Prognostic Nomogram for Patients with Hostile Neck Anatomy after Endovascular Abdominal Aortic Aneurysm Repair

Since the introduction of endovascular aortic aneurysm repair (EVAR), the morphology of aneurysm neck has a great impact on prognosis of patients who underwent elective abdominal aortic aneurysm (AAA) repair. In this study, we aimed to analyze the morphologic features and prognosis after EVAR for patients with hostile neck anatomy and tried to create a novel prognostic nomogram in predicting EVAR-related adverse events.

We retrospectively reviewed 812 patients with infra-renal AAA who underwent elective EVAR procedures between January 2010 and December 2015 at our single center and identified patients with hostile neck. Univariable and multivariable analyses were performed to determine the significant prognostic factors for EVAR-related adverse events, which were then integrated to build a nomogram. The model was subjected to bootstrap resamples for internal validation. The discriminative ability was presented with calibration plots and measured by concordance index (C-index) and area under the curve (AUC) from receiver-operating characteristic curve.

The overall EVAR-related adverse events rate for 323 patients with hostile neck was 12.1%. By multivariable analysis, significant risk factors of adverse events included female (odds ratio [OR], 2.58; 95% confidence interval [CI], 1.18-5.62; P = 0.017), conical neck (OR, 4.66; 95% CI, 1.5-14.51; P = 0.008), short neck (OR, 2.71; 95% CI, 1.49-4.94; P = 0.001), and angulated neck (OR, 3.26; 95% CI, 1.43-7.43; P = 0.005). A nomogram was developed based on the results of multivariable analysis. Calibration plots presented an excellent agreement between model predicted and actually observed risk of adverse events after internal validation. The discrimination ability of this risk predictive model was reasonable (C-index = 0.79; AUC = 0.81, 95% CI, 0.73-0.89).

EVAR is a feasible and safe treatment for most of patients with hostile neck. We developed and validated a novel model for predicting the risk of adverse events after EVAR and clarified high-risk patients.