Administrative data measured surgical site infection probability within 30 days of surgery in elderly patients

Objective — Elderly patients are inordinately affected by surgical site infections (SSIs). This study derived and internally validated a model that used routinely collected health administrative data to measure the probability of SSI in elderly patients within 30-days of surgery.

Study Design and Setting — All people exceeding 65 years undergoing surgery from two hospitals with known SSI status were linked to population-based administrative datasets in Ontario, Canada. We used bootstrap methods to create a multivariate model that used health administrative data to predict the probability of SSI.

Results — Of 3436 patients, 177 (5.1%) had an SSI. The Elderly SSI Risk Model included 6 covariates: number of distinct physician fee codes within 30 days of surgery; presence or absence of a post-discharge prescription for an antibiotic; presence or absence of three diagnostic codes; and a previously derived score that gauged SSI risk based on procedure codes. The model was highly explanatory (Nagelkerke’s R² 0.458), strongly discriminative (c-statistic 0.918), and well calibrated (calibration slope 1).

Conclusion — Health administrative data can effectively determine 30-day risk of SSI risk in elderly patients undergoing a broad assortment of surgeries. External validation is necessary before this can be routinely used to monitor SSIs in the elderly.