Development and validation of a perioperative prediction model for in-hospital mortality after surgery for type A aortic dissection

Type A aortic dissection (TAAD) remains a life-threatening cardiovascular emergency with substantial risk of postoperative mortality. Accurate perioperative risk stratification is essential to guide clinical decision-making and optimize patient outcomes. To develop, internally validate, and visualize a robust perioperative nomogram for predicting in-hospital mortality in patients undergoing surgical repair for TAAD. We conducted a retrospective cohort study of 998 patients who underwent open surgical repair for TAAD between May 2020 and April 2025. LASSO regression was performed for variable selection, followed by multivariable logistic regression to construct the predictive model. A visual nomogram was then developed based on the independent predictors. The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC) for discrimination, and calibration curves with the Hosmer–Lemeshow test. Internal validation and optimism correction of the nomogram were performed using bootstrap resampling with 1,000 iterations. The overall in-hospital mortality rate was 4.3% (43/998). In the multivariable model, inter-hospital transfer (OR = 3.86, 95% CI: 1.91–7.82), lower limb malperfusion (OR = 3.09, 95% CI: 1.32–6.96), and chest pain were identified as independent risk factors for in-hospital mortality. Conversely, admission hypertension (OR = 0.20, 95% CI: 0.08–0.48) and the presence of collateral circulation served as significant independent protective factors. The derived nomogram demonstrated strong discrimination, with a bias-corrected AUC of 0.824 (95% CI: 0.752–0.897) and exhibited robust calibration. We established and internally validated a parsimonious perioperative nomogram for TAAD surgical mortality. Our findings highlight that inter-hospital transfer and lower limb malperfusion are critical risk factors, while admission hypertension and collateral circulation are protective. This intuitive, paper-based nomogram provides clinicians with a practical tool for individualized, bedside risk estimation, though future external validation is warranted.

Authors

Institutions

Publication Details

Journal
Journal of Cardiothoracic Surgery
Published
2026-10-16
DOI
https://doi.org/10.1186/s13019-026-04713-6
Primary Topic
Aortic Disease and Treatment Approaches
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development and validation of a perioperative prediction model for in-hospital mortality after surgery for type A aortic dissection

CongCong Zhang, Gang Wang, LiLi Wei, YanPing Wan et al.
Journal of Cardiothoracic Surgery
Aortic Disease and Treatment Approaches
article

Development and validation of a perioperative prediction model for in-hospital mortality after surgery for type A aortic dissection

CongCong Zhang, Gang Wang, LiLi Wei, YanPing Wan, Cui Liu, ZongBi Wu, Meng Ying, Ao Li, Youli Jiang
article en

Abstract

Type A aortic dissection (TAAD) remains a life-threatening cardiovascular emergency with substantial risk of postoperative mortality. Accurate perioperative risk stratification is essential to guide clinical decision-making and optimize patient outcomes. To develop, internally validate, and visualize a robust perioperative nomogram for predicting in-hospital mortality in patients undergoing surgical repair for TAAD. We conducted a retrospective cohort study of 998 patients who underwent open surgical repair for TAAD between May 2020 and April 2025. LASSO regression was performed for variable selection, followed by multivariable logistic regression to construct the predictive model. A visual nomogram was then developed based on the independent predictors. The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC) for discrimination, and calibration curves with the Hosmer–Lemeshow test. Internal validation and optimism correction of the nomogram were performed using bootstrap resampling with 1,000 iterations. The overall in-hospital mortality rate was 4.3% (43/998). In the multivariable model, inter-hospital transfer (OR = 3.86, 95% CI: 1.91–7.82), lower limb malperfusion (OR = 3.09, 95% CI: 1.32–6.96), and chest pain were identified as independent risk factors for in-hospital mortality. Conversely, admission hypertension (OR = 0.20, 95% CI: 0.08–0.48) and the presence of collateral circulation served as significant independent protective factors. The derived nomogram demonstrated strong discrimination, with a bias-corrected AUC of 0.824 (95% CI: 0.752–0.897) and exhibited robust calibration. We established and internally validated a parsimonious perioperative nomogram for TAAD surgical mortality. Our findings highlight that inter-hospital transfer and lower limb malperfusion are critical risk factors, while admission hypertension and collateral circulation are protective. This intuitive, paper-based nomogram provides clinicians with a practical tool for individualized, bedside risk estimation, though future external validation is warranted.

Journal of Cardiothoracic Surgery
Qingdao University (CN), Guangzhou University of Chinese Medicine (CN), Affiliated Hospital of Qingdao University (CN), Second Xiangya Hospital of Central South University (CN), Shenzhen Third People’s Hospital (CN), University of South China (CN)
Good health and well-being
Openalex Percentile: Top 13%
Aortic Disease and Treatment Approaches
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.