Development and validation of a risk prediction model for demoralization syndrome in patients with chronic heart failure

Abstract

To investigate the associated factors for demoralization syndrome in patients with chronic heart failure (CHF) and to develop a risk prediction model that facilitates early identification of high-risk individuals. A total of 331 patients with CHF were recruited from two hospitals in Anhui Province, China, between March 2025 and February 2026 using convenience sampling. Demographic and clinical data were collected, and validated scales were used to assess demoralization, social support, and symptom burden. Univariate and multivariable logistic regression analyses were performed—specifically, univariate analysis was employed to screen for associated factors, while multivariable regression was applied to identify independent predictors and construct the prediction model. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Demoralization syndrome was identified in 57.7% of patients. In the univariate analyses, several variables emerged as associated factors. In the multivariable model, exercise frequency, NYHA functional class, SSRS score, KCCQ score, and blood stasis constitution were identified as independent predictors and were incorporated into the final prediction model. The AUC was 0.835 (95% CI: 0.783–0.886) in the training set and 0.822 (95% CI: 0.735–0.909), in the validation set. The model showed good calibration and favorable clinical net benefit. Exercise frequency, NYHA functional class, social support, symptom burden, and blood stasis constitution are independent predictors of demoralization syndrome in patients with CHF. The proposed prediction model demonstrated robust discrimination and calibration in internal validation, suggesting its potential as a preliminary auxiliary reference for risk awareness.