated the incidence of chart-confirmed errors. Results The study included 24,529 methotrexate dispensings among 3,668 RA patients. Among these, 722 (3%) had one dispensing and 23,807 (97.1%) had >= 2 dispensings during 1-year follow-up period. We flagged 653 (2.7%) with a potential medication error (46 with one dispensing and 607 with >= 2 dispensings). We sampled 94 for chart review, and confirmed three methotrexate errors. All three confirmed frequency errors involved a first methotrexate dispensing followed by injected rescue therapy, leucovorin, (positive predictive value, 60%; 95% confidence interval [CI], 15-95%). No potential errors were found among patients with >= 2 dispensings. We estimated the frequency error incidence among one methotrexate dispensing to be 0.4% (95%CI, 0.1% to 1.2%). Conclusion Rescue therapy is a specific indicator of methotrexate overdose among first methotrexate dispensings. This method is generalizable to other medications with serious adverse events treated with antidotes.
机译:目的,我们验证了一种算法来检测计算机化的医疗保健数据中的频率误差,并估计综合医疗保健系统中这些误差的发生率。方法采用2010年1月1日,2010年1月1日,2010年1月1日,展示了Kaiser Permanente的电子健康记录的Sentinel系统分析工具,以鉴定甲氨蝶呤新使用的类风湿性关节炎(RA)患者(365天基线) 。我们使用ICD-9代码995.20(不利药物事件),目前程序术语(CPT)代码96409注射白草和处方再填充模式的潜在甲氨蝶呤频率误差。我们进行了图表审查以确认频率误差,评估检测频率误差的性能,并估计图表确认错误的发生率。结果该研究包括3,668名患者中的24,529个甲氨蝶呤分配。其中,722(3%)有一个分配,23,807(97.1%)>在1年后续期间= 2分配。我们将653(2.7%)标记为潜在的药物误差(46个,一个分配和607个,带> = 2分配)。我们对图表进行了抽样的图表审查,并确认了三个甲氨蝶呤错误。所有三种确诊的频率误差涉及第一次甲氨蝶呤分配,然后注入救援治疗,Leucovorin(阳性预测值,60%; 95%置信区间[CI],15-95%)。在> = 2分配的患者中没有发现潜在的错误。我们估计一个甲氨蝶呤分配给0.4%(95%CI,0.1%至1.2%)的频率误差发病率。结论救援治疗是第一次甲氨蝶呤分配剂中甲氨蝶呤过量的特定指标。该方法是概遍的其他药物,其具有用解毒剂处理的严重不良事件。

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