A risk factor-based predictive model of outcomes in carotid endarterectomy: the National Surgical Quality Improvement
Kimon Bekelis1, Samuel F Bakhoum1, Atman Desai1
1Section of Neurosurgery (K.B., A.D.), Department of Medicine (T.A.M.), and Section of Vascular Surgery (P.G.), Dartmouth-Hitchcock Medical Center, Lebanon, NH; Geisel School of Medicine at Dartmouth (S.F.B.), Hanover, NH; and Section of Vascular Surgery, State University of New York at Stony Brook, Stony Brook, NY (N.L.).
Background And Purpose:
Accurate knowledge of individualized risks and benefits is crucial to the surgical management of patients undergoing carotid endarterectomy (CEA). Although large randomized trials have determined specific cutoffs for the degree of stenosis, precise delineation of patient-level risks remains a topic of debate, especially in real world practice. We attempted to create a risk factor-based predictive model of outcomes in CEA.
Methods:
We performed a retrospective cohort study involving patients who underwent CEAs from 2005 to 2010 and were registered in the American College of Surgeons National Quality Improvement Project database.
Results:
Of the 35 698 patients, 20 015 were asymptomatic (56.1%) and 15 683 were symptomatic (43.9%). These patients demonstrated a 1.64% risk of stroke, 0.69% risk of myocardial infarction, and 0.75% risk of death within 30 days after CEA. Multivariate analysis demonstrated that increasing age, male sex, history of chronic obstructive pulmonary disease, myocardial infarction, angina, congestive heart failure, peripheral vascular disease, previous stroke or transient ischemic attack, and dialysis were independent risk factors associated with an increased risk of the combined outcome of postoperative stroke, myocardial infarction, or death. A validated model for outcome prediction based on individual patient characteristics was developed. There was a steep effect of age on the risk of myocardial infarction and death.
Conclusions:
This national study confirms that that risks of CEA vary dramatically based on patient-level characteristics. Because of limited discrimination, it cannot be used for individual patient risk assessment. However, it can be used as a baseline for improvement and development of more accurate predictive models based on other databases or prospective studies.