Abstract：
BACKGROUND: Accurately identifying patients at high risk of delirium is vital for timely preventive intervention measures. Approaches for identifying the risk of developing delirium among critically ill children are not well researched.
OBJECTIVE: To develop and validate machine learning-based models for predicting delirium among critically ill children 24 h after pediatric intensive care unit (PICU) admission.
METHODS: A prospective cohort study.
METHODS: A large academic medical center with a 57-bed PICU in southwestern China from November 2019 to February 2022.
METHODS: One thousand five hundred and seventy-six critically ill children requiring PICU stay over 24 h.
METHODS: Five machine learning algorithms were employed. Delirium was screened by bedside nurses twice a day using the Cornell Assessment of Pediatric Delirium. Twenty-four clinical features from medical and nursing records during hospitalization were used to inform the models. Model performance was assessed according to numerous learning metrics, including the area under the receiver operating characteristic curve (AUC).
RESULTS: Of the 1576 enrolled patients, 929 (58.9 %) were boys, and the age ranged from 28 days to 15 years with a median age of 12 months (IQR 3 to 60 months). Among them, 1126 patients were assigned to the training cohort, and 450 were assigned to the validation cohort. The AUCs ranged from 0.763 to 0.805 for the five models, among which the eXtreme Gradient Boosting (XGB) model performed best, achieving an AUC of 0.805 (95 % CI, 0.759-0.851), with 0.798 (95 % CI, 0.758-0.834) accuracy, 0.902 sensitivity, 0.839 positive predictive value, 0.640 F1-score and a Brier score of 0.144. Almost all models showed lower predictive performance in children younger than 24 months than in older children. The logistic regression model also performed well, with an AUC of 0.789 (95 % CI, 0.739, 0.838), just under that of the XGB model, and was subsequently transformed into a nomogram.
CONCLUSIONS: Machine learning-based models can be established and potentially help identify critically ill children who are at high risk of delirium 24 h after PICU admission. The nomogram may be a beneficial management tool for delirium for PICU practitioners at present.
OBJECTIVE: To develop and validate machine learning-based models for predicting delirium among critically ill children 24 h after pediatric intensive care unit (PICU) admission.
METHODS: A prospective cohort study.
METHODS: A large academic medical center with a 57-bed PICU in southwestern China from November 2019 to February 2022.
METHODS: One thousand five hundred and seventy-six critically ill children requiring PICU stay over 24 h.
METHODS: Five machine learning algorithms were employed. Delirium was screened by bedside nurses twice a day using the Cornell Assessment of Pediatric Delirium. Twenty-four clinical features from medical and nursing records during hospitalization were used to inform the models. Model performance was assessed according to numerous learning metrics, including the area under the receiver operating characteristic curve (AUC).
RESULTS: Of the 1576 enrolled patients, 929 (58.9 %) were boys, and the age ranged from 28 days to 15 years with a median age of 12 months (IQR 3 to 60 months). Among them, 1126 patients were assigned to the training cohort, and 450 were assigned to the validation cohort. The AUCs ranged from 0.763 to 0.805 for the five models, among which the eXtreme Gradient Boosting (XGB) model performed best, achieving an AUC of 0.805 (95 % CI, 0.759-0.851), with 0.798 (95 % CI, 0.758-0.834) accuracy, 0.902 sensitivity, 0.839 positive predictive value, 0.640 F1-score and a Brier score of 0.144. Almost all models showed lower predictive performance in children younger than 24 months than in older children. The logistic regression model also performed well, with an AUC of 0.789 (95 % CI, 0.739, 0.838), just under that of the XGB model, and was subsequently transformed into a nomogram.
CONCLUSIONS: Machine learning-based models can be established and potentially help identify critically ill children who are at high risk of delirium 24 h after PICU admission. The nomogram may be a beneficial management tool for delirium for PICU practitioners at present.
摘要：
背景：准确识别谵妄高危患者对于及时采取预防性干预措施至关重要。确定危重儿童发生谵妄风险的方法还没有得到很好的研究。
目的：开发并验证基于机器学习的模型，用于预测儿科重症监护病房（PICU）入院后24小时的危重患儿谵妄。
方法：前瞻性队列研究。
方法：2019年11月至2022年2月，中国西南部拥有57张病床的PICU的大型学术医学中心。
方法：一千五百七十六名需要PICU住院24小时以上的危重患儿。
方法：采用了五种机器学习算法。每天两次由床边护士使用康奈尔小儿谵妄评估筛查谵妄。住院期间医疗和护理记录中的24项临床特征用于告知模型。模型性能是根据许多学习指标进行评估的，包括接收器工作特征曲线下的面积(AUC)。
结果：在1576名入组患者中，929（58.9％）是男孩，年龄范围为28天至15岁，中位年龄为12个月（IQR3至60个月）。其中,1126名患者被分配到训练组，450人被分配到验证队列.五个型号的AUC范围从0.763到0.805，其中极限梯度提升(XGB)模型表现最好，达到0.805的AUC（95%CI，0.759-0.851），准确度为0.798(95%CI，0.758-0.834)，0.902灵敏度，0.839阳性预测值，0.640F1评分和Brier评分为0.144。几乎所有模型在24个月以下的儿童中的预测性能都低于年龄较大的儿童。逻辑回归模型也表现良好，AUC为0.789（95%CI，0.739，0.838），就在XGB模型下，随后被转换成列线图。
结论：可以建立基于机器学习的模型，并可能有助于识别在PICU入住24小时后发生谵妄的高危危重患儿。列线图可能是目前PICU从业人员谵妄的有益管理工具。
目的：开发并验证基于机器学习的模型，用于预测儿科重症监护病房（PICU）入院后24小时的危重患儿谵妄。
方法：前瞻性队列研究。
方法：2019年11月至2022年2月，中国西南部拥有57张病床的PICU的大型学术医学中心。
方法：一千五百七十六名需要PICU住院24小时以上的危重患儿。
方法：采用了五种机器学习算法。每天两次由床边护士使用康奈尔小儿谵妄评估筛查谵妄。住院期间医疗和护理记录中的24项临床特征用于告知模型。模型性能是根据许多学习指标进行评估的，包括接收器工作特征曲线下的面积(AUC)。
结果：在1576名入组患者中，929（58.9％）是男孩，年龄范围为28天至15岁，中位年龄为12个月（IQR3至60个月）。其中,1126名患者被分配到训练组，450人被分配到验证队列.五个型号的AUC范围从0.763到0.805，其中极限梯度提升(XGB)模型表现最好，达到0.805的AUC（95%CI，0.759-0.851），准确度为0.798(95%CI，0.758-0.834)，0.902灵敏度，0.839阳性预测值，0.640F1评分和Brier评分为0.144。几乎所有模型在24个月以下的儿童中的预测性能都低于年龄较大的儿童。逻辑回归模型也表现良好，AUC为0.789（95%CI，0.739，0.838），就在XGB模型下，随后被转换成列线图。
结论：可以建立基于机器学习的模型，并可能有助于识别在PICU入住24小时后发生谵妄的高危危重患儿。列线图可能是目前PICU从业人员谵妄的有益管理工具。
