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ORIGINAL RESEARCH article

Front. Surg.

Sec. Cardiovascular Surgery

Volume 12 - 2025 | doi: 10.3389/fsurg.2025.1697977

Analysis of risk factors and linear prediction model construction for prolonged mechanical ventilation after Stanford A-type aortic dissection

Provisionally accepted
Jiajie  YuJiajie Yu1*Hongmei  LianHongmei Lian2Ting  ChenTing Chen2Yali  WangYali Wang1*Yanlin  WeiYanlin Wei1
  • 1North Sichuan Medical College, Nanchong, China
  • 2Affiliated Hospital of North Sichuan Medical College, Nanchong, China

The final, formatted version of the article will be published soon.

Objective: To explore the risk factors for prolonged acute ventilation time after Stanford type A aortic dissection and to construct a nomogram prediction model. Methods: A total of 178 patients with Stanford type A aortic dissection admitted to the Department of Cardiac and Vascular Surgery of the Affiliated Hospital of North Sichuan Medical College from 2020 to 2024 were retrospectively enrolled. The patients were randomly divided into a modeling group (124 cases) and a validation group (54 cases) at a 7:3ratio . Risk factors for prolonged mechanical ventilation time after surgery were analyzed using univariate and multivariate logistic regression analysis, and a risk prediction model was constructed based on the results of multivariate logistic regression analysis. Results: Multivariate logistic regression analysis showed that age, body mass index, preoperative oxygenation index, cardiopulmonary bypass time, and postoperative serum creatinine were risk factors for prolonged mechanical ventilation time after Stanford type A aortic dissection (p<0.05).A risk prediction model was constructed based on these findings. The area under the ROC curve was 0.91 (95%CI: 0.86-0.97), with an accuracy of 0.88 (95%CI: 0.81-0.93), sensitivity of 0.92 (95%CI: 0.86-0.98), specificity of 0.82 (95%CI: 0.71-0.92), and an optimal cut-off value of 0.527. The results of model validation showed that the area under the ROC curve was 0.79 (95%CI: 0.66-0.92), with an accuracy of 0.72 (95%CI: 0.58-0.84), sensitivity of 0.77 (95%CI: 0.64-0.90), specificity of 0.6 (95%CI: 0.35-0.85). Conclusion The prediction model for prolonged mechanical ventilation time in patients with Stanford type A aortic dissection has a good prediction effect and is convenient for clinical use, providing a reference for medical workers to take preventive treatment.

Keywords: aortic dissection, mechanical ventilation, predictive models, Risk factors, heart surgery

Received: 04 Sep 2025; Accepted: 20 Oct 2025.

Copyright: © 2025 Yu, Lian, Chen, Wang and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Jiajie Yu, 1138703971@qq.com
Yali Wang, 3924614048@qq.com

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