Construction of a nomogram model to predict the risk of anxiety depression in elderly bronchiectasis
10.3760/cma.j.cn115455-20230914-00258
- VernacularTitle:个体化预测老年支气管扩张症患者焦虑抑郁发生风险的列线图模型构建
- Author:
Jing LI
1
;
Jiazhen WANG
1
Author Information
1. 四川省三台县人民医院呼吸与危重症医学科，绵阳 621100
- Publication Type:Journal Article
- Keywords:
Bronchiectasis;
Anxiety;
Depressive disorder;
Nomograms;
Risk factors
- From:
Chinese Journal of Postgraduates of Medicine
2025;48(2):173-177
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the risk factors of anxiety and depression in elderly bronchiectasis patients and establish a nomogram model.Methods:A total of 90 elderly patients with bronchiectasis who were treated in Sichuan Santai County People′s Hospital from January 2020 to January 2023 were selected as the research subjects. The incidence of anxiety and depression in patients was observed, influencing factors were analyzed by univariate and multivariate Logistic regression analysis, and a predictive risk column chart model was established.Results:In 90 elderly patients, 40 patients experienced anxiety and depression, with an incidence rate of 44.44%. Univariate and multivariate Logistic regression analysis showed that hospitalization frequency, hemoptysis, educational level, arterial partial oxygen pressure (PaO 2), severity of the condition, copper green colonization, length of hospital stay, monthly family income, and family happiness index were risk factors for anxiety and depression in elderly patients with bronchiectasis ( P<0.05). A column line prediction model for anxiety and depression in elderly patients with bronchiectasis based on 9 independent risk factors was drawn. The calibration verification result show that the predicted values of the model had a high degree of fit with the measured values. The internal validation result of bootstrap showed that the C-index was 0.724 (95% CI 0.670 -0.779). Conclusions:The occurrence of anxiety and depression in elderly patients with bronchiectasis can be influenced by various factors. The model has high predictive performance.
