Construction of risk prediction model for fear of disease progression in stroke patients
10.3760/cma.j.cn115682-20240204-00703
- VernacularTitle:脑卒中患者发生恐惧疾病进展的风险预测模型构建
- Author:
Jingjing JIA
1
;
Xinping WANG
;
Jia LIU
;
Yuanyuan GUI
;
Hongmin LIU
Author Information
1. 齐齐哈尔医学院基础护理学教研室，齐齐哈尔 161000
- Keywords:
Stroke;
Fear of disease progression;
Predictive model;
Nomogram
- From:
Chinese Journal of Modern Nursing
2024;30(20):2737-2743
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore influencing factors of the occurrence of fear of disease progression (FoP) in stroke patients and to construct a risk prediction nomogram model.Methods:A total of 201 stroke patients of the First Affiliated Hospital of Qiqihar Medical University and the Third Affiliated Hospital of Qiqihar Medical University from July to December 2023 were selected as the survey objects by the convenient sampling method. Baseline data questionnaire, Fear of Progression Questionnaire-Short Form (FoP-Q-SF), Chinese version of Perceived Stress Scale (CPSS), Social Support Rating Scale (SSRS) and Fatigue Severity Scale (FSS) were used to investigate of patients FoP occurrence, stress perception, social support level and fatigue level of stroke patients. The influencing factors of FoP occurrence in stroke patients were explored and R 4.3.2 was used to construct a risk prediction nomogram model for FoP occurrence in stroke patients. The performance of the model was evaluated using receiver operating characteristic curve, calibration curve, and clinical decision curve from the perspectives of discrimination, calibration and clinical practicality.Results:In this study, a total of 201 questionnaires were collected, 199 were valid, and the effective rate was 99%. The FOP-Q-SF score of 199 patients was (29.64±9.50), of which 71 patients (35.7%) developed FoP. Educational level, complications, self-care ability, social support, stress perception and fatigue were the influencing factors of FoP in stroke patients ( P<0.05). A risk prediction nomogram model for FoP in stroke patients was constructed based on the results of binary Logistic regression analysis. The Hosmer-Lemeshow results showed that the nomogram model fitted well (χ 2=10.466, P=0.234). The area under ROC curve was 0.912 (95% CI: 0.871-0.952). The clinical decision curve showed that when the threshold probability ranged from 4% to 99%, choosing this model to predict the risk of FoP could benefit stroke clinically. Conclusions:Educational level, complications, self-care ability, social support, stress perception and fatigue are the influencing factors for fear of disease progression in stroke patients. The risk nomogram model based on multiple evaluation indexes has certain clinical value.
