Mo Zucong, Lu Zheng, Tang Xiaogang, Lin Xuezhen, Wang Shuangquan, Zhang Yunli, Huang Zhai
Sector II of Intensive Care Department, The People's Hospital of Guangxi Zhuang Autonomous Region Nanning 530021, Guangxi, China.
Am J Transl Res. 2023 Jul 15;15(7):4639-4648. eCollection 2023.
To analyze the predictive effect of a back propagation (BP) neural network, random forest (RF) and decision tree model on the prognosis of elderly patients with cardiogenic shock after extracorporeal membrane oxygenation (ECMO).
This is a retrospective analysis of the clinical data of elderly patients with cardiogenic shock (258 cases) who underwent ECMO in People's Hospital of Guangxi Zhuang Autonomous Region from January 2016 to January 2022. All patients were followed up for 6 months after ECMO treatment. The prognosis was evaluated, and the prognostic factors were analyzed. BP neural network, RF and decision tree were used to establish predictive models, and the predictive performance of the models was evaluated.
Among the 258 elderly patients with cardiogenic shock, 52 (20.16%) died 6 months after the ECMO treatment. Based on BP neural network, RF, and decision tree, predictive models for the prognosis and death of elderly patients with cardiogenic shock were constructed. A test set was used to predict the performance of the three models. The results showed that the predictive performances of the three models were all more than 80.00%. The accuracy, sensitivity, and specificity of the RF model were 0.987, 1.000, and 0.929 respectively, which were higher than those of the decision tree model. The area under the receiver operating characteristic curve (AUC) of the RF model was 1.000, which was higher than 0.916 for the decision tree model. DeLong test showed that there was a significant difference in the AUC of the RF model compared to the decision tree test set (D=-2.063, =0.042 < 0.05).
The predictive performance is good in all the three models, which have a high application value for prognosis of ECMO in elderly patients with cardiogenic shock. In clinical practice, predictive models should be selected according to the actual situation, so clinicians and patients can make decisions.
分析反向传播（BP）神经网络、随机森林（RF）和决策树模型对体外膜肺氧合（ECMO）后老年心源性休克患者预后的预测效果。
这是一项对2016年1月至2022年1月在广西壮族自治区人民医院接受ECMO治疗的老年心源性休克患者（258例）临床资料的回顾性分析。所有患者在ECMO治疗后随访6个月。评估预后并分析预后因素。使用BP神经网络、RF和决策树建立预测模型，并评估模型的预测性能。
在258例老年心源性休克患者中，52例（20.16%）在ECMO治疗后6个月死亡。基于BP神经网络、RF和决策树，构建了老年心源性休克患者预后和死亡的预测模型。使用测试集预测三种模型的性能。结果显示，三种模型的预测性能均超过80.00%。RF模型的准确性、敏感性和特异性分别为0.987、1.000和0.929，高于决策树模型。RF模型的受试者工作特征曲线（AUC）下面积为1.000，高于决策树模型的0.916。DeLong检验显示，RF模型与决策树测试集的AUC存在显著差异（D=-2.063，P=0.042<0.05）。
三种模型的预测性能均良好，对老年心源性休克患者ECMO预后具有较高的应用价值。在临床实践中，应根据实际情况选择预测模型，以便临床医生和患者做出决策。
