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. 2024 Feb 29;24(1):129.
doi: 10.1186/s12872-024-03766-6.

Risk factors for one-year mortality following discharge in patients with acute aortic dissection: development and validation of a predictive model in a cross-sectional study

Ting Zhou #  1 , Jing-Xiao Li #  2 , Chao-Yong Zhang  3 , Yu-Gui Li  4 , Jun Peng  4 , Chun-Lou Wei  1 , Meng-Hua Chen  5 , Hua-Fu Zhou  6
Affiliations

Risk factors for one-year mortality following discharge in patients with acute aortic dissection: development and validation of a predictive model in a cross-sectional study

Ting Zhou et al. BMC Cardiovasc Disord. .

Abstract

Purpose: This study was aimed to identify the risk factors that influence the mortality risk in patients with acute aortic dissection (AAD) within one year after discharge, and aimed to construct a predictive model for assessing mortality risk.

Methods: The study involved 320 adult patients obtained from the Medical Information Mart for Intensive Care (MIMIC) database. Logistic regression analysis was conducted to identify potential risk factors associated with mortality in AAD patients within one year after discharge and to develop a predictive model. The performance of the predictive model was assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). To further validate the findings, patient data from the First Affiliated Hospital of Guangxi Medical University (157 patients) were analyzed.

Results: Univariate and multivariate logistic regression analyses revealed that gender, length of hospital stay, highest blood urea nitrogen (BUN_max), use of adrenaline, and use of amiodarone were significant risk factors for mortality within one year after discharge (p < 0.05). The constructed model exhibited a consistency index (C-index) and an area under the ROC curve of 0.738. The calibration curve and DCA demonstrated that these indicators had a good degree of agreement and utility. The external validation results of the model also indicated good predictability (AUC = 0.700, p < 0.05).

Conclusion: The personalized scoring prediction model constructed by gender, length of hospital stays, BUN_max levels, as well as the use of adrenaline and amiodarone, can effectively identify AAD patients with high mortality risk within one year after discharge.

Keywords: Acute aortic dissection; Public database; Risk prediction model.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Study enrollment flowchart
Fig. 2
Fig. 2
Nomogram constructed based on potential risk factors
Fig. 3
Fig. 3
ROC (A), calibration curve (B), and DCA curve (C) for internal validation of nomogram
Fig. 4
Fig. 4
ROC and calibration curve for external validation
See this image and copyright information in PMC

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