Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore, Maryland, USA.
Infect Control Hosp Epidemiol. 2010 Jul;31(7):694-700. doi: 10.1086/653206.
To develop and validate an algorithm to identify and classify noninvasive infections due to Staphylococcus aureus by using positive clinical culture results and administrative data.
Retrospective cohort study.
Veterans Affairs Maryland Health Care System.
Data were collected retrospectively on all S. aureus clinical culture results from samples obtained from nonsterile body sites during October 1998 through September 2008 and associated administrative claims records. An algorithm was developed to identify noninvasive infections on the basis of a unique S. aureus-positive culture result from a nonsterile site sample with a matching International Classification of Diseases, Ninth Revision (ICD-9-CM), code for infection at time of sampling. Medical records of a subset of cases were reviewed to find the proportion of true noninvasive infections (cases that met the Centers for Disease Control and Prevention National Healthcare Safety Network [NHSN] definition of infection). Positive predictive value (PPV) and negative predictive value (NPV) were calculated for all infections and according to body site of infection.
We identified 4,621 unique S. aureus-positive culture results, of which 2,816 (60.9%) results met our algorithm definition of noninvasive S. aureus infection and 1,805 (39.1%) results lacked a matching ICD-9-CM code. Among 96 cases that met our algorithm criteria for noninvasive S. aureus infection, 76 also met the NHSN criteria (PPV, 79.2% [95% confidence interval, 70.0%-86.1%]). Among 98 cases that failed to meet the algorithm criteria, 80 did not meet the NHSN criteria (NPV, 81.6% [95% confidence interval, 72.8%-88.0%]). The PPV of all culture results was 55.4%. The algorithm was most predictive for skin and soft-tissue infections and bone and joint infections.
When culture-based surveillance methods are used, the addition of administrative ICD-9-CM codes for infection can increase the PPV of true noninvasive S. aureus infection over the use of positive culture results alone.
开发并验证一种算法，通过阳性临床培养结果和行政数据识别和分类非侵袭性金黄色葡萄球菌感染。
回顾性队列研究。
马里兰退伍军人事务部医疗保健系统。
收集了 1998 年 10 月至 2008 年 9 月期间从非无菌部位获得的所有金黄色葡萄球菌临床培养结果以及相关的行政索赔记录的回顾性数据。基于来自非无菌部位样本的独特金黄色葡萄球菌阳性培养结果，开发了一种算法，该算法通过与采样时感染的国际疾病分类，第 9 修订版（ICD-9-CM）代码相匹配来识别非侵袭性感染。对病例的一个子集的医疗记录进行了审查，以确定真实非侵袭性感染的比例（符合疾病控制和预防中心国家医疗保健安全网络[ NHSN]感染定义的病例）。计算了所有感染和根据感染部位的阳性预测值（PPV）和阴性预测值（NPV）。
我们鉴定了 4621 个独特的金黄色葡萄球菌阳性培养结果，其中 2816 个（60.9％）结果符合我们的非侵袭性金黄色葡萄球菌感染算法定义，而 1805 个（39.1％）结果缺乏匹配的 ICD-9-CM 代码。在符合我们的非侵袭性金黄色葡萄球菌感染算法标准的 96 例中，有 76 例也符合 NHSN 标准（PPV，79.2％[95％置信区间，70.0％-86.1％]）。在不符合算法标准的 98 例中，有 80 例不符合 NHSN 标准（NPV，81.6％[95％置信区间，72.8％-88.0％]）。所有培养结果的 PPV 为 55.4％。该算法对皮肤和软组织感染以及骨和关节感染的预测性最高。
当使用基于培养的监测方法时，与仅使用阳性培养结果相比，添加感染的行政 ICD-9-CM 代码可提高真正的非侵袭性金黄色葡萄球菌感染的 PPV。
