Center for Interdisciplinary Research on Antibiotic Resistance, School of Nursing, Columbia University, New York, New York, USA.
Surg Infect (Larchmt). 2011 Dec;12(6):459-64. doi: 10.1089/sur.2010.109. Epub 2011 Dec 2.
Surgical site infections (SSIs), the second most common healthcare-associated infections, increase hospital stay and healthcare costs significantly. Traditional surveillance of SSIs is labor-intensive. Mandatory reporting and new non-payment policies for some SSIs increase the need for efficient and standardized surveillance methods. Computer algorithms using administrative, clinical, and laboratory data collected routinely have shown promise for complementing traditional surveillance.
Two computer algorithms were created to identify SSIs in inpatient admissions to an urban, academic tertiary-care hospital in 2007 using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes (Rule A) and laboratory culture data (Rule B). We calculated the number of SSIs identified by each rule and both rules combined and the percent agreement between the rules. In a subset analysis, the results of the rules were compared with those of traditional surveillance in patients who had undergone coronary artery bypass graft surgery (CABG).
Of the 28,956 index hospital admissions, 5,918 patients (20.4%) had at least one major surgical procedure. Among those and readmissions within 30 days, the ICD-9-CM-only rule identified 235 SSIs, the culture-only rule identified 287 SSIs; combined, the rules identified 426 SSIs, of which 96 were identified by both rules. Positive and negative agreement between the rules was 36.8% and 97.1%, respectively, with a kappa of 0.34 (95% confidence interval [CI] 0.27-0.41). In the subset analysis of patients who underwent CABG, of the 22 SSIs identified by traditional surveillance, Rule A identified 19 (86.4%) and Rule B identified 13 (59.1%) cases. Positive and negative agreement between Rules A and B within these "positive controls" was 81.3% and 50.0% with a kappa of 0.37 (95% CI 0.04-0.70).
Differences in the rates of SSI identified by computer algorithms depend on sources and inherent biases in electronic data. Different algorithms may be appropriate, depending on the purpose of case identification. Further research on the reliability and validity of these algorithms and the impact of changes in reimbursement on clinician practices and electronic reporting is suggested.
手术部位感染（SSI）是第二大常见的与医疗保健相关的感染，它会显著增加住院时间和医疗保健成本。传统的 SSI 监测需要大量的人力。强制性报告和对某些 SSI 实行新的不付款政策增加了对高效和标准化监测方法的需求。使用常规收集的行政、临床和实验室数据的计算机算法已显示出补充传统监测的潜力。
使用国际疾病分类，第九修订版，临床修正（ICD-9-CM）诊断代码（规则 A）和实验室培养数据（规则 B），创建了两个计算机算法，以识别 2007 年城市学术三级保健医院住院患者的 SSI。我们计算了每个规则和两个规则结合所识别的 SSI 数量，以及规则之间的百分比一致性。在亚组分析中，将规则的结果与接受冠状动脉旁路移植术（CABG）的患者的传统监测结果进行了比较。
在 28956 例索引住院患者中，有 5918 例（20.4%）至少进行了一次主要手术。在这些患者和 30 天内的再入院患者中，仅使用 ICD-9-CM 的规则识别出 235 例 SSI，仅使用培养物的规则识别出 287 例 SSI；两个规则结合识别出 426 例 SSI，其中 96 例被两个规则同时识别出。规则之间的阳性和阴性一致性分别为 36.8%和 97.1%，kappa 值为 0.34（95%置信区间 [CI] 0.27-0.41）。在 CABG 患者的亚组分析中，传统监测识别出的 22 例 SSI 中，规则 A 识别出 19 例（86.4%），规则 B 识别出 13 例（59.1%）。在这些“阳性对照”中，规则 A 和 B 之间的阳性和阴性一致性分别为 81.3%和 50.0%，kappa 值为 0.37（95%置信区间 [CI] 0.04-0.70）。
计算机算法识别的 SSI 率差异取决于电子数据的来源和固有偏差。根据病例识别的目的，可能需要使用不同的算法。建议进一步研究这些算法的可靠性和有效性，以及报销变化对临床医生实践和电子报告的影响。
