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盆腔炎的检测:利用行政数据开发自动病例发现算法

Detection of pelvic inflammatory disease: development of an automated case-finding algorithm using administrative data.

作者信息

Satterwhite Catherine L, Yu Onchee, Raebel Marsha A, Berman Stuart, Howards Penelope P, Weinstock Hillard, Kleinbaum David, Scholes Delia

机构信息

Division of STD Prevention, Centers for Disease Control and Prevention, 1600 Clifton Road, Mailstop E-02, Atlanta, GA 30333, USA.

出版信息

Infect Dis Obstet Gynecol. 2011;2011:428351. doi: 10.1155/2011/428351. Epub 2011 Nov 14.

Abstract

ICD-9 codes are conventionally used to identify pelvic inflammatory disease (PID) from administrative data for surveillance purposes. This approach may include non-PID cases. To refine PID case identification among women with ICD-9 codes suggestive of PID, a case-finding algorithm was developed using additional variables. Potential PID cases were identified among women aged 15-44 years at Group Health (GH) and Kaiser Permanente Colorado (KPCO) and verified by medical record review. A classification and regression tree analysis was used to develop the algorithm at GH; validation occurred at KPCO. The positive predictive value (PPV) for using ICD-9 codes alone to identify clinical PID cases was 79%. The algorithm identified PID appropriate treatment and age 15-25 years as predictors. Algorithm sensitivity (GH = 96.4%; KPCO = 90.3%) and PPV (GH = 86.9%; KPCO = 84.5%) were high, but specificity was poor (GH = 45.9%; KPCO = 37.0%). In GH, the algorithm offered a practical alternative to medical record review to further improve PID case identification.

摘要

国际疾病分类第九版(ICD - 9)编码传统上用于从管理数据中识别盆腔炎性疾病(PID),以进行监测。这种方法可能会纳入非PID病例。为了在具有提示PID的ICD - 9编码的女性中优化PID病例识别,利用额外变量开发了一种病例查找算法。在Group Health(GH)和科罗拉多州凯撒医疗集团(KPCO)中,在15 - 44岁的女性中识别潜在的PID病例,并通过病历审查进行核实。在GH使用分类与回归树分析来开发该算法;在KPCO进行验证。仅使用ICD - 9编码识别临床PID病例的阳性预测值(PPV)为79%。该算法将PID适当治疗和年龄15 - 25岁确定为预测因素。算法的敏感性(GH = 96.4%;KPCO = 90.3%)和PPV(GH = 86.9%;KPCO = 84.5%)较高,但特异性较差(GH = 45.9%;KPCO = 37.0%)。在GH,该算法为病历审查提供了一种实用的替代方法,以进一步改善PID病例识别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73ac/3226320/9a83ed35051f/IDOG2011-428351.001.jpg

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