Background: Acute aortic dissection is a potentially fatal cardiovascular disorder associated with high mortality. However, current predictive models show a limited ability to efficiently and flexibly detect this mortality risk, and have been unable to discover a relationship between the mortality rate and certain variables. Thus, this study takes an artificial intelligence approach, whereby clinical data-driven machine learning was utilized to predict the in-hospital mortality of acute aortic dissection.

Methods: Patients diagnosed with acute aortic dissection between January 2015 to December 2018 were voluntarily enrolled from the Second Xiangya Hospital of Central South University in the study. The diagnosis was defined by magnetic resonance angiography or computed tomography angiography, with an onset time of the symptoms being within 14 days. The analytical variables included demographic characteristics, physical examination, symptoms, clinical condition, laboratory results, and treatment strategies. The machine learning algorithms included logistic regression, decision tree, K nearest neighbor, Gaussian naive bayes, and extreme gradient boost (XGBoost). Evaluation of the predictive performance of the models was mainly achieved using the area under the receiver operating characteristic curve. SHapley Additive exPlanation was also implemented to interpret the final prediction model.

Results: A total of 1,344 acute aortic dissection patients were recruited, including 1,071 (79.7%) patients in the survivor group and 273 (20.3%) patients in non-survivor group. The extreme gradient boost model was found to be the most effective model with the greatest area under the receiver operating characteristic curve (0.927, 95% CI: 0.860–0.968). The three most significant aspects of the extreme gradient boost importance matrix plot were treatment, type of acute aortic dissection, and ischemia-modified albumin levels. In the SHapley Additive exPlanation summary plot, medical treatment, type A acute aortic dissection, and higher ischemia-modified albumin level were shown to increase the risk of hospital-based mortality.

背景：急性主动脉夹层是一种潜在致命的心血管疾病，死亡率高。然而，当前的预测模型显示出有效和灵活地检测这种死亡风险的能力有限，并且无法发现死亡率与某些变量之间的关系。因此，本研究采用人工智能方法，利用临床数据驱动的机器学习来预测急性主动脉夹层的院内死亡率。

方法：2015 年 1 月至 2018 年 12 月期间诊断为急性主动脉夹层的患者是从中南大学湘雅二医院自愿入组的。诊断由磁共振血管造影或计算机断层扫描血管造影确定，​​症状出现时间在 14 天内。分析变量包括人口学特征、体格检查、症状、临床状况、实验室结果和治疗策略。机器学习算法包括逻辑回归、决策树、K 最近邻、高斯朴素贝叶斯和极端梯度提升 (XGBoost)。模型预测性能的评估主要是使用接收者操作特征曲线下的面积来实现的。

结果：共招募了 1,344 名急性主动脉夹层患者，其中幸存者组 1,071 名（79.7%）患者和非幸存者组 273 名（20.3%）患者。发现极端梯度提升模型是最有效的模型，其接收器操作特征曲线下的面积最大（0.927，95% CI：0.860–0.968）。极端梯度提升重要性矩阵图的三个最重要的方面是治疗、急性主动脉夹层的类型和缺血修饰的白蛋白水平。在 SHapley Additive exPlanation 总结图中，药物治疗、A 型急性主动脉夹层和较高的缺血修饰白蛋白水平被证明会增加住院死亡率的风险。