Prediction tool for high risk of surgical site infection in spinal surgery.

Abstract

The incidence of surgical site infection (SSI) is higher in spinal surgeries than in general orthopedic operations. In this study, we aimed to develop a scoring system with reduced health care costs for detecting spinal surgery patients at high risk for SSI. Retrospective cohort study. In total, 824 patients who underwent spinal surgery at 2 university hospitals from September 2005 to May 2011. We reviewed the medical records of 824 patients, and we examined 19 risk factors to identify high-risk patients. After narrowing down the variables by univariate analysis, multiple logistic analysis was performed for factors with P values <.2, using SSI as a dependent variable. Only factors that showed P values <.05 were included in the final models, and each factor was scored based on the β coefficient values obtained. The clinical prediction rules were thereby prepared. "Emergency operation," "blood loss >400 mL," "presence of diabetes," "presence of skin disease," and "total serum albumin value <3.2 g/dL" were detected by multivariable modeling and were incorporated into the risk scores. Applying these 5 independent predictive factors, we were able to predict the infection incidence after spinal surgery. Our present study could aid physicians in making decisions regarding prevention strategies in patients undergoing spinal surgery. Stratification of risks employing this scoring system will facilitate the identification of patients most likely to benefit from complex, time-consuming and expensive infection prevention strategies, thereby possibly reducing healthcare costs.