作者：赵静，焦剑慧，沈慧，王红丽

单位：

225000江苏省扬州市，扬州大学附属医院心血管内科

单位（英文）：

Cardiovascular Medicine, Affiliated Hospital of Yangzhou University, Yangzhou 225000, China

关键词：

　心力衰竭；恐惧疾病进展；影响因素分析；列线图

关键词（英文）：

Heart failure; Fear of disease progression; Root cause analysis; Nomograms

中图分类号：

R 541.6

DOI：

10.12114/j.issn.1008-5971.2023.00.301

基金项目：

国家自然科学基金资助项目（82100428）；江苏省自然科学基金青年基金项目（BK20210142）；2022年扬州市护理 学会软科学研究培育课题项目（202212）；江苏省人兽共患病学重点实验室“护馨基金”项目（HX2213）

摘要：

目的　探讨老年慢性心力衰竭（CHF）患者发生恐惧疾病进展（FoP）的影响因素，并构建其风险预 测列线图模型。方法　采用随机抽样法选取2021年1月至2022年11月于扬州大学附属医院治疗的老年CHF患者为调查 对象。分别采用基线资料调查表、恐惧疾病进展简化量表（FoP-Q-SF）、社会支持评定量表（SSRS）、一般自我效 能量表（GSES）调查患者基线资料、FoP发生情况、社会支持水平和自我效能感水平。采用多因素Logistic回归分析 探讨老年CHF患者发生FoP的影响因素，采用R软件（4.0.2）构建老年CHF患者发生FoP的风险预测列线图模型，采用 ROC曲线、Hosmer-Lemeshow拟合优度检验评估该列线图模型的区分度、拟合情况。结果　本研究共发放问卷245份， 回收有效问卷223份，有效回收率为91.0%。223例患者FoP-Q-SF评分为（33.3±7.0）分，其中107例患者发生FoP， 发生率为48.0%。发生FoP患者与未发生FoP患者家庭人均月收入、病程、NYHA分级、左心室射血分数（LVEF）、 合并其他慢性病数量、社会支持水平、自我效能感水平比较，差异有统计学意义（P＜0.05）。多因素Logistic回归分 析结果显示，家庭人均月收入、病程、NYHA分级、LVEF、合并其他慢性病数量、社会支持水平、自我效能感水平 是老年CHF患者发生FoP的影响因素（P＜0.05）。基于多因素Logistic回归分析结果构建老年CHF患者发生FoP的风险 预测列线图模型。ROC曲线分析结果显示，该列线图模型预测老年CHF患者发生FoP的AUC为0.855〔95%CI（0.806， 0.904）〕，最佳截断值为 0.550 ，灵敏度为 76.4% ，特异度为 82.9%。Hosmer-Lemeshow 拟合优度检验结果显示，该列 线图模型拟合良好（χ 2 =8.128 ， P=0.421）。 结论 　家庭人均月收入、病程、 NYHA分级、 LVEF、合并其他慢性病数 量、社会支持水平、自我效能感水平是老年CHF患者发生FoP的影响因素，基于上述影响因素构建的老年CHF患者发生 FoP的风险预测列线图模型具有一定区分度。

英文摘要：

Objective To explore the influencing factors of fear of disease progression (FoP) in elderly patients with chronic heart failure (CHF) and construct its risk prediction nomogram model. Methods Elderly CHF patients treated in Affiliated Hospital of Yangzhou University from January 2021 to November 2022 were selected by random sampling method. The Baseline Information Questionnaire, Fear of Progression Questionnaire-Short Form (FOP-Q-SF) , Social Support Rating Scale (SSRS) and General Self-Efficacy Scale (GSES) were used to investigate the baseline data, the occurrence of FoP, the level of social support and the level of self-efficacy of the patients. Multivariate Logistic regression analysis was used to explore the influencing factors of FoP in elderly CHF patients. R software (4.0.2) was used to construct the risk prediction nomogram model for FoP in elderly CHF patients. ROC curve and Hosmer-Lemeshow goodness of fit test were used to evaluate the discrimination and fit of the nomogram model. Results A total of 245 questionnaires were sent out in this study, and 223 were effectively collected, with an effective recovery rate of 91.0%. The FoP-Q-SF score of 223 patients was (33.3±7.0) , of which 107 patients developed FoP, with an incidence rate of 48.0%. There were statistically significant differences in per capita monthly income, course of disease, NYHA grading, left ventricular ejection fraction (LVEF) , the number of other chronic diseases, social support level, and self-efficacy level between FoP patients and non-FOP patients (P < 0.05) . The results of multivariate Logistic regression analysis showed that per capita monthly income, course of disease, NYHA grading, LVEF, the number of other chronic diseases, social support level, and self-efficacy level were the influencing factors for FoP in elderly CHF patients (P < 0.05) . Based on the results of multivariate Logistic regression analysis, the risk prediction nomogram model for FoP in elderly CHF patients was constructed. The results of ROC curve analysis showed that the AUC of the nomogram model for predicting FoP in elderly CHF patients was 0.855 [95%CI (0.806, 0.904) ] , the best cut-off value was 0.550, the sensitivity was 76.4%, and the specificity was 82.9%. The Hosmer Lemeshow goodness of fit test showed that the nomogram model fitted well ( χ 2 =8.128, P=0.421) . Conclusion Per capita monthly income, course of disease, NYHA grading, LVEF, the number of other chronic diseases, social support level, and self efficacy level are the influencing factors of FoP in elderly CHF patients. The risk prediction nomogram model for FoP in elderly CHF patients constructed based on the above influencing factors has a certain degree of discrimination.

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