Unraveling phenotypic heterogeneity in stanford type B aortic dissection patients through machine learning clustering analysis of cardiovascular CT imaging

作者：K. Liu, Deyin Zhao, Lvfan Feng, Zhaoxuan Zhang, Peng Qiu, Xiaoyu Wu, Ruihua Wang, Azad Hussain, Jamol Uzokov, Yanshuo Han · 发表于：Hellenic Journal of Cardiology · 年份：2024 · DOI：10.1016/j.hjc.2024.08.006 · 被引用次数：3 · 研究领域：Aortic Disease and Treatment Approaches、Connective tissue disorders research、Aortic aneurysm repair treatments

OBJECTIVE: Aortic dissection remains a life-threatening condition necessitating accurate diagnosis and timely intervention. This study aimed to investigate phenotypic heterogeneity in patients with Stanford type B aortic dissection (TBAD) through machine learning clustering analysis of cardiovascular computed tomography (CT) imaging. METHODS: Electronic medical records were collected to extract demographic and clinical features of patients with TBAD. Exclusion criteria ensured homogeneity and clinical relevance of the TBAD cohort. Controls were selected on the basis of age, comorbidity status, and imaging availability. Aortic morphological parameters were extracted from CT angiography and subjected to K-means clustering analysis to identify distinct phenotypes. RESULTS: Clustering analysis revealed three phenotypes of patients with TBAD with significant correlations with population characteristics and dissection rates. This pioneering study used CT-based three-dimensional reconstruction to classify high-risk individuals, demonstrating the potential of machine learning in enhancing diagnostic accuracy and personalized treatment strategies. Recent advancements in machine learning have garnered attention in cardiovascular imaging, particularly in aortic dissection research. These studies leverage various imaging modalities to extract valuable features and information from cardiovascular images, paving the way for more personalized interventions. CONCLUSION: This study provides i...