Background: For acute type-A aortic dissection (ATAAD) surgery, early post-surgery assessment is crucially important for effective treatment plans, underscoring the need for a framework to identify the risk level of aortic dissection cases. We examined true-lumen narrowing during follow-up examinations, collected morphological data 14 days (early stages) after surgery, and assessed patient risk levels over 2.8 years. Purpose: To establish an implementable framework supported by mathematical techniques to predict the risk of aortic dissection patients experiencing true-lumen narrowing after ATAAD surgery. Materials and Methods: This retrospective study analyzed CT data from 21 ATAAD patients. Forty uniformly distributed cross-sectional shapes (CSSs) are derived from each lumen to account for gradual changes in shape. We introduced the form factor (FF) to assess CSS morphology. Linear discriminant analysis (LDA) is used for the risk classification of aortic dissection patients. Leave-one-patient-out cross-validation (LOPO-CV) is used for risk prediction. Results: For this investigation, we examined data of 21 ATAAD patients categorized into high-risk, medium-risk, and low-risk cases based on clinical observations of the range of true-lumen narrowing. Our risk classification machine-learning (ML) model preserving the model's generalizability. The model's predictions reliably identified low-risk patients, thereby potentially reducing hospital visits. It also demonstrated proficiency in accurately predicting the risk for all high-risk patients. Conclusion: The suggested method anticipates the risk linked to aortic enlargement in patients with a narrowing true lumen in the early stage following ATAAD surgery, thereby aiding follow-up doctors in enhancing patient care.

翻译：背景：对于急性A型主动脉夹层（ATAAD）手术，术后早期评估对制定有效治疗方案至关重要，这突显了建立主动脉夹层病例风险等级识别框架的必要性。我们通过随访检查观察真腔狭窄情况，收集术后14天（早期）的形态学数据，并对患者进行为期2.8年的风险评估。目的：建立一种基于数学技术的可实施框架，用于预测ATAAD术后患者发生真腔狭窄的风险。材料与方法：本回顾性研究分析了21例ATAAD患者的CT数据。从每个管腔中提取40个均匀分布的横截面形状（CSS），以反映形状的渐进变化。引入形状因子（FF）评估CSS形态。采用线性判别分析（LDA）对主动脉夹层患者进行风险分类，并采用留一患者交叉验证（LOPO-CV）进行风险预测。结果：本研究分析了21例ATAAD患者的数据，根据真腔狭窄范围的临床观察结果将其分为高风险、中风险和低风险病例。我们的风险分类机器学习（ML）模型保持了模型的泛化能力。该模型的预测结果可靠地识别了低风险患者，从而可能减少患者就诊次数。同时，该模型在准确预测所有高风险患者风险方面也表现出色。结论：所提出的方法可预测ATAAD术后早期真腔狭窄患者发生主动脉扩张的风险，从而帮助随访医生改善患者护理。