Influencing Factors and Prediction Model Construction of Anxiety and Depression in Patients With Coronary Heart Disease Undergoing In-Stent Restenosis

Abstract

Background: In-stent Restenosis (ISR) represents a major challenge in interventional cardiology, and psychological distress is known to worsen coronary heart disease outcomes. However, the prevalence and determinants of anxiety and depression specifically in ISR patients remain largely uncharacterized. This study aimed to investigate the incidence and independent risk factors of these symptoms and to develop predictive models for their early identification.

Methods: A single-center retrospective study involving 256 patients with confirmed ISR was conducted. Psychological status was assessed using the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS). Univariate and multivariate logistic regression analyses were employed to identify independent risk factors. Nomograms were constructed and validated based on these factors, with model performance evaluated by the area under the curve (AUC), calibration plots, and Decision Curve Analysis (DCA).

Results: The prevalence of anxiety and depression symptoms was 37.89% and 42.58%, respectively. Multivariate analysis identified poorer sleep quality (higher Pittsburgh Sleep Quality Index (PSQI) score), lower social support (lower Social Support Rating Scale (SSRS) score), higher New York Heart Association (NYHA) classification (≥Ⅲ), multiple stents (≥2), and suburban/rural residence as independent risk factors for anxiety. For depression, older age, higher PSQI score, lower SSRS score, multiple stents, and advanced Mehran classification (III/IV) were significant predictors. The nomograms demonstrated good predictive accuracy, with AUCs of 0.79 (95% confidence interval (CI): 0.74–0.85) for anxiety and 0.82 (95% CI: 0.76–0.87) for depression, along with good calibration and clinical utility.

Conclusions: Anxiety symptoms and depressive symptoms are highly prevalent in ISR patients. The developed nomograms, incorporating clinical and psychosocial factors, provide effective tools for individualized risk stratification, facilitating early identification and targeted psychological interventions to improve patient outcomes.