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用于识别急性憩室炎的算法的开发与验证

Development and validation of algorithms to identify acute diverticulitis.

作者信息

Kawatkar Aniket, Chu Li-Hao, Iyer Rajan, Yen Linnette, Chen Wansu, Erder M Haim, Hodgkins Paul, Longstreth George

机构信息

Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.

出版信息

Pharmacoepidemiol Drug Saf. 2015 Jan;24(1):27-37. doi: 10.1002/pds.3708. Epub 2014 Sep 25.

Abstract

PURPOSE

The objectives of this study were to develop and validate algorithms to accurately identify patients with diverticulitis using electronic medical records (EMRs).

METHODS

Using Kaiser Permanente Southern California's EMRs of adults (≥18 years) with International Classification of Diseases, Clinical Modifications, Ninth Revision diagnosis codes of diverticulitis (562.11, 562.13) between 1 January 2008 and 31 August 2009, we generated random samples for pilot (N = 692) and validation (N = 1502) respectively. Both samples were stratified by inpatient (IP), emergency department (ED), and outpatient (OP) care settings. We developed and validated several algorithms using EMR data on diverticulitis diagnosis code, antibiotics, computed tomography, diverticulosis history, pain medication and/or pain diagnosis, and excluding patients with infections and/or conditions that could mimic diverticulitis. Evidence of diverticulitis was confirmed through manual chart review. Agreement between EMR algorithm and manual chart confirmation was evaluated using sensitivity and positive predictive value (PPV).

RESULTS

Both samples were similar in socio-demographics and clinical symptoms. An algorithm based on diverticulitis diagnosis code with antibiotic prescription dispensed within 7 days of diagnosis date, performed well overall. In the validation sample, sensitivity and PPV were (84.6, 98.2%), (95.8, 98.1%), and (91.8, 82.6%) for OP, ED, and IP, respectively.

CONCLUSION

Using antibiotic prescriptions to supplement diagnostic codes improved the accuracy of case identification for diverticulitis, but results varied by care setting.

摘要

目的

本研究的目的是开发并验证利用电子病历(EMR)准确识别憩室炎患者的算法。

方法

利用南加州凯撒医疗机构2008年1月1日至2009年8月31日期间年龄≥18岁、国际疾病分类临床修订版第九版诊断代码为憩室炎(562.11、562.13)的成人电子病历,我们分别生成了用于试点(N = 692)和验证(N = 1502)的随机样本。两个样本均按住院(IP)、急诊科(ED)和门诊(OP)护理环境进行分层。我们利用关于憩室炎诊断代码、抗生素、计算机断层扫描、憩室病史、止痛药物和/或疼痛诊断的电子病历数据开发并验证了几种算法,并排除了有感染和/或可能模拟憩室炎病症的患者。憩室炎的证据通过人工病历审查得到确认。使用敏感性和阳性预测值(PPV)评估电子病历算法与人工病历确认之间的一致性。

结果

两个样本在社会人口统计学和临床症状方面相似。一种基于憩室炎诊断代码且在诊断日期后7天内开具抗生素处方的算法总体表现良好。在验证样本中,门诊、急诊科和住院患者的敏感性和PPV分别为(84.6, 98.2%)、(95.8, 98.1%)和(91.8, 82.6%)。

结论

使用抗生素处方补充诊断代码提高了憩室炎病例识别的准确性,但结果因护理环境而异。

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