A nomogram prediction model for short-term aortic-related adverse events in patients with acute Stanford type B aortic intramural hematoma: development and validation - Meng, Dujuan; Wang, Yasong; Zhou, Tienan; Gu, Ruoxi; Zhang, Zhiqiang; Zhao, Tinghao; He, Houlin; Min, Ying; Wang, Xiaozeng (Frontiers in Cardiovascular Medicine, 2024)
Background This study is to examine the factors associated with short-term aortic-related adverse events in patients with acute type B aortic intramural hematoma (IMH). Additionally, we develop a risk prediction nomogram model and evaluate its accuracy. Methods This study included 197 patients diagnosed with acute type B IMH. The patients were divided into stable group (n = 125) and exacerbation group (n = 72) based on the occurrence of aortic-related adverse events. Logistic regression and the Least Absolute Shrinkage and Selection Operator (LASSO) method for variables based on baseline assessments with significant differences in clinical and image characteristics were employed to identify independent predictors. A nomogram risk model was constructed based on these independent predictors. The nomogram model was evaluated using various methods such as the receiver operating characteristic curve, calibration curve, decision analysis curve, and clinical impact curve. Internal validation was performed using the Bootstrap method. Results A nomogram risk prediction model was established based on four variables: absence of diabetes, anemia, maximum descending aortic diameter (MDAD), and ulcer-like projection (ULP). The model demonstrated a discriminative ability with an area under the curve (AUC) of 0.813. The calibration curve indicated a good agreement between the predicted probabilities and the actual probabilities. The Hosmer-Lemeshow goodness of fit test showed no significant difference (χ2 = 7.040, P = 0.532). The decision curve analysis (DCA) was employed to further confirm the clinical effectiveness of the nomogram. Conclusion This study introduces a nomogram prediction model that integrates four important risk factors: ULP, MDAD, anemia, and absence of diabetes. The model allows for personalized prediction of patients with type B IMH.