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PREDICTIVE MODEL OF SUCCESS FOR EVAR
Author(s) -
Anderson J. L.,
Fitridge R.,
Boult M.,
Barnes M.,
Maddern G.
Publication year - 2007
Publication title -
anz journal of surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.426
H-Index - 70
eISSN - 1445-2197
pISSN - 1445-1433
DOI - 10.1111/j.1445-2197.2007.04134_1.x
Subject(s) - medicine , audit , logistic regression , stepwise regression , statistician , surgery , accounting , pathology , business
Purpose To provide an overview of a predictive model of success that has been developed using the five‐year results from the audit of endovascular aneurysm repair (EVAR). Methodology Preoperative and operative EVAR information was collected from surgeons for procedures performed between November 1999 and May 2001. Annual follow‐up has continued with a view to examining mid to long‐term safety and effectiveness. Data was linked through the National Death Index to obtain accurate mortality information. A statistician applied generalised linear models (logistic regressions) on the data to predict measures of success. Stepwise forward logistic regressions were used to select which of the preoperative patient variables were included in each success measure model. Using this analysis, an interactive Microsoft Excel program was designed to help surgeons to evaluate the predicted likelihood of success of the procedure. Results Eight predictor variables were used to assess relationships with various measure of success. Measures included technical success, likelihood of re‐interventions, graft complications, migration, conversion to open, rupture, endoleak, mortality and survival. Copies of the model (Excel spreadsheet) were circulated to members of the audit reference group and other specialist vascular surgeons for comment. Clinical feedback was used to further refine the model and improve its utility. Conclusions The predictive model is available to vascular surgeons through the RACS website. It was developed as an aid for surgeons and patients to decide treatment options. Surgeons and patients can discuss patient’s likely outcomes (e.g. complications and survival likelihood) to better inform the EVAR decision.