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Published on: September 19, 2018
Description of a risk predictive model of 30-day postoperative mortality after elective abdominal aortic aneurysm
Mohammad H Eslami1, Denis V Rybin2, Gheorghe Doros2
1Division of Vascular Surgery, Department of Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pa.
Objective:
Despite vast improvement in the field of vascular surgery, elective abdominal aortic aneurysm (AAA) repair still leads to perioperative death. Patients with asymptomatic AAAs, therefore, would benefit from an individual risk assessment to help with decisions regarding operative intervention. The purpose of this study was to describe such a 30-day postoperative (POD) risk prediction model using American College of Surgeons National Surgical Quality Improvement Project (NSQIP) data.
Methods:
The NSQIP database (2005-2011) was queried for patients undergoing elective AAA repair using open or endovascular techniques. Clinical variables and known predictors of mortality were included in a full prediction model. These variables included procedure type, patient's age, functional dependence and comorbidities, and surgeon's specialty. Backward elimination with alpha-level of 0.2 was used to construct a parsimonious model. Model discrimination was evaluated in equally sized risk quintiles.
Results:
The overall mortality rate for 18,917 elective AAA patients was 1.7%. In this model, surgeon's specialty was not predictive of POD. The most significant factors affecting POD included open repair (odds ratio [OR], 2.712; 95% confidence interval [CI], 2.119-3.469; P < .001), age >70 (OR, 2.243; 95% CI, 1.695-3.033; P < .001), functional dependency (OR, 2.290; 95% CI, 1.442-3.637; P < .001), creatinine above 2.0 mg/dL (OR, 2.1; 95% CI, 1.403-3.142; P < .001) and low hematocrit levels (OR, 2.157; 95% CI, 1.365-3.408; P = .001).The discriminating ability of the NSQIP model was reasonable (C-statistic = 0.751) and corrected to 0.736 after internal validation. The NSQIP model performed well predicting mortality among risk-group quintiles.
Conclusions:
The NSQIP risk prediction model is a robust vehicle to predict POD among patient undergoing elective AAA repair. This model can be used for risk stratification of patients undergoing elective AAA repair.
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