Abstract
The incidence of abdominal aortic aneurysm (AAA) is very high, but there is no risk assessment model for early identification of AAA in clinic. The aim of this study was to develop a nomogram risk assessment model for predicting AAA. The data of 280 patients diagnosed as AAA and 385 controls in The Affiliated Suzhou Hospital of Nanjing Medical University were retrospectively reviewed. The LASSO regression method was applied to filter variables, and multivariate logistic regression was used to construct a nomogram. The discriminatory ability of the model was determined by calculating the area under the curve (AUC). The calibration capability of the model is evaluated by using bootstrap (resampling = 1000) internal validation and Hosmer-Lemeshow test. The clinical utility and clinical application value were evaluated by decision curve analysis (DCA) and clinical impact curve (CIC). In addition, a retrospective review of 133 AAA patients and 262 controls from The First Affiliated Hospital of Soochow University was performed as an external validation cohort. Eight variables are selected to construct the nomogram of AAA risk assessment model. The nomogram predicted AAA with AUC values of 0.928 (95%CI, 0.907-0.950) in the training cohort, and 0.902 (95%CI, 0.865-0.940) in the external validation cohort, the risk prediction model has excellent discriminative ability. The calibration curve and Hosmer-Lemeshow test proved that the nomogram predicted outcomes were close to the ideal curve, the predicted outcomes were consistent with the real outcomes, the DCA curve and CIC curve showed that patients could benefit. This finding was also confirmed in the external validation cohort. In this study, a nomogram was constructed that incorporated eight demographic and clinical characteristics of AAA patients, which can be used as a practical approach for the personalized early screening and auxiliary diagnosis of the potential risk factors.