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首页> 外文期刊>Injury >Development of a surgical site infection prediction model in orthopaedic trauma: The Denver Health Model
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Development of a surgical site infection prediction model in orthopaedic trauma: The Denver Health Model

机译：矫形创伤手术部位感染预测模型的发展：丹佛健康模型

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Abstract Background The CDC’s National Healthcare Safety Network’s (NHSN) current risk adjustment model for surgical site infections (SSI) following open reduction internal fixation (ORIF) of long bone fractures is a suboptimal predictor of risk. We hypothesized that by including variables known to be associated with SSI following ORIF, we would develop a model that would increase the accuracy and predictability of SSI risk. Methods Patients who underwent ORIF of a long bone between January 1, 2012 and December 31, 2014 were included in the study (n=1543). Patient risk factors, injury risk factors and perioperative risk factors were considered in the development of this model. We developed a risk prediction model for SSI following ORIF and then applied this to a new dataset of ORIF to determine the expected number of infections. This was compared to the expected number of infections calculated using the NHSN risk adjusted model. Results The final multivariate model included age (odds ratio: 1.02, p-value Staphylococcus aureus (MRSA), which was the most important predictor of infection (7.20, Conclusions The model that was developed uses five easily identifiable risk factors that result in a more accurate prediction of infection at our facility than the currently used model. Level of evidence: Prognostic and epidemiologic study, level III.
机译：摘要背景CDC的国家医疗保健安全网络（NHSN）在长骨骨折开放式内固定（ORIF）之后的外科部位感染（SSI）是风险的次优预测因子。我们假设通过包括在orif之后包括SSI相关的变量，我们将开发一个模型，这会提高SSI风险的准确性和可预测性。方法在2012年1月1日至2014年12月31日期间的长期骨骼中患有患者的患者（n = 1543）。在该模型的发展中考虑了患者危险因素，伤害风险因素和围手术期危险因素。我们开发了SSI之后的SSI风险预测模型，然后将其应用于orif的新数据集以确定预期的感染次数。将其与使用NHSN风险调整模型计算的预期感染数进行比较。结果最终多变量模型包括年龄（差距：1.02，P值葡萄球菌（MRSA），这是感染最重要的预测因子（7.20，结论开发的模型使用五种易于识别的风险因素，导致更多精确预测我们的设施感染而不是目前使用的模型。证据水平：预后和流行病学研究，III水平。

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