关键词: 慢性心力衰竭；疾病进展恐惧；心理弹性；影响因素；预测模型；护理

Abstract: Objective: To investigate the current status and influencing factors of Fear of Progression (FoP) in patients with Chronic Heart Failure (CHF), to construct a risk prediction model and to verify the prediction effect of the model. Methods: A total of 188 patients with CHF who hospitalized in the department of cardiology of 2 tertiary hospitals from November 2021 to March 2022 were selected as the subjects by convenience sampling. The risk factors between dysfunctional FoP group (n=64) and non-dysfunctional FoP group (n=124) were compared. The risk prediction model was constructed with Logistic regression analysis using the self-regulation common sense model as the theoretical framework. Hosmer-Lemeshow test was used to test the goodness of fit and the area under the Receiver Operating Characteristic curve (AUC) was used to evaluate the efficacy of the model. Eighty CHF patients were selected to verify the prediction effect of the model. Results: The incidence of dysfunctional FoP in patients with CHF was 34.04%. Age (OR=0.909), left atrial diameter (OR=1.074), New York Heart Association function classification (OR=9.156), number of non-cardiovascular comorbidities (OR=4.222), Heart Failure Somatic Perception Scale score (OR=1.153), Connor-Davidson Resilience Scale-10 item (OR=0.749), and Self-care of Heart Failure Index (OR=0.926) were independent influencing factors of dysfunctional FoP in patients with CHF (P<0.05). The AUC of the model was 0.926, the sensitivity was 81.5%, the specificity was 89.1%, and the Youden index was 0.706. The prediction accuracy was 88.75%. Conclusion: The risk prediction model for FoP in patients with CHF constructed in this study has good predictive effects, which is suitable for clinical screening. It might provide a scientific reference for nursing staff to identify high-risk populations and to take targeted positive psychological interventions.

Key words: Chronic Heart failure; Fear of Progression; psychological resilience; risk factor; prediction model; nursing care