自动腹腔大动脉化得分和动脉样性心血管疾病在英国生物银行成像研究研究
Marc Sim1, James Webster2, Cassandra Smith1
1Nutrition and Health Innovation Research Institute, Edith Cowan University, Perth, Australia; Medical School, The University of Western Australia, Perth, Australia.
在脊柱图像上检测到的腹腔大动脉化 (AAC) 可以预测未来的动脉样硬化心血管疾病 (ASCVD) 事件. 一个机器学习算法 (ML-AAC24) 有效地识别高风险个体,提供了一个新的选机会.
科学领域:
- 心血管疾病的研究研究.
- 医学成像分析分析 医学成像分析
- 机器学习在医疗保健中的应用
背景情况:
- 腹腔大动脉化 (AAC) 是动脉样硬化心血管疾病 (ASCVD) 的亚临床标志物.
- 从骨密度扫描的侧脊图像上可以识别AAC.
- 早期识别AAC有助于预防ASCVD的进展.
研究的目的:
- 在外部验证一个机器学习衍生 AAC 24 点算法 (ML-AAC24).
- 评估ML-AAC24分数与发生的ASCVD事件之间的关联.
主要方法:
- 利用英国生物库成像研究数据从中年个体与侧侧脊柱图像.
- 计算了ML-AAC24分数,并将其分类为低,中等和高风险组.
- 分析了有关ASCVD事件的相关健康记录,包括住院治疗和死亡.
主要成果:
- 分析了超过5万名参与者,其中2.3%的人经历了事件ASCVD事件.
- 较高的ML-AAC24分数 (中等和高) 与发生ASCVD的风险增加有显著的关联.
- 在对传统的ASCVD风险因素进行调整后,这些关联仍然强.
结论:
- 应用到侧脊图像的ML-AAC24算法是查ASCVD高风险个体的一个有希望的工具.
- 这种方法提供了一种新的方法,用于在例行骨质疏松症查期间识别有风险的人群.
- 利用现有的成像数据进行AAC评估可以提高心血管风险分层.
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