Comoretto Rosanna I, Azzolina Danila, Amigoni Angela, Stoppa Giorgia, Todino Federica, Wolfler Andrea, Gregori Dario
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
Department of Medical Sciences, University of Ferrara, 44100 Ferrara, Italy.
Diagnostics (Basel). 2021 Jul 20;11(7):1299. doi: 10.3390/diagnostics11071299.
The present work aims to identify the predictors of hemodynamic failure (HF) developed during pediatric intensive care unit (PICU) stay testing a set of machine learning techniques (MLTs), comparing their ability to predict the outcome of interest. The study involved patients admitted to PICUs between 2010 and 2020. Data were extracted from the Italian Network of Pediatric Intensive Care Units (TIPNet) registry. The algorithms considered were generalized linear model (GLM), recursive partition tree (RPART), random forest (RF), neural networks models, and extreme gradient boosting (XGB). Since the outcome is rare, upsampling and downsampling algorithms have been applied for imbalance control. For each approach, the main performance measures were reported. Among an overall sample of 29,494 subjects, only 399 developed HF during the PICU stay. The median age was about two years, and the male gender was the most prevalent. The XGB algorithm outperformed other MLTs in predicting HF development, with a median ROC measure of 0.780 (IQR 0.770-0.793). PIM 3, age, and base excess were found to be the strongest predictors of outcome. The present work provides insights for the prediction of HF development during PICU stay using machine-learning algorithms.
本研究旨在通过测试一组机器学习技术（MLT）来识别小儿重症监护病房（PICU）住院期间发生的血流动力学衰竭（HF）的预测因素，并比较它们预测感兴趣结果的能力。该研究纳入了2010年至2020年间入住PICU的患者。数据从意大利小儿重症监护病房网络（TIPNet）登记处提取。所考虑的算法包括广义线性模型（GLM）、递归划分树（RPART）、随机森林（RF）、神经网络模型和极端梯度提升（XGB）。由于结果罕见，已应用上采样和下采样算法来控制不平衡。对于每种方法，都报告了主要性能指标。在总共29494名受试者的样本中，只有399人在PICU住院期间发生了HF。中位年龄约为两岁，男性最为常见。XGB算法在预测HF发生方面优于其他MLT，中位ROC值为0.780（IQR 0.770 - 0.793）。发现小儿死亡风险评估系统（PIM）3、年龄和碱剩余是结果的最强预测因素。本研究为使用机器学习算法预测PICU住院期间HF的发生提供了见解。
