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

Abstract：

OBJECTIVES: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 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. RESULTS: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). 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.

journal_name

J Clin Epidemiol

journal_title

Journal of clinical epidemiology

authors

van Walraven C,Jackson TD,Daneman N

doi

10.1016/j.jclinepi.2016.05.010

subject

Has Abstract

pub_date

2016-09-01 00:00:00

pages

112-117

eissn

0895-4356

issn

1878-5921

pii

S0895-4356(16)30148-2

journal_volume

77

pub_type

杂志文章

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