Li Xia, Zhang Weisong, Wang Tao, Qiu Zhengfeng, Sun Xuan, Qu Wenhao, Zhang Guopei
Department of Intensive Care Unit, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, 224000, People's Republic of China.
Department of Thoracic Surgery, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, 224000, People's Republic of China.
Int J Gen Med. 2025 Jul 5;18:3727-3737. doi: 10.2147/IJGM.S526749. eCollection 2025.
To analyze the influencing factors contributing to the occurrence of delirium in patients within the Intensive Care Unit (ICU) and to construct a prediction model for delirium in critically ill patients, subsequently verifying its predictive value.
A prospective study was conducted involving 641 patients admitted between January 2023 and June 2024. A simple random sampling method was employed to develop the predictive model, with a validation set comprising 193 patients, thus creating a training set of 448 patients. Delirium was assessed using the Confusion Assessment Method for the ICU (CAM-ICU). The baseline data of the two patient groups in the training and validation sets were compared. Logistic regression analysis was utilized to identify independent risk factors influencing the onset of delirium. The R programming language was employed to establish a column-line graph model for predicting delirium occurrence in ICU patients. The Bootstrap method facilitated model validation, while calibration curves and Receiver Operating Characteristic (ROC) curves were utilized to evaluate the model's discriminatory ability and predictive efficacy. Finally, the prediction model was validated using the validation set.
In the training cohort, the incidence of delirium among patients was 35.71%. Logistic regression analysis revealed that the Glasgow Coma Scale (GCS) score (OR=0.421, 95% CI: 0.355-0.501, P<0.001), blood urea nitrogen (BUN) (OR=1.169, 95% CI: 1.014-1.348, P=0.031), emergency surgery (OR=2.735, 95% CI: 1.42-5.268, P=0.003), use of sedative medications (OR=3.816, 95% CI: 1.968-7.397, P<0.001), and postoperative status following major cardiovascular surgery (OR=2.124, 95% CI: 1.205-3.745, P=0.009) were identified as independent risk factors for delirium in the ICU.
The predictive model developed in this study for the occurrence of delirium in ICU patients has been validated, demonstrating high predictive efficacy and offering significant clinical early warning guidance.
分析重症监护病房（ICU）患者谵妄发生的影响因素，构建危重症患者谵妄预测模型，并验证其预测价值。
进行一项前瞻性研究，纳入2023年1月至2024年6月期间收治的641例患者。采用简单随机抽样方法构建预测模型，其中验证集包含193例患者，由此形成448例患者的训练集。使用ICU意识模糊评估法（CAM-ICU）评估谵妄。比较训练集和验证集中两组患者的基线数据。采用Logistic回归分析确定影响谵妄发生的独立危险因素。运用R编程语言建立预测ICU患者谵妄发生的列线图模型。采用Bootstrap法进行模型验证，同时利用校准曲线和受试者工作特征（ROC）曲线评估模型的辨别能力和预测效能。最后，使用验证集对预测模型进行验证。
在训练队列中，患者谵妄发生率为35.71%。Logistic回归分析显示，格拉斯哥昏迷量表（GCS）评分（OR=0.421，95%CI：0.355-0.501，P<0.001）、血尿素氮（BUN）（OR=1.169，95%CI：1.014-1.348，P=0.031）、急诊手术（OR=2.735，95%CI：1.42-5.268，P=0.003）、使用镇静药物（OR=3.816，95%CI：1.968-7.397，P<0.001）以及重大心血管手术后的术后状态（OR=2.124，95%CI：1.205-3.745，P=0.009）被确定为ICU患者谵妄的独立危险因素。
本研究建立的ICU患者谵妄发生预测模型已得到验证，具有较高的预测效能，可为临床提供重要的早期预警指导。
