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

ObjectivesElderly 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 SettingAll people exceeding 65 years undergoing surgery from two hospitals with known SSI status were linked to population-based administrative data sets in Ontario, Canada. We used bootstrap methods to create a multivariate model that used health administrative data to predict the probability of SSI.ResultsOf 3,436 patients, 177 (5.1%) had an SSI. The Elderly SSI Risk Model included six covariates: number of distinct physician fee codes within 30 days of surgery; presence or absence of a postdischarge 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).ConclusionHealth 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.