Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe).
Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe).
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DOI：
10.1177/20552076231223811
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发表时间：
2024-01
期刊：
影响因子：
3.9
通讯作者：
Park, Hye Youn
中科院分区：
文献类型：
--
作者：
Lee, So Hee;Hur, Hyun Jung;Kim, Sung Nyun;Ahn, Jang Ho;Ro, Du Hyun;Hong, Arum;Park, Hye Yoon;Choe, Pyoeng Gyun;Kim, Back;Park, Hye Youn
谵妄常见于2019冠状病毒病（COVID-19）感染住院患者。由于谵妄与不良临床结果密切相关，预测和预防谵妄至关重要。我们开发了一种机器学习（ML）模型来预测COVID-19住院患者的谵妄，并确定可改变的因素来预防谵妄。数据集（n = 878）来自4个医疗中心。共纳入人口统计学特征、生命体征、实验室结果和用药等78项预测指标，主要预后指标为住院期间谵妄的发生。为了进行分析，采用极限梯度增强（XGBoost）算法，并通过递归特征消去选择影响最大的因素。在ML模型的性能指标中，选择接受者工作特征曲线下面积（AUROC）作为评价指标。对于所建立的谵妄预测模型的性能，计算其准确率、精密度、召回率、F1评分和AUROC（分别为0.944、0.581、0.421、0.485、0.873）。影响该模型谵妄的因素有机械通气、药物（抗精神病药、镇静剂、氨溴索、哌拉西林/他唑巴坦、对乙酰氨基酚、头孢曲松、丙帕他莫）、钠离子浓度（p < 0.05）。我们开发并内部验证了ML模型来预测COVID-19住院患者的谵妄。该模型确定了与谵妄发展相关的可修改因素，可用于预测和预防COVID-19住院患者的谵妄。
Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium. The data set (n = 878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric. Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p < 0.05). We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.
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