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Influencing Factors and Prediction Model Construction of Anxiety and Depression in Patients With Coronary Heart

Sihui Sun1, Xiaoxian Wu1, Lichen Tang1

  • 1Department of Cardiology, Nanjing First Hospital, Nanjing Medical University, 210000 Nanjing, Jiangsu, China.

Actas Espanolas De Psiquiatria
|August 23, 2026
PubMed

Insights

Anxiety and depression are common in in-stent restenosis (ISR) patients. Predictive models using clinical and psychosocial factors can help identify at-risk individuals for timely psychological support.

Area of Science:

  • Cardiology
  • Psychology
  • Medical Informatics

Background:

  • In-stent restenosis (ISR) poses significant challenges in interventional cardiology.
  • Psychological distress is linked to poorer outcomes in coronary heart disease.
  • Prevalence and determinants of anxiety and depression in ISR patients are not well-understood.

Purpose of the Study:

  • To investigate the incidence of anxiety and depression in ISR patients.
  • To identify independent risk factors for these psychological symptoms.
  • To develop and validate predictive models for early identification of anxiety and depression in ISR.

Main Methods:

  • Retrospective study of 256 ISR patients.
  • Assessment of psychological status using Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS).
  • Logistic regression for risk factor identification; nomograms constructed and validated using AUC, calibration plots, and DCA.

Main Results:

  • Anxiety prevalence: 37.89%; Depression prevalence: 42.58%.
  • Independent risk factors for anxiety: poor sleep quality (PSQI), low social support (SSRS), high NYHA class, multiple stents, suburban/rural residence.
  • Independent risk factors for depression: older age, high PSQI, low SSRS, multiple stents, advanced Mehran classification.
  • Nomograms showed good predictive accuracy (AUC 0.79 for anxiety, 0.82 for depression) with good calibration and clinical utility.

Conclusions:

  • Anxiety and depression are highly prevalent in ISR patients.
  • Developed nomograms effectively stratify risk based on clinical and psychosocial factors.
  • Nomograms facilitate early identification and targeted psychological interventions to improve ISR patient outcomes.
Abstract

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