Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study.
Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study.
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DOI：
10.1136/bmjopen-2022-066782
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发表时间：
2023-04-03
期刊：
影响因子：
2.9
通讯作者：
Lu, Xinwu
中科院分区：
文献类型：
--
作者：
Wu, Zhaoyu;Li, Yixuan;Xu, Zhijue;Liu, Haichun;Liu, Kai;Qiu, Peng;Chen, Tao;Lu, Xinwu
综合分析人口统计学信息、病史、住院期间血压（BP）和心率（HR）变异性，利用机器学习技术建立急性主动脉夹层（AD）患者术前住院期间死亡率的预测模型。回顾性队列研究。数据来源于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2004 - 2018年的电子病历和数据库。380例诊断为急性AD的住院患者纳入研究。术前住院死亡率。术前死亡55例（14.47%）。受试者工作特征曲线下面积、决策曲线分析和校准曲线分析结果表明，极限梯度增强（XGBoost）模型具有最高的准确性和鲁棒性。根据XGBoost模型的SHapley加法解释分析，斯坦福大学A型、最大主动脉直径>5.5 cm、HR高变异性、舒张压高变异性和主动脉弓受累对术前院内死亡的发生率影响最大。此外，预测模型可以准确地预测术前住院死亡率在个人水平。在当前的研究中，我们成功地构建了机器学习模型来预测急性AD患者的术前住院死亡率，这可以帮助识别高风险患者并优化临床决策。临床实践中的进一步应用需要使用大样本前瞻性数据库来验证这些模型。ChiCTR1900025818。
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. Retrospective cohort study. 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. 380 inpatients diagnosed with acute AD were included in the study. Preoperative in-hospital mortality rate. 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. 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. ChiCTR1900025818.
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影响因子：
28.1
作者：
Lundberg, Scott M.;Nair, Bala;Lee, Su-In
通讯作者：
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DOI：
10.1016/j.jtcvs.2018.01.096
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作者：
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