Shi Yu, Wang Hai, Zhang Li, Zhang Ming, Shi Xiaoyan, Pei Honghong, Bai Zhenghai
Emergency Department & EICU, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaan Xi, 710004, Peoples' Republic of China.
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi 'an Jiaotong University, Xi'an, Shaan Xi, 710061, Peoples' Republic of China.
Int J Gen Med. 2022 Apr 21;15:4259-4272. doi: 10.2147/IJGM.S353318. eCollection 2022.
Intensive care unit (ICU) delirium is one of the most common clinical syndromes that results in many adverse events that affect patients, families, and hospitals. To date, there has been no tool for effectively predicting the occurrence of delirium in emergency intensive care unit (EICU) patients.
We conducted a retrospective cohort study and constructed a prediction model for 319 patients in EICU, who met our inclusion criteria. We analyzed the relationship between patients' clinical data within 24 hours of admission and delirium, applied univariate and multivariate logistic regression analyses to select the most relevant variables for construction of nomogram models, then applied bootstrapping for internal validation.
A total of five variables, namely stomach and urinary tubes, as well as sedative, mechanical ventilation and APACHE-II scores, were selected for model construction. We generated a total of five sets of models (three sets of construction models and two sets of internal verification models), with similar predictive value. The optimal model was selected, and together with the 5 variables used to construct a nomogram. The AUC of the MFP model in all patients was 0.76 (0.70, 0.82), whereas that in non-elderly patients (<60 years old) for the full model was 0.83 (0.74, 0.91). In elderly patients (≥60 years old), the AUC of the MFP model was 0.82 (0.73, 0.91).
Overall, the five-marker-based prognostic tool, established herein, can effectively predict the occurrence of delirium in EICU patients.
重症监护病房（ICU）谵妄是最常见的临床综合征之一，会导致许多影响患者、家庭和医院的不良事件。迄今为止，尚无有效预测急诊重症监护病房（EICU）患者谵妄发生的工具。
我们进行了一项回顾性队列研究，为319例符合纳入标准的EICU患者构建了预测模型。我们分析了患者入院24小时内的临床数据与谵妄之间的关系，应用单因素和多因素逻辑回归分析选择构建列线图模型最相关的变量，然后应用自抽样法进行内部验证。
共选择了五个变量用于模型构建，即胃管和尿管以及镇静剂、机械通气和急性生理与慢性健康状况评分系统（APACHE-II）评分。我们总共生成了五组模型（三组构建模型和两组内部验证模型），预测价值相似。选择了最优模型，并结合用于构建列线图的5个变量。所有患者中MFP模型的曲线下面积（AUC）为0.76（0.70，0.82），而全模型在非老年患者（<60岁）中的AUC为0.83（0.74，0.91）。在老年患者（≥60岁）中，MFP模型的AUC为0.82（0.73，0.91）。
总体而言，本文建立的基于五个标志物的预后工具可有效预测EICU患者谵妄的发生。
