A clinical-deep learning model for prolonged length of stay following endovascular aortic repair
- 1Department of Vascular Surgery, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
- 2Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Guilin, China
Abstract
Background: Prolonged length of stay (PLOS) after endovascular aortic repair (EVAR) for abdominal aortic aneurysm (AAA) is associated with increased complications, resource burden, and patient distress. Early identification of high-risk patients remains challenging. This study aimed to develop and externally validate a predictive model integrating clinical variables and three-dimensional deep learning (3D-DL) features from preoperative computed tomography angiography (CTA) to predict PLOS after EVAR.
Methods: A total of 788 patients with AAA who underwent elective EVAR were enrolled from two hospitals. The internal training cohort consisted of 556 patients, and the external validation cohort included 232 patients. PLOS was defined as a LOS above the 75th percentile of the distribution. A clinical nomogram was developed using LASSO regression followed by multivariate logistic regression. Deep learning (DL) features were extracted from manually segmented computed tomography angiography (CTA) images using a 3D ResNet200 network, and LASSO regression was applied to select the most informative features. An ensemble model integrating the clinical predictors and the selected DL features was constructed. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis.
Results: LASSO combined with multivariate logistic regression identified five independent clinical predictors: age, hemoglobin, albumin, maximum aneurysm diameter, and diabetes. The clinical model achieved an area under the curve (AUC) of 0.917 in the internal cohort and 0.944 in the external cohort. From the 3D ResNet200 network, 32 DL features were extracted. LASSO regression further selected two of these features. The ensemble predictive model, which integrated the clinical predictors and DL features, demonstrated improved discrimination, with an AUC of 0.971 in the internal cohort and 0.958 in the external cohort. Internal validation with 1,000 bootstrap resamples yielded optimism-corrected AUCs of 0.911 for the clinical model and 0.966 for the ensemble model. Subgroup analyses demonstrated consistent model performance across age and diabetes strata, while performance in the female subgroup warrants further evaluation in larger cohorts.
Conclusion: This study developed an ensemble predictive model to predict PLOS following EVAR. The model demonstrated excellent discrimination and provided significant prognostic value beyond clinical variables alone. This nomogram may serve as a preoperative tool for risk stratification and resource planning in this population. Future studies should focus on prospective validation and clinical integration.
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BibTeX
@article{hu2026clinicaldeep,
title = {{A clinical-deep learning model for prolonged length of stay following endovascular aortic repair}},
author = {Changfu Hu and Tao Bai and Xiao Qin and Kangsui Tang and Tao Peng and Qingbo Yang and Minkang Jiang and Lin Zhang},
journal = {Frontiers in Cardiovascular Medicine},
year = {2026},
month = oct,
doi = {10.3389/fcvm.2026.1867707},
url = {https://doi.org/10.3389/fcvm.2026.1867707},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A clinical-deep learning model for prolonged length of stay following endovascular aortic repair
AU - Changfu Hu
AU - Tao Bai
AU - Xiao Qin
AU - Kangsui Tang
AU - Tao Peng
AU - Qingbo Yang
AU - Minkang Jiang
AU - Lin Zhang
PY - 2026
DA - 2026/10/12
JO - Frontiers in Cardiovascular Medicine
DO - 10.3389/fcvm.2026.1867707
UR - https://doi.org/10.3389/fcvm.2026.1867707
AB - Background: Prolonged length of stay (PLOS) after endovascular aortic repair (EVAR) for abdominal aortic aneurysm (AAA) is associated with increased complications, resource burden, and patient distress. Early identification of high-risk patients remains challenging. This study aimed to develop and externally validate a predictive model integrating clinical variables and three-dimensional deep learning (3D-DL) features from preoperative computed tomography angiography (CTA) to predict PLOS after EVAR. Methods: A total of 788 patients with AAA who underwent elective EVAR were enrolled from two hospitals. The internal training cohort consisted of 556 patients, and the external validation cohort included 232 patients. PLOS was defined as a LOS above the 75th percentile of the distribution. A clinical nomogram was developed using LASSO regression followed by multivariate logistic regression. Deep learning (DL) features were extracted from manually segmented computed tomography angiography (CTA) images using a 3D ResNet200 network, and LASSO regression was applied to select the most informative features. An ensemble model integrating the clinical predictors and the selected DL features was constructed. Model performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis. Results: LASSO combined with multivariate logistic regression identified five independent clinical predictors: age, hemoglobin, albumin, maximum aneurysm diameter, and diabetes. The clinical model achieved an area under the curve (AUC) of 0.917 in the internal cohort and 0.944 in the external cohort. From the 3D ResNet200 network, 32 DL features were extracted. LASSO regression further selected two of these features. The ensemble predictive model, which integrated the clinical predictors and DL features, demonstrated improved discrimination, with an AUC of 0.971 in the internal cohort and 0.958 in the external cohort. Internal validation with 1,000 bootstrap resamples yielded optimism-corrected AUCs of 0.911 for the clinical model and 0.966 for the ensemble model. Subgroup analyses demonstrated consistent model performance across age and diabetes strata, while performance in the female subgroup warrants further evaluation in larger cohorts. Conclusion: This study developed an ensemble predictive model to predict PLOS following EVAR. The model demonstrated excellent discrimination and provided significant prognostic value beyond clinical variables alone. This nomogram may serve as a preoperative tool for risk stratification and resource planning in this population. Future studies should focus on prospective validation and clinical integration.
SN - 2297-055X
ER -