We examined predictors of high and low cost for abdominal aortic aneurysm (AAA) repair in a normally distributed sample of non-ruptured patients who could have undergone either procedure.
Method:
Aneurysm patients were grouped into OPEN repair or EVAR repair subgroups based on the procedure used. For each subgroup, a multiple regression using numerous risk factors was performed to identify statistical significance in estimating total cost in each subgroup. Two models were applied to each subgroup. We compared predicted cost of each model on patients who underwent OPEN repair, on patients who underwent EVAR, and on what would happen if OPEN repair patients underwent EVAR, or vice versa.
Result:
EVAR totaled approximately $7,000 more than OPEN repair, on the average. For the OPEN subgroup (n=230), the factors that proved to be statistically significant in explaining variation in total cost(p < .10) were age, history of COPD, and transfer to another hospital or rehab center. Bypass was used initially but later dropped because of marginal significance. Estimated coefficients were used as factor weightings to specify our OPEN model. For the EVAR subgroup(n=158), factors that showed significance were use of betablockers, creatinine levels > 1.45 mg/dl, history of iliac aneurysm, and ejection fraction < 30%. Estimated coefficients were used as factor weightings to specify our EVAR model.
Of the 230 patients that had OPEN procedure, 53 had a predicted EVAR cost lower than the predicted OPEN cost. Of these 53, 17 patients were in the lower half of actual OPEN costs, but 36 were in the higher half of actual costs. On the other hand, when both models were applied to the 158 EVAR patients, our predictive models suggested that 95 of these patients had a lower predicted OPEN cost than the predicted EVAR cost. 52 of the 95 patients were in the lower half of actual costs and 43 of the 95 patients were in the upper half of actual EVAR costs.
Conclusion:
This approach suggests that cost-predictive methodology could be used prospectively for clinicians seeing new patients in a way that optimizes cost-efficiency.