School of Public Administration, Nanchang University, No.999 Xuefu Road, 330031, Nanchang, JiangXi Province, China.
School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, No.13 Hangkong Road, 430030, Wuhan, HuBei Province, China.
J Infect Public Health. 2018 Nov-Dec;11(6):821-825. doi: 10.1016/j.jiph.2018.06.003. Epub 2018 Jun 23.
Public report of surgical site infections (SSI) rates has been an important component of SSI reduction strategies, and risk adjustment is needed before SSI rates are publicly reported. Improving the risk adjustment model facilitates meaningful comparison in the public reporting of SSIs. This research aimed to explore an optimal risk adjustment model for the public reporting of cesarean section (CS) SSI.
Information on 2506 cases of CS performed at T hospital, a tertiary general hospital located in the W City of H Province in China, from 01 January 2013 to 31 December 2014 was collected. The data were used to construct the multivariate risk adjustment models of CS SSI through logistic and Poisson stepwise regression. The c-index was used to compare the predictive power between the new logistic regression and the National Nosocomial Infections Surveillance (NNIS) risk index model. Pearson goodness-of-fit was determined to compare the goodness-of-fit between the new Poisson regression and the NNIS risk index model. The two new regression models were also compared.
The logistic and Poisson regression models included two patient-related risk factors, namely, BMI (OR=1.085, P=0.006; RR=1.081, P=0.006) and ASA score (OR=1.522, P=0.044; RR=1.501, P=0.047). The c-index of the logistic regression model (0.628) was higher than that of the NNIS risk index model (0.600). The goodness-of-fit of the Poisson regression model (0.946) was better than that of the NNIS risk index model (0.851).
The logistic and Poisson regression risk models are better than the NNIS risk index model, implying that a multifactorial risk adjustment model is needed for the public reporting of CS SSI. The advantage of logistic regression model is that the predictive power of model can be evaluated by c-index, however, Poisson regression may offer more advantages on model accuracy than logistic regression does when the infection rate decreases.
公开外科部位感染（SSI）率是减少 SSI 策略的一个重要组成部分，在公开报告 SSI 率之前需要进行风险调整。改进风险调整模型有助于在 SSI 的公开报告中进行有意义的比较。本研究旨在探索一种用于公开剖宫产（CS）SSI 报告的最佳风险调整模型。
收集了中国 H 省 W 市 T 医院 2013 年 1 月 1 日至 2014 年 12 月 31 日期间进行的 2506 例 CS 病例的信息。使用逻辑和泊松逐步回归构建 CS SSI 的多变量风险调整模型。通过比较新的逻辑回归和国家医院感染监测（NNIS）风险指数模型之间的 c 指数，来比较新的逻辑回归模型的预测能力。通过比较新的泊松回归和 NNIS 风险指数模型之间的皮尔逊拟合优度，来确定新的泊松回归模型的拟合优度。还比较了两个新的回归模型。
逻辑和泊松回归模型包含两个与患者相关的风险因素，即 BMI（OR=1.085，P=0.006；RR=1.081，P=0.006）和 ASA 评分（OR=1.522，P=0.044；RR=1.501，P=0.047）。逻辑回归模型的 c 指数（0.628）高于 NNIS 风险指数模型（0.600）。泊松回归模型的拟合优度（0.946）优于 NNIS 风险指数模型（0.851）。
逻辑和泊松回归风险模型优于 NNIS 风险指数模型，这意味着需要建立一个多因素风险调整模型来公开报告 CS SSI。逻辑回归模型的优势在于可以通过 c 指数评估模型的预测能力，然而，当感染率降低时，泊松回归可能比逻辑回归具有更高的模型准确性优势。
