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



All 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.



Of 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).



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.
Ottawa Hospital Research Institute, University of Ottawa, ASB1-003 1053, Carling Ave, Ottawa, Ontario K1Y 4E9, Canada; Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, Ontario M4N 3M5, Canada. Electronic address: carlv@ohri.ca.
Carl
C
van Walraven
University Health Network, 190 Elizabeth St., Toronto, Ontario M5G 2C4, Canada.
Timothy D
TD
Jackson
Institute for Clinical Evaluative Sciences, 2075 Bayview Ave, Toronto, Ontario M4N 3M5, Canada; Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Ave, Toronto, Ontario M4N 3M5, Canada.
Nick
N
Daneman
D000368:Aged; D000369:Aged, 80 and over; D003428:Cross Infection / Q000453:epidemiology*; D066264:Datasets as Topic; D005260:Female; D015577:Geriatric Assessment / Q000379:methods / Q000706:statistics & numerical data*; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data*; D008297:Male; D009864:Ontario / Q000453:epidemiology; D011336:Probability; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods / Q000706:statistics & numerical data; D012307:Risk Factors; D013530:Surgical Wound Infection / Q000453:epidemiology*
Bootstrap; Data mining; Health administrative data; Multivariable modeling; Risk; Surgical site infections