Wen Jing, Barber Grant E, Ananthakrishnan Ashwin N
1Department of Biostatistics,Harvard School of Public Health,Boston,Massachusetts.
2Harvard Medical School,Boston,Massachusetts.
Infect Control Hosp Epidemiol. 2015 Aug;36(8):893-8. doi: 10.1017/ice.2015.102. Epub 2015 Apr 30.
To develop an algorithm using administrative codes, laboratory data, and medication data to identify recurrent Clostridium difficile infection (CDI) and to examine the sensitivity, specificity, positive and negative predictive values, and performance of this algorithm.
We identified all patients with 2 or more International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) codes for CDI (008.45) from January 1 through December 31, 2013. Information on number of diagnosis codes, stool toxin assays (enzyme immunoassay or polymerase chain reaction), and unique prescriptions for metronidazole and vancomycin was identified. Logistic regression was used to identify independent predictors of recurrent CDI and a predictive model was developed.
A total of 591 patients with at least 2 ICD-9 codes for CDI were included (median age, 66 years). The derivation cohort consisted of 157 patients among whom 43 (27%) had recurrent CDI. Presence of 3 or more ICD-9 codes for CDI (odds ratio, 2.49), 2 or more stool tests (odds ratio, 2.88), and 2 or more prescriptions for vancomycin (odds ratio, 5.87) were independently associated with confirmed recurrent CDI. A classifier incorporating 2 or more prescriptions for vancomycin and either 2 or more stool tests or 3 or more ICD-9-CM codes had a positive predictive value of 41% and negative predictive value of 90%. The area under the receiver operating characteristic curve for this combined classifier was modest (0.69).
Identification of recurrent episodes of CDI in administrative data poses challenges. Accurate assessment of burden requires individual case review to confirm diagnosis.
开发一种利用管理代码、实验室数据和用药数据来识别复发性艰难梭菌感染（CDI）的算法，并检验该算法的敏感性、特异性、阳性和阴性预测值以及性能。
我们确定了2013年1月1日至12月31日期间所有有2个或更多第九版国际疾病分类临床修订本（ICD-9-CM）CDI代码（008.45）的患者。收集了诊断代码数量、粪便毒素检测（酶免疫测定或聚合酶链反应）以及甲硝唑和万古霉素的独特处方信息。采用逻辑回归来识别复发性CDI的独立预测因素，并建立了一个预测模型。
共纳入591例至少有2个CDI的ICD-9代码的患者（中位年龄66岁）。推导队列由157例患者组成，其中43例（27%）有复发性CDI。存在3个或更多CDI的ICD-9代码（比值比，2.49）、2次或更多粪便检测（比值比，2.88）以及2次或更多万古霉素处方（比值比，5.87）与确诊的复发性CDI独立相关。一个纳入2次或更多万古霉素处方以及2次或更多粪便检测或3个或更多ICD-9-CM代码的分类器的阳性预测值为41%，阴性预测值为90%。该联合分类器的受试者工作特征曲线下面积中等（0.69）。
在管理数据中识别CDI的复发事件具有挑战性。准确评估负担需要对个体病例进行审查以确认诊断。
