Predicting XXXX after vascular surgery: a model based on pre- and intraoperative data.

@entity1 @entity146 @entity190 @entity260 @entity432 @entity933
OBJECTIVE: The aim of the study was to determine pre- and intraoperative risk factors for the development of @entity260 among @entity1 undergoing aortic, carotid, and peripheral vascular surgery to predict the risk for @entity260 . SUMMARY BACKGROUND DATA: Although @entity260 after vascular surgery is a frequent complication and is associated with the need for more inpatient hospital care and longer length of hospital stay, little is known about risk factors for @entity933 in @entity1 undergoing vascular surgery. METHODS: Pre-, intra-, and postoperative data were prospectively collected, including the first 7 postoperative days with daily follow-up by a surgeon and a psychiatrist of 153 @entity1 undergoing elective vascular surgery. @entity933 (Diagnostic and statistical Manual of Mental Disorders IV) was diagnosed by the psychiatrist. Multivariate linear logistic regression and a cross validation analysis were performed to find a set of parameters to predict @entity260 . RESULTS: Sixty @entity1 (39.2%) developed @entity260 . The best set of predictors included the absence of @entity432 and hypercholesterinemia, history of a major amputation, age over 65 years, a body size of less than 170 cm, preoperative @entity146 parameters and intraoperative parameters correlated to increased @entity190 . The combination of these parameters allows the estimation of an individual @entity1 ' risk for @entity260 already at the end of vascular surgery with an overall accuracy of 69.9%. CONCLUSIONS: @entity260 after vascular surgery is a frequent complication. A model based on pre- and intraoperative somatic and @entity146 risk factors allows prediction of the @entity1 's risk for developing @entity260 .
