Risk factors for perioperative mortality in acute aortic dissection and the construction of a Nomogram prediction model
- VernacularTitle:急性主动脉夹层围手术期死亡的危险因素及Nomogram预测模型的构建
- Author:
Tie DENG
1
,
2
;
Bangguo LI
1
;
Hong YU
1
Author Information
1. Department of Radiology, Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, 563003, Guizhou, P. R. China
2. Medical Imaging Department, Chongqing University Central Hospital, Chongqing, 400010, P. R. China
- Publication Type:Journal Article
- Keywords:
Acute aortic dissection;
computed tomography angiography;
perioperative mortality;
risk factors;
Nomogram;
Stanford type A aortic dissection;
logistic regression;
risk prediction
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2026;33(03):424-430
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the value of preoperative clinical data and computed tomography angiography (CTA) data in predicting perioperative mortality risk in patients with acute aortic dissection (AAD), and to construct a Nomogram prediction model. Methods A retrospective study was conducted on AAD patients treated at Affiliated Hospital of Zunyi Medical University from February 2013 to July 2023. Patients who died during the perioperative period were included in the death group, and those who improved during the same period were randomly selected as the non-death group using a random number table method. The first CTA data and preoperative clinical data within the perioperative period of the two groups were collected, and related risk factors were analyzed to screen out independent predictive factors for perioperative death. The Nomogram prediction model for perioperative mortality risk in AAD patients was constructed using the screened independent predictive factors, and the effect of the Nomogram was evaluated by calibration curves and area under the curve (AUC). Results A total of 270 AAD patients were included. There were 60 patients in the death group, including 42 males and 18 females with an average age of (56.89±13.42) years. There were 210 patients in the non-death group, including 163 males and 47 females with an average age of (56.15±13.77) years. Multivariate logistic regression analysis showed that type A AAD [OR=4.589, 95%CI (2.273, 9.267), P<0.001], irregular tear morphology [OR=2.054, 95%CI (1.025, 4.117), P=0.042], decreased hemoglobin [OR=0.983, 95%CI (0.971, 0.995), P=0.007], increased uric acid [OR=1.003, 95%CI (1.001, 1.005), P=0.004], and increased aspartate aminotransferase [OR=1.003, 95%CI (1.000, 1.006), P=0.035] were independent risk factors for perioperative death in AAD patients. The Nomogram prediction model constructed using the above risk factors had an AUC of 0.790 for predicting perioperative death, indicating good predictive performance. Conclusion Type A AAD, irregular tear morphology, decreased hemoglobin, increased uric acid, and increased aspartate aminotransferase are independent predictive factors for perioperative death in AAD patients. The Nomogram prediction model constructed using these factors can help assess the perioperative mortality risk of AAD patients.
