Dong Haili, Liu Li, Ma Shasha, Han Haixia, Zhang Jiadong, Salvador Jordan Tovera, Liu Xiaoxiao
Department of Nursing, Binzhou Medical University Hospital, Binzhou, Shandong Province, People's Republic of China.
School of Nursing, Binzhou Medical University, Binzhou, Shandong Province, People's Republic of China.
Risk Manag Healthc Policy. 2025 Mar 26;18:1031-1043. doi: 10.2147/RMHP.S490487. eCollection 2025.
To construct and validate a nomogram model predicting the risk of post-intensive care syndrome-family (PICS-F) in family members of critically ill patients.
This study was conducted on family members (parents, spouses, or children) of critically ill patients in the three intensive care units of Binzhou Medical University Hospital from December 2023 to June 2024, responsible for medical decisions and primary care. The sleep disturbances, fatigue, anxiety, and depression were assessed using the Pittsburgh Sleep Quality Index, the Subscale of Fatigue Assessment Instrument, and the Hospital Anxiety and Depression Scale, respectively. Predictive factors were identified through univariate and multivariate logistic regression, and a nomogram was constructed using R4.2.3. Internal validation used the Bootstrap sampling method, and external validation employed the time-period method.
The study involved 567 participants divided into a modeling group (n = 432; median age = 46 years; 209 males, 223 females) and an external validation group (n = 135; median age = 45 years; 70 males, 65 females). The model included five predictive factors: family age, patient age, APACHE II score, average monthly income per family member, and PSSS score. The AUC of the modeling group was 0.894 (0.864 ~ 0.924), with a specificity of 85.4%, a sensitivity of 78.0%, and a maximum Youden index of 0.634. The H-L test revealed a good fit ( value = 9.528, = 0.300). The internal validation results of the Bootstrap sampling method showed that the calibration curve of the model was close to the ideal curve, and the DCA curve results indicated high clinical practicality. Moreover, the external validation results showed that AUC was 0.847 (0.782 ~ 0.912), with sensitivity and specificity of 74.5% and 86.3%, respectively. The H-L test results indicated a good fit ( value = 9.625, = 0.292).
The nomogram demonstrated strong predictive performance for PICS-F risk in ICU patients' families, offering a valuable tool for clinical assessment.
构建并验证一种预测重症患者家庭成员发生重症后综合征-家庭版（PICS-F）风险的列线图模型。
本研究于2023年12月至2024年6月在滨州医学院附属医院三个重症监护病房中对重症患者的家庭成员（父母、配偶或子女）进行，这些家庭成员负责医疗决策和初级护理。分别使用匹兹堡睡眠质量指数、疲劳评估工具子量表和医院焦虑抑郁量表评估睡眠障碍、疲劳、焦虑和抑郁情况。通过单因素和多因素逻辑回归确定预测因素，并使用R4.2.3构建列线图。内部验证采用Bootstrap抽样方法，外部验证采用时间周期法。
该研究纳入567名参与者，分为建模组（n = 432；中位年龄 = 46岁；男性209名，女性223名）和外部验证组（n = 135；中位年龄 = 45岁；男性70名，女性65名）。该模型包括五个预测因素：家庭成员年龄、患者年龄、APACHE II评分、家庭成员月平均收入和PSSS评分。建模组的AUC为0.894（0.864 ~ 0.924），特异性为85.4%，敏感性为78.0%，最大约登指数为0.634。H-L检验显示拟合良好（χ²值 = 9.528，P = 0.300）。Bootstrap抽样方法的内部验证结果表明，该模型的校准曲线接近理想曲线，DCA曲线结果表明具有较高的临床实用性。此外，外部验证结果显示，AUC为0.847（0.782 ~ 0.912），敏感性和特异性分别为74.5%和86.3%。H-L检验结果表明拟合良好（χ²值 = 9.625，P = 0.292）。
该列线图对ICU患者家庭中PICS-F风险具有较强的预测性能，为临床评估提供了有价值的工具。
