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Updated: May 3, 2026
In Vivo Mouse Model of Spinal Implant Infection
Published on: June 23, 2020
Predicting surgical site infection after spine surgery: a validated model using a prospective surgical registry
Michael J Lee1, Amy M Cizik1, Deven Hamilton1
1Department of Orthopedic Surgery and Sports Medicine, University of Washington Medical Center, 1959 NE Pacific St, Box 356500, Seattle, WA 98195, USA.
Background Context:
The impact of surgical site infection (SSI) is substantial. Although previous study has determined relative risk and odds ratio (OR) values to quantify risk factors, these values may be difficult to translate to the patient during counseling of surgical options. Ideally, a model that predicts absolute risk of SSI, rather than relative risk or OR values, would greatly enhance the discussion of safety of spine surgery. To date, there is no risk stratification model that specifically predicts the risk of medical complication.
Purpose:
The purpose of this study was to create and validate a predictive model for the risk of SSI after spine surgery.
Study Design:
This study performs a multivariate analysis of SSI after spine surgery using a large prospective surgical registry. Using the results of this analysis, this study will then create and validate a predictive model for SSI after spine surgery.
Patient Sample:
The patient sample is from a high-quality surgical registry from our two institutions with prospectively collected, detailed demographic, comorbidity, and complication data.
Outcome Measures:
An SSI that required return to the operating room for surgical debridement.
Materials And Methods:
Using a prospectively collected surgical registry of more than 1,532 patients with extensive demographic, comorbidity, surgical, and complication details recorded for 2 years after the surgery, we identified several risk factors for SSI after multivariate analysis. Using the beta coefficients from those regression analyses, we created a model to predict the occurrence of SSI after spine surgery. We split our data into two subsets for internal and cross-validation of our model. We created a predictive model based on our beta coefficients from our multivariate analysis.
Results:
The final predictive model for SSI had a receiver-operator curve characteristic of 0.72, considered to be a fair measure. The final model has been uploaded for use on SpineSage.com.
Conclusions:
We present a validated model for predicting SSI after spine surgery. The value in this model is that it gives the user an absolute percent likelihood of SSI after spine surgery based on the patient's comorbidity profile and invasiveness of surgery. Patients are far more likely to understand an absolute percentage, rather than relative risk and confidence interval values. A model such as this is of paramount importance in counseling patients and enhancing the safety of spine surgery. In addition, a tool such as this can be of great use particularly as health care trends toward pay for performance, quality metrics (such as SSI), and risk adjustment. To facilitate the use of this model, we have created a Web site (SpineSage.com) where users can enter patient data to determine likelihood for SSI.
