Exploring an optimal risk adjustment model for public reporting of cesarean section surgical site infections

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.