Development and Validation of a Nomogram Prediction Model for Key Symptoms of Post-Intensive Care Syndrome-Family in Family Members of Critically-Ill Patients: Focusing on Sleep Disturbance, Fatigue, Anxiety, and Depression.

IF 2.7 4区医学 Q2 HEALTH CARE SCIENCES & SERVICES

Risk Management and Healthcare Policy Pub Date : 2025-03-26 eCollection Date: 2025-01-01 DOI:10.2147/RMHP.S490487

Haili Dong, Li Liu, Shasha Ma, Haixia Han, Jiadong Zhang, Jordan Tovera Salvador, Xiaoxiao Liu

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引用次数: 0

Abstract

Purpose: 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.

Methods: 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.

Results: 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 (X ² value = 9.528, P = 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 (X ² value = 9.625, P = 0.292).

Conclusion: The nomogram demonstrated strong predictive performance for PICS-F risk in ICU patients' families, offering a valuable tool for clinical assessment.

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目的：构建并验证预测重症患者家属重症监护后综合征（PICS-F）风险的提名图模型：本研究以 2023 年 12 月至 2024 年 6 月期间滨州医科大学附属医院三个重症监护病房负责医疗决策和初级护理的重症患者家属（父母、配偶或子女）为研究对象。分别使用匹兹堡睡眠质量指数、疲劳评估量表和医院焦虑抑郁量表对患者的睡眠障碍、疲劳、焦虑和抑郁进行评估。通过单变量和多变量逻辑回归确定了预测因素，并使用 R4.2.3 绘制了提名图。内部验证采用 Bootstrap 抽样法，外部验证采用时间段法：研究涉及 567 名参与者，分为建模组（n = 432；中位年龄 = 46 岁；男性 209 人，女性 223 人）和外部验证组（n = 135；中位年龄 = 45 岁；男性 70 人，女性 65 人）。模型包括五个预测因素：家庭年龄、患者年龄、APACHE II 评分、每个家庭成员的平均月收入和 PSSS 评分。建模组的 AUC 为 0.894（0.864 ~ 0.924），特异性为 85.4%，灵敏度为 78.0%，最大尤登指数为 0.634。H-L 检验显示拟合度良好（X 2 值 = 9.528，P = 0.300）。Bootstrap 抽样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，外部验证结果表明，AUC 为 0.847（0.782 ~ 0.912），灵敏度和特异度分别为 74.5%和 86.3%。H-L 检验结果显示拟合度良好（X 2 值 = 9.625，P = 0.292）：该提名图对 ICU 患者家属的 PICS-F 风险具有很强的预测能力，为临床评估提供了一个有价值的工具。

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来源期刊

Risk Management and Healthcare Policy Medicine-Public Health, Environmental and Occupational Health

CiteScore

6.20

自引率

2.90%

发文量

242

审稿时长

16 weeks

期刊介绍： Risk Management and Healthcare Policy is an international, peer-reviewed, open access journal focusing on all aspects of public health, policy and preventative measures to promote good health and improve morbidity and mortality in the population. Specific topics covered in the journal include: Public and community health Policy and law Preventative and predictive healthcare Risk and hazard management Epidemiology, detection and screening Lifestyle and diet modification Vaccination and disease transmission/modification programs Health and safety and occupational health Healthcare services provision Health literacy and education Advertising and promotion of health issues Health economic evaluations and resource management Risk Management and Healthcare Policy focuses on human interventional and observational research. The journal welcomes submitted papers covering original research, clinical and epidemiological studies, reviews and evaluations, guidelines, expert opinion and commentary, and extended reports. Case reports will only be considered if they make a valuable and original contribution to the literature. The journal does not accept study protocols, animal-based or cell line-based studies.