A nomogram prediction model for acute Stanford type A aortic dissection
10.3760/cma.j.cn112434-20230619-00126
- VernacularTitle:急性Stanford A型主动脉夹层的列线图预测模型
- Author:
Meng WANG
1
;
Qingliang CHEN
;
Yunpeng BAI
;
Tongyun CHEN
Author Information
1. 天津市胸科医院心外科，天津 300222
- Keywords:
Aortic dissection;
Risk factors;
Nomogram
- From:
Chinese Journal of Thoracic and Cardiovascular Surgery
2024;40(2):100-104
- CountryChina
- Language:Chinese
-
Abstract:
Objective:This study was conducted to investigate the independent risk factors for predicting the occurrence of acute Stanford type A aortic dissection(TAAD), and to construct a nomogram model for predicting the occurrence of TAAD.Methods:The clinical data of patients meeting the diagnostic criteria for TAAD admitted to Tianjin Chest Hospital from June 2016 to December 2021 and healthy people examined by the physical examination center of Tianjin Chest Hospital during the same period were retrospectively collected, and the independent risk factors for TAAD were predicted by propensity matching analysis. Univariate and multivariate Logistic regression were used to analyze the variables with statistical differences, and a nomogram model was constructed to predict the occurrence of TAAD disease according to the screened risk factors. Results:A total of 148 patients in the TAAD group and 5 690 patients in the control group were collected. After bias matching analysis, 148 pairs were successfully matched. Multivariate Logistic regression analysis was performed on the matching results. The results showed that hypertension(HBP), diabetes mellitus(T2DM), Lp(a), very low density lipoprotein(VLDL) and apolipoprotein A1/B(ApoA1/B) were independent risk factors for the development of TAAD. HBP, Lp(a) and ApoA1/B were pathogenic factors( OR 7.267, 1.010 and 2.199, P<0.05, respectively), while T2DM and VLDL were protective factors( OR 0.173 and 0.139, P<0.05). Based on the independent risk factors obtained by multi-factor Logistic regression analysis, a nomogram model of TAAD incidence was constructed. The area under ROC curve( AUC) for predicting the onset of TAAD was 81.6%(95% CI: 0.766-0.863), and the internal calibration curve was close to the standard curve. Conclusion:This model has a good degree of differentiation and calibration, which is helpful for clinicians to guide healthy people to prevent the occurrence of TAAD and provide a theoretical basis for the prevention of TAAD.
