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

Anxiety symptoms were present in 37.89% of patients with in-stent restenosis.

• Single-center retrospective study of 256 patients with confirmed ISR
• Psychological status assessed using the Self-Rating Anxiety Scale (SAS)
• Prevalence of 37.89% indicates anxiety as a common comorbidity in this population
• Study population was drawn from a single interventional cardiology center

Depression symptoms were present in 42.58% of patients with in-stent restenosis.

• Assessed using the Self-Rating Depression Scale (SDS) in 256 ISR patients
• Depression prevalence (42.58%) was higher than anxiety prevalence (37.89%) in this cohort
• Both conditions were characterized as 'highly prevalent' by the authors
• Study design was retrospective and single-center

Five independent risk factors for anxiety were identified in ISR patients: poorer sleep quality, lower social support, higher NYHA classification (≥III), multiple stents (≥2), and suburban/rural residence.

• Identified via multivariate logistic regression analysis
• Sleep quality measured by the Pittsburgh Sleep Quality Index (PSQI); higher scores indicate poorer sleep
• Social support measured by the Social Support Rating Scale (SSRS); lower scores indicate less support
• NYHA classification ≥III and use of ≥2 stents were clinical predictors
• Suburban/rural residence was included as a sociodemographic risk factor

Five independent risk factors for depression were identified in ISR patients: older age, poorer sleep quality, lower social support, multiple stents (≥2), and advanced Mehran classification (III/IV).

• Identified via multivariate logistic regression analysis in the same 256-patient cohort
• Mehran classification III/IV represents more complex or severe restenosis patterns
• Sleep quality (PSQI) and social support (SSRS) were shared risk factors with anxiety
• Older age and advanced Mehran classification were unique predictors for depression compared to the anxiety model

The nomogram for predicting anxiety demonstrated good predictive accuracy with an AUC of 0.79 (95% CI: 0.74–0.85).

• Model performance evaluated by AUC, calibration plots, and Decision Curve Analysis (DCA)
• AUC of 0.79 indicates good discriminatory ability for anxiety prediction
• 95% confidence interval ranged from 0.74 to 0.85
• Good calibration and clinical utility were also reported alongside AUC

The nomogram for predicting depression demonstrated good predictive accuracy with an AUC of 0.82 (95% CI: 0.76–0.87).

• AUC of 0.82 indicates good discriminatory ability for depression prediction
• 95% confidence interval ranged from 0.76 to 0.87
• The depression model showed slightly higher discriminatory performance than the anxiety model (0.82 vs. 0.79)
• Model validation included calibration plots and Decision Curve Analysis (DCA)

Both nomograms incorporated a combination of clinical and psychosocial factors to enable individualized risk stratification.

• Nomograms were constructed and validated based on factors identified in multivariate logistic regression
• Psychosocial factors (sleep quality and social support) appeared as predictors in both models
• Authors note the tools are intended to facilitate 'early identification and targeted psychological interventions'
• The models are described as providing 'effective tools for individualized risk stratification'