Development of a surgical site infection prediction model in orthopaedic trauma: The Denver Health Model.

BACKGROUND

The CDC's National Healthcare Safety Network's (NHSN) current risk adjustment model for surgical site infections (SSI) following open reduction internal fixation (ORIF) of long bone fractures is a suboptimal predictor of risk. We hypothesized that by including variables known to be associated with SSI following ORIF, we would develop a model that would increase the accuracy and predictability of SSI risk.

METHODS

Patients who underwent ORIF of a long bone between January 1, 2012 and December 31, 2014 were included in the study (n=1543). Patient risk factors, injury risk factors and perioperative risk factors were considered in the development of this model. We developed a risk prediction model for SSI following ORIF and then applied this to a new dataset of ORIF to determine the expected number of infections. This was compared to the expected number of infections calculated using the NHSN risk adjusted model.

RESULTS

The final multivariate model included age (odds ratio: 1.02, p-value<0.001, 95% confidence interval: 1.00-1.04), lower leg fracture (2.63, 0.004, 1.40-4.93), open fracture (1.87, 0.07, 0.93-3.76), American Society of Anesthesiologists (ASA) (2.09, 0.02, 1.07-4.08) and history of methicillin-resistant Staphylococcus aureus (MRSA), which was the most important predictor of infection (7.20, <0.001, 2.61-19.85). The c-index was 0.74 compared to 0.65 for the NHSN model, indicating that our model more accurate in estimating infection risk. When the developed model was used to predict the number of expected infections on a new dataset from 2015, 36.3 SSI were expected compared to 5.7 calculated by the NHSN model.

CONCLUSIONS

The model that was developed uses five easily identifiable risk factors that result in a more accurate prediction of infection at our facility than the currently used model.

LEVEL OF EVIDENCE

Prognostic and epidemiologic study, level III.