用机器学习方法开发心脏重症监护病房谵妄发生率的预测模型。

IF 7.2 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS Revista española de cardiología (English ed.) Pub Date : 2024-07-01 DOI:10.1016/j.rec.2023.12.007
Ryoung-Eun Ko , Jihye Lee , Sungeun Kim , Joong Hyun Ahn , Soo Jin Na , Jeong Hoon Yang
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引用次数: 0

摘要

引言和目的:谵妄被认为是心脏重症监护病房(CICU)的一个重要预后因素,随着重症心脏病患者人口统计学的变化而不断发展。本研究旨在为 CICU 患者建立一个谵妄预测模型:本研究包括三星医疗中心 CICU 的连续入院患者。为了评估模型的候选变量,我们采用了以下机器学习方法:随机森林、极梯度提升、偏最小二乘法和 Plmnet-elastic.net。选定相关变量后,我们进行了逻辑回归分析,得出了模型公式。内部验证采用 100 次重复保持验证:我们分析了2774名患者,其中677人(24.4%)在CICU中出现了谵妄。基于机器学习的模型显示出良好的预测性能。我们选择了具有临床意义且经常出现的重要预测因子,为 CICU 患者构建了谵妄预测评分模型。该模型包括白蛋白水平、国际标准化比率、血尿素氮、白细胞计数、C反应蛋白水平、年龄、心率和机械通气。该模型的接收者操作特征曲线下面积 (AUROC) 为 0.861(95%CI,0.843-0.879)。通过 100 次重复交叉验证的内部验证也得到了类似的结果(AUROC,0.854;95%CI,0.826-0.883):利用四种机器学习方法中经常被列为高度重要的变量,我们创建了一个新的谵妄预测模型。该模型可作为一种有用而简单的工具,用于对 CICU 患者床旁发生谵妄的风险进行分层。
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Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units

Introduction and objectives

Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predictive model for delirium for patients in the CICU.

Methods

This study included consecutive patients admitted to the CICU of the Samsung Medical Center. To assess the candidate variables for the model: we applied the following machine learning methods: random forest, extreme gradient boosting, partial least squares, and Plmnet-elastic.net. After selecting relevant variables, we performed a logistic regression analysis to derive the model formula. Internal validation was conducted using 100-repeated hold-out validation.

Results

We analyzed 2774 patients, 677 (24.4%) of whom developed delirium in the CICU. Machine learning-based models showed good predictive performance. Clinically significant and frequently important predictors were selected to construct a delirium prediction scoring model for CICU patients. The model included albumin level, international normalized ratio, blood urea nitrogen, white blood cell count, C-reactive protein level, age, heart rate, and mechanical ventilation. The model had an area under the receiver operating characteristics curve (AUROC) of 0.861 (95%CI, 0.843-0.879). Similar results were obtained in internal validation with 100-repeated cross-validation (AUROC, 0.854; 95%CI, 0.826-0.883).

Conclusions

Using variables frequently ranked as highly important in four machine learning methods, we created a novel delirium prediction model. This model could serve as a useful and simple tool for risk stratification for the occurrence of delirium at the patient's bedside in the CICU.

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