PDF(Pubmed)
Abstract：
UNASSIGNED: Delirium is the most common neuropsychological complication among older adults admitted to the intensive care unit (ICU) and is often associated with a poor prognosis. This study aimed to construct and validate an interpretable machine learning (ML) for early delirium prediction in older ICU patients.
UNASSIGNED: This was a retrospective observational cohort study and patient data were extracted from the Medical Information Mart for Intensive Care-IV database. Feature variables associated with delirium, including predisposing factors, disease-related factors, and iatrogenic and environmental factors, were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, decision trees, support vector machines, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, recall, F1 score, calibration plot, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to improve the interpretability of the final model.
UNASSIGNED: Nine thousand seven hundred forty-eight adults aged 65 years or older were included for analysis. Twenty-six features were selected to construct ML prediction models. Among the models compared, the XGBoost model demonstrated the best performance including the highest AUC (0.836), accuracy (0.765), sensitivity (0.713), recall (0.713), and F1 score (0.725) in the training set. It also exhibited excellent discrimination with AUC of 0.810, good calibration, and had the highest net benefit in the validation cohort. The SHAP summary analysis showed that Glasgow Coma Scale, mechanical ventilation, and sedation were the top three risk features for outcome prediction. The SHAP dependency plot and SHAP force analysis interpreted the model at both the factor level and individual level, respectively.
UNASSIGNED: ML is a reliable tool for predicting the risk of critical delirium in elderly patients. By combining XGBoost and SHAP, it can provide clear explanations for personalized risk prediction and more intuitive understanding of the effect of key features in the model. The establishment of such a model would facilitate the early risk assessment and prompt intervention for delirium.
UNASSIGNED: This was a retrospective observational cohort study and patient data were extracted from the Medical Information Mart for Intensive Care-IV database. Feature variables associated with delirium, including predisposing factors, disease-related factors, and iatrogenic and environmental factors, were selected using least absolute shrinkage and selection operator regression, and prediction models were built using logistic regression, decision trees, support vector machines, extreme gradient boosting (XGBoost), k-nearest neighbors and naive Bayes methods. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, recall, F1 score, calibration plot, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to improve the interpretability of the final model.
UNASSIGNED: Nine thousand seven hundred forty-eight adults aged 65 years or older were included for analysis. Twenty-six features were selected to construct ML prediction models. Among the models compared, the XGBoost model demonstrated the best performance including the highest AUC (0.836), accuracy (0.765), sensitivity (0.713), recall (0.713), and F1 score (0.725) in the training set. It also exhibited excellent discrimination with AUC of 0.810, good calibration, and had the highest net benefit in the validation cohort. The SHAP summary analysis showed that Glasgow Coma Scale, mechanical ventilation, and sedation were the top three risk features for outcome prediction. The SHAP dependency plot and SHAP force analysis interpreted the model at both the factor level and individual level, respectively.
UNASSIGNED: ML is a reliable tool for predicting the risk of critical delirium in elderly patients. By combining XGBoost and SHAP, it can provide clear explanations for personalized risk prediction and more intuitive understanding of the effect of key features in the model. The establishment of such a model would facilitate the early risk assessment and prompt intervention for delirium.
摘要：
■谵妄是重症监护病房（ICU）收治的老年人中最常见的神经心理并发症，通常与预后不良有关。本研究旨在构建和验证可解释的机器学习（ML），用于老年ICU患者的早期谵妄预测。
这是一项回顾性观察性队列研究，患者数据从医疗信息集市重症监护IV数据库中提取。与谵妄相关的特征变量，包括诱发因素，疾病相关因素,以及医源性和环境因素，使用最小绝对收缩和选择算子回归进行选择，并使用逻辑回归建立预测模型，决策树,支持向量机，极端梯度提升(XGBoost)，k近邻和朴素贝叶斯方法。多个指标用于评估模型的性能，包括接收器工作特性曲线下的面积（AUC），准确度,灵敏度,特异性，召回，F1得分,校准图,和决策曲线分析。使用Shapley添加剂扩张（SHAP）来提高最终模型的可解释性。
■九千七百四十八名65岁或以上的成年人被纳入分析。选择26个特征构建ML预测模型。在比较的模型中，XGBoost模型表现出最好的性能，包括最高的AUC（0.836），精度（0.765），灵敏度（0.713），召回(0.713)，和训练集中的F1得分(0.725)。它还表现出优异的辨别力，AUC为0.810，良好的校准，并且在验证队列中具有最高的净获益。SHAP汇总分析表明，格拉斯哥昏迷量表，机械通气,镇静是结局预测的三大风险特征。SHAP依赖图和SHAP力分析在因子水平和个体水平上解释了模型，分别。
■ML是预测老年患者严重谵妄风险的可靠工具。通过结合XGBoost和SHAP，它可以为个性化风险预测提供清晰的解释，并更直观地理解模型中关键特征的作用。这种模式的建立将有助于对谵妄进行早期风险评估和及时干预。
这是一项回顾性观察性队列研究，患者数据从医疗信息集市重症监护IV数据库中提取。与谵妄相关的特征变量，包括诱发因素，疾病相关因素,以及医源性和环境因素，使用最小绝对收缩和选择算子回归进行选择，并使用逻辑回归建立预测模型，决策树,支持向量机，极端梯度提升(XGBoost)，k近邻和朴素贝叶斯方法。多个指标用于评估模型的性能，包括接收器工作特性曲线下的面积（AUC），准确度,灵敏度,特异性，召回，F1得分,校准图,和决策曲线分析。使用Shapley添加剂扩张（SHAP）来提高最终模型的可解释性。
■九千七百四十八名65岁或以上的成年人被纳入分析。选择26个特征构建ML预测模型。在比较的模型中，XGBoost模型表现出最好的性能，包括最高的AUC（0.836），精度（0.765），灵敏度（0.713），召回(0.713)，和训练集中的F1得分(0.725)。它还表现出优异的辨别力，AUC为0.810，良好的校准，并且在验证队列中具有最高的净获益。SHAP汇总分析表明，格拉斯哥昏迷量表，机械通气,镇静是结局预测的三大风险特征。SHAP依赖图和SHAP力分析在因子水平和个体水平上解释了模型，分别。
■ML是预测老年患者严重谵妄风险的可靠工具。通过结合XGBoost和SHAP，它可以为个性化风险预测提供清晰的解释，并更直观地理解模型中关键特征的作用。这种模式的建立将有助于对谵妄进行早期风险评估和及时干预。
