基于机器学习的急性主动脉夹层患者术前院内死亡率预测：一项两中心回顾性队列研究。
Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study.
机构信息
Department of Vascular Surgery, Shanghai Jiao Tong University School of Medicine Affiliated to Ninth People's Hospital, Shanghai, China.
Big Data Research Lab, University of Waterloo, Waterloo, Ontario, Canada.
出版信息
BMJ Open. 2023 Apr 3;13(4):e066782. doi: 10.1136/bmjopen-2022-066782.
OBJECTIVES
To conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques.
DESIGN
Retrospective cohort study.
SETTING
Data were collected from the electronic records and the databases of Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018.
PARTICIPANTS
380 inpatients diagnosed with acute AD were included in the study.
PRIMARY OUTCOME
Preoperative in-hospital mortality rate.
RESULTS
A total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level.
CONCLUSION
In the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database.
TRIAL REGISTRATION NUMBER
ChiCTR1900025818.
目的
通过对患者的人口统计学信息、病史以及住院期间的血压（BP）和心率（HR）变异性进行全面分析，利用机器学习技术建立急性主动脉夹层（AD）患者术前院内死亡率的预测模型。
设计
回顾性队列研究。
地点
数据来自上海交通大学医学院附属第九人民医院和安徽医科大学第一附属医院的电子病历和数据库，时间为 2004 年至 2018 年。
参与者
380 例急性 AD 住院患者纳入本研究。
主要结局
术前院内死亡率。
结果
共有 55 例（14.47%）患者在术前住院期间死亡。受试者工作特征曲线下面积、决策曲线分析和校准曲线的结果表明，极端梯度提升（XGBoost）模型具有最高的准确性和稳健性。根据 XGBoost 模型的 SHapley Additive exPlanations 分析，斯坦福 A 型、最大主动脉直径>5.5cm、HR 变异性高、舒张压变异性高以及主动脉弓受累对术前院内死亡的发生影响最大。此外，该预测模型能够准确预测个体水平的术前院内死亡率。
结论
本研究成功构建了用于预测急性 AD 患者术前院内死亡率的机器学习模型，有助于识别高危患者并优化临床决策。这些模型的进一步临床应用需要使用大样本、前瞻性数据库进行验证。
临床试验注册号
ChiCTR1900025818。
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