ArticleFrontiers in cardiovascular medicine2024
A nomogram prediction model for short-term aortic-related adverse events in patients with acute Stanford type B aortic intramural hematoma: development and validation.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Prognostic value of initial hematoma area in patients with acute type B aortic intramural hematoma.Quantitative imaging in medicine and surgery · 2026Article
- Association between atherosclerotic index of plasma and long-term aortic-related adverse events in type B aortic dissection patients undergoing thoracic endovascular aortic repair.Cardiovascular diabetology · 2026Article
- Development and Validation of the Systemic Inflammatory Response Index-Based Nomogram for Predicting Short-Term Adverse Events in Patients With Acute Uncomplicated Type B Aortic Intramural Hematoma.Journal of inflammation research · 2025Article
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Authors and funding
9 authors.
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Abstract
Background: This study is to examine the factors associated with short-term aortic-related adverse events in patients with acute type B aortic intramural hematoma (IMH). Additionally, we develop a risk prediction nomogram model and evaluate its accuracy. Methods: This study included 197 patients diagnosed with acute type B IMH. The patients were divided into stable group ( Results: A nomogram risk prediction model was established based on four variables: absence of diabetes, anemia, maximum descending aortic diameter (MDAD), and ulcer-like projection (ULP). The model demonstrated a discriminative ability with an area under the curve (AUC) of 0.813. The calibration curve indicated a good agreement between the predicted probabilities and the actual probabilities. The Hosmer-Lemeshow goodness of fit test showed no significant difference ( Conclusion: This study introduces a nomogram prediction model that integrates four important risk factors: ULP, MDAD, anemia, and absence of diabetes. The model allows for personalized prediction of patients with type B IMH.
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