ArticleFrontiers in surgery2025
Analysis of risk factors and linear prediction model construction for prolonged mechanical ventilation after Stanford A-type aortic dissection.
Article in Frontiers in surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Machine learning approaches for risk prediction in aortic dissection: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Review
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5 authors.
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Abstract
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 7:3 ratio. 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 ( 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.
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