365. Development of a surgical site infection risk predictor model for open reduction internal fixation of long bone surgeries
Session: Poster Abstract Session: HAI: Surgical Site Infections
Thursday, October 8, 2015
Room: Poster Hall
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 sub-optimal predictor of risk. The development of the model only includes factors such as age, procedure duration and hospital size. Exclusion of numerous critical risk factors known to be associated with SSI may contribute to inaccuracies in the current CDC model. We hypothesized that by including variables known to be associated with SSI following ORIF of long bone fractures, we would increase the accuracy and predictability of our model.

Methods: Patients who underwent ORIF of long bone between January 1, 2012 and December 31, 2013 were included in the study (n = 1163). Patient risk factors, injury risk factors, perioperative risk factors and postoperative risk factors were included in the development of this model. We performed univariate analysis to assess each variable’s association with infection. Risk factors meeting a p-value cut-off <0.30 were considered for inclusion in a multivariate logistic regression model. Potential confounding and multicollinearity were investigated. Once the model was developed, it was applied to a novel dataset of ORIF procedures 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: 1.2, p-value: 0.16, 95% confidence interval: 0.9 – 1.5), ASA (0.6, 0.03, 1.1 – 6.3), length of stay post-operatively (1.0, 0.31, 1.0 – 1.02), and history of MRSA, which was the most important predictor of infection (10.7, <0.001, 1.0 – 27.9). The c-index was 0.795 compared to the c-index of the NHSN model as 0.64, indicating that our model was superior in estimating infection risk. When the developed model was used to predict the number of expected infections on the 2014 data, 17.2 SSI were expected compared to 5.6 calculated by the NHSN model.  

Conclusion: The model that was developed uses 4 easily identifiable risk factors that result in a more accurate prediction of infection than the currently used model. Future direction would include applying this model to other facilities to test the fit and accuracy.

Sara Reese, PhD, CIC1, Bryan Knepper, MPH, MSc, CIC1, Cyril Mauffrey, MD, FACS, FRCS2, Connie Price, MD3 and Heather Young, MD3, (1)Patient Safety and Quality, Denver Health Medical Center, Denver, CO, (2)Department of Orthopedic Surgery, Denver Health Medical Center, Denver, CO, (3)Infectious Diseases, Denver Health Medical Center, Denver, CO

Disclosures:

S. Reese, None

B. Knepper, None

C. Mauffrey, None

C. Price, None

H. Young, None

Findings in the abstracts are embargoed until 12:01 a.m. PDT, Wednesday Oct. 7th with the exception of research findings presented at the IDWeek press conferences.