Wang Faying, Li Jingshu, Fan Yuying, Qi Xiaona
Clinical Nursing Teaching Department, Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Office of General Affairs, School of Nursing, Harbin Medical University, Harbin, China.
Nurs Crit Care. 2024 Jul;29(4):646-660. doi: 10.1111/nicc.12978. Epub 2023 Sep 12.
Postintensive care syndrome (PICS) has adverse multidimensional effects on nearly half of the patients discharged from ICU. Mental disorders such as anxiety, depression and post-traumatic stress disorder (PTSD) are the most common psychological problems for patients with PICS with harmful complications. However, developing prediction models for mental disorders in post-ICU patients is an understudied problem.
To explore the risk factors of PICS mental disorders, establish the prediction model and verify its prediction efficiency.
In this cohort study, data were collected from 393 patients hospitalized in the ICU of a tertiary hospital from April to September 2022. Participants were randomly assigned to modelling and validation groups using a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to select the predictors, multiple logistic regression analysis was used to establish the risk prediction model, and a dynamic nomogram was developed. The Hosmer-Lemeshow (HL) test was performed to determine the model's goodness of fit. The area under the receiver operating characteristic (ROC) curve was used to evaluate the model's prediction efficiency.
The risk factors of mental disorders were Sepsis-related organ failure assessment (SOFA) score, Charlson comorbidity index (CCI), delirium duration, ICU depression score and ICU sleep score. The HL test revealed that p = .249, the area under the ROC curve = 0.860, and the corresponding sensitivity and specificity were 84.8% and 71.0%, respectively. The area under the ROC curve of the verification group was 0.848. A mental disorders dynamic nomogram for post-ICU patients was developed based on the regression model.
The prediction model provides a reference for clinically screening patients at high risk of developing post-ICU mental disorders, to enable the implementation of timely preventive management measures.
The dynamic nomogram can be used to systematically monitor various factors associated with mental disorders. Furthermore, nurses need to develop and apply accurate nursing interventions that consider all relevant variables.
重症监护后综合征（PICS）对近一半从重症监护病房（ICU）出院的患者产生多维度不良影响。焦虑、抑郁和创伤后应激障碍（PTSD）等精神障碍是PICS患者最常见的心理问题，且伴有有害并发症。然而，针对ICU后患者精神障碍的预测模型研究较少。
探讨PICS精神障碍的危险因素，建立预测模型并验证其预测效率。
在这项队列研究中，收集了2022年4月至9月在一家三级医院ICU住院的393例患者的数据。参与者按7:3的比例随机分配到建模组和验证组。采用最小绝对收缩和选择算子（LASSO）回归分析选择预测因子，多元逻辑回归分析建立风险预测模型，并绘制动态列线图。进行Hosmer-Lemeshow（HL）检验以确定模型的拟合优度。采用受试者工作特征（ROC）曲线下面积评估模型的预测效率。
精神障碍的危险因素为脓毒症相关器官功能衰竭评估（SOFA）评分、Charlson合并症指数（CCI）、谵妄持续时间、ICU抑郁评分和ICU睡眠评分。HL检验显示p = 0.249，ROC曲线下面积 = 0.860，相应的敏感性和特异性分别为84.8%和71.0%。验证组的ROC曲线下面积为0.848。基于回归模型绘制了ICU后患者精神障碍动态列线图。
该预测模型为临床筛查有发生ICU后精神障碍高风险的患者提供了参考，以便能够及时实施预防性管理措施。
动态列线图可用于系统监测与精神障碍相关的各种因素。此外，护士需要制定并应用考虑所有相关变量的准确护理干预措施。
