目的:使用一组机器学习算法开发一种决策支持工具,用于预测支气管肺发育不良(BPD)新生儿的拔管失败(EF)。
方法:使用284例机械通气BPD新生儿的数据集通过机器学习算法开发预测模型,包括极端梯度增强(XGBoost),随机森林,支持向量机,天真贝叶斯,逻辑回归,和k最近的邻居。通过受试者工作特征曲线下面积(AUC)评估前三个模型,并通过决策曲线分析(DCA)测试其性能。使用混淆矩阵来显示最佳模型的高性能。计算了重要性矩阵图和SHapley加法扩张值,以评估特征重要性并可视化结果。使用列线图和临床影响曲线来验证最终模型。
结果:根据AUC值和DCA结果,XGboost模型表现最好(AUC=0.873,敏感性=0.896,特异性=0.838).列线图和临床影响曲线验证了XGBoost模型具有显著的预测价值。以下是EF的预测因素:pO2,血红蛋白,机械通气(MV)率,pH值,阿普加5分钟得分,FiO2,C反应蛋白,1分钟时的阿普加得分,红细胞计数,PIP,胎龄,在最初的24小时内最高的FiO2,心率,出生体重,pCO2。Further,PO2,血红蛋白,和MV率是预测EF的三个最重要因素。
结论:本研究表明,XGBoost模型在预测机械通气的BPD新生儿的EF方面具有重要意义。这有助于确定BPD新生儿的正确拔管时间,以减少并发症的发生。
OBJECTIVE: To develop a decision-support tool for predicting extubation failure (EF) in
neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms.
METHODS: A dataset of 284 BPD
neonates on mechanical ventilation was used to develop predictive models via machine-learning algorithms, including extreme gradient boosting (XGBoost), random forest, support vector machine, naïve Bayes, logistic regression, and k-nearest neighbor. The top three models were assessed by the area under the receiver operating characteristic curve (AUC), and their performance was tested by decision curve analysis (DCA). Confusion matrix was used to show the high performance of the best model. The importance matrix plot and SHapley Additive exPlanations values were calculated to evaluate the feature importance and visualize the results. The nomogram and clinical impact curves were used to validate the final model.
RESULTS: According to the AUC values and DCA results, the XGboost model performed best (AUC = 0.873, sensitivity = 0.896, specificity = 0.838). The nomogram and clinical impact curve verified that the XGBoost model possessed a significant predictive value. The following were predictive factors for EF: pO2, hemoglobin, mechanical ventilation (MV) rate, pH, Apgar score at 5 min, FiO2, C-reactive protein, Apgar score at 1 min, red blood cell count, PIP, gestational age, highest FiO2 at the first 24 h, heart rate, birth weight, pCO2. Further, pO2, hemoglobin, and MV rate were the three most important factors for predicting EF.
CONCLUSIONS: The present study indicated that the XGBoost model was significant in predicting EF in BPD
neonates with mechanical ventilation, which is helpful in determining the right extubation time among
neonates with BPD to reduce the occurrence of complications.