Scholarlink

<p>Thoracic aortic aneurysms carry a&#xa0;high risk of life-threatening complications, such as acute type&#xa0;A dissections. Traditional risk stratification is primarily based on the maximum aortic diameter, yet many dissections occur at diameters below the classical thresholds for prophylactic surgical intervention. A&#xa0;more precise risk assessment requires consideration of additional parameters, including aortic root morphology, aortic length, tortuosity, volume, patient age, comorbidities, body size and genetic factors (hereditary thoracic aortic diseases). Modern imaging techniques, such as 4D-flow magnetic resonance imaging (MRI) and measurements of aortic wall elasticity as well as circulating biomarkers, also offer new perspectives but have not yet been integrated into routine clinical practice. The current challenge lies in incorporating these multiple risk factors into practical, evidence-based decision algorithms. Initial multimodal approaches, such as the AORTA gene score, have shown promising results. Artificial intelligence and big data analyses could further improve individual risk prediction by identifying subtle risk patterns. The long-term goal is to develop precise, patient-specific risk models to enable personalized decision-making regarding prophylactic interventions in thoracic aortic diseases.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Risikoevaluation bei thorakalem Aortenaneurysma

  • 期刊论文

  • 发表日期：

    2025年6月23日

查看全文
  • Till Joscha Demal,
  • Marco Sachse,
  • Hermann Reichenspurner,
  • Christian Detter

摘要

Thoracic aortic aneurysms carry a high risk of life-threatening complications, such as acute type A dissections. Traditional risk stratification is primarily based on the maximum aortic diameter, yet many dissections occur at diameters below the classical thresholds for prophylactic surgical intervention. A more precise risk assessment requires consideration of additional parameters, including aortic root morphology, aortic length, tortuosity, volume, patient age, comorbidities, body size and genetic factors (hereditary thoracic aortic diseases). Modern imaging techniques, such as 4D-flow magnetic resonance imaging (MRI) and measurements of aortic wall elasticity as well as circulating biomarkers, also offer new perspectives but have not yet been integrated into routine clinical practice. The current challenge lies in incorporating these multiple risk factors into practical, evidence-based decision algorithms. Initial multimodal approaches, such as the AORTA gene score, have shown promising results. Artificial intelligence and big data analyses could further improve individual risk prediction by identifying subtle risk patterns. The long-term goal is to develop precise, patient-specific risk models to enable personalized decision-making regarding prophylactic interventions in thoracic aortic diseases.