Exploring surgical infection prediction: A comparative study of established risk indexes and a novel model

Abstract

BackgroundSurgical site infections are a major health problem that deteriorates the patients’ health and increases health care costs. A reliable method to identify patients with modifiable risk of surgical site infection is necessary to reduce the incidence of them but data are limited. Hence the objective is to assess the predictive validity of a logistic regression model compared to risk indexes to identify patients at risk of surgical site infections. MethodsIn this study, we evaluated the predictive validity of a new model which incorporates important predictors based on logistic regression model compared to three state-of-the-art risk indexes to identify high risk patients, recruited from 2016 to 2020 from a medium size hospital in North Norway, prone to surgical site infection. ResultsThe logistic regression model demonstrated significantly higher scores, defined as high-risk, in 110 patients with surgical site infections than in 110 patients without surgical site infections (p < 0.001, CI 19–44) compared to risk indexes. The logistic regression model achieved an area under the curve of 80 %, which was better than the risk indexes SSIRS (77 %), NNIS (59 %), and JSS-SSI (52 %) for predicting surgical site infections. The logistic regression model identified operating time and length of stay as the major predictors of surgical site infections. ConclusionsThe logistic regression model demonstrated better performance in predicting surgical site infections compared to three state-of-the-art risk indexes. The model could be further developed into a decision support tool, by incorporating predictors available prior to surgery, to identify patients with modifiable risk prone to surgical site infection.