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. 2025 Mar 26:18:1031-1043.
doi: 10.2147/RMHP.S490487. eCollection 2025.

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

Haili Dong  1   2 , Li Liu  1 , Shasha Ma  3 , Haixia Han  4 , Jiadong Zhang  5 , Jordan Tovera Salvador  6 , Xiaoxiao Liu  2
Affiliations

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

Haili Dong et al. Risk Manag Healthc Policy. .

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.

Keywords: nomogram model; nursing; post-intensive care syndrome-family; prediction model.

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Conflict of interest statement

The authors declare that they have no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Nomogram to detect PICS-F for family members. APACHE II indicates Acute Physiology and Chronic Health Evaluation II; PSSS indicates Perceived Social Support Scale.
Figure 2
Figure 2
Nomogram ROC curves generated from the training dataset. The area under the ROC curve of the training dataset was 0.894 (95% CI: 0.864~0.924).
Figure 3
Figure 3
Calibration plot for the training dataset. The x-axis showed the predicted probability of the nomogram, the y-axis showed the actual prediction results.
Figure 4
Figure 4
Nomogram ROC curves generated from the externally verified dataset. The area under the ROC curve of the external validation AUC was 0.847 (ranging from 0.782 to 0.912).
Figure 5
Figure 5
DCA curves for the training dataset. The decision curve graph showed the net benefit of the intervention measures taken by the established model on the families with post-intensive care syndrome-family.
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