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A web-based novel prediction model for predicting depression in elderly patients with coronary heart disease: A multicenter retrospective, propensity-score matched study.
Tan, Juntao; Xu, Zhengguo; He, Yuxin; Zhang, Lingqin; Xiang, Shoushu; Xu, Qian; Xu, Xiaomei; Gong, Jun; Tan, Chao; Tan, Langmin.
Afiliación
  • Tan J; Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
  • Xu Z; Department of Teaching and Research, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
  • He Y; Department of Medical Administration, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, China.
  • Zhang L; Department of Biomedical Equipment, People's Hospital of Chongqing Bishan District, Chongqing, China.
  • Xiang S; College of Medical Informatics, Chongqing Medical University, Chongqing, China.
  • Xu Q; College of Medical Informatics, Chongqing Medical University, Chongqing, China.
  • Xu X; Medical Data Science Academy, Chongqing Medical University, Chongqing, China.
  • Gong J; Library, Chongqing Medical University, Chongqing, China.
  • Tan C; Department of Gastroenterology, The Fifth People's Hospital of Chengdu, Chengdu, China.
  • Tan L; Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Psychiatry ; 13: 949753, 2022.
Article en En | MEDLINE | ID: mdl-36329913
Background: Depression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients with CHD and to construct a prediction model for early identification of depression in this patient population. Materials and methods: We used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set (n = 880) and an external validation set (n = 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively. Results: The predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722-0.803) and 0.679 (95% CI = 0.572-0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (https://cytjt007.shinyapps.io/dynnomapp_depression/). Conclusion: Reductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD.
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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Psychiatry Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Psychiatry Año: 2022 Tipo del documento: Article País de afiliación: China