Zhang Jinyan, Zhou Shenghong
Department of Internal Medicine of TCM, Affiliated Hospital of Shandong Academy of Traditional Chinese Medicine, Jinan, 250000, China.
Institute of Basic Theory of Chinese Medicine, Shandong Academy of Chinese Medicine, Jinan, 250000, China.
BMC Public Health. 2025 Jun 6;25(1):2110. doi: 10.1186/s12889-025-23342-7.
Patients with chronic obstructive pulmonary disease (COPD) are at an elevated risk for depression. However, effective predictive tools for identifying high-risk individuals are currently lacking. This study aims to develop a nomogram for predicting depression risk in COPD patients.
A total of 1,671 participants from NHANES 2007-2012 were included in the study. The data were divided into training and testing sets in a 7:3 ratio. LASSO regression was employed to identify the optimal predictors in the training set. Subsequently, univariate and multivariate logistic regression analyses were conducted to determine independent predictors for constructing the nomogram. The model was then evaluated using the C-index, calibration curve, Hosmer-Lemeshow test, and decision curve analysis (DCA). And conducted a sensitivity analysis to assess the robustness of the model's predictive performance. Finally, the Youden index was used to determine the optimal prediction threshold.
Eight predictors were selected for the model, including age, gender, marital status, poverty income ratio (PIR), body mass index(BMI), sleep disorder, work limitation, and social barriers. The C-index for the training and test sets were 0.71 and 0.72, respectively, indicating significant classification performance. All four evaluation methods demonstrated that the model has strong discriminatory ability, calibration, and clinical utility. Additionally, the threshold for predicting risk and the corresponding score from the nomogram were 0.57 and 93, respectively. The sensitivity analysis demonstrated the robustness of the results, with the model exhibiting good discrimination and calibration across different gender and age groups.
The nomogram has potential value in the preliminary prediction of depression risk in COPD patients, facilitating the early initiation of preventive interventions for depression. Future studies should focus on optimizing the model and validating its performance in larger, more diverse populations.
慢性阻塞性肺疾病（COPD）患者患抑郁症的风险较高。然而，目前缺乏有效的预测工具来识别高危个体。本研究旨在开发一种预测COPD患者抑郁症风险的列线图。
本研究纳入了2007 - 2012年美国国家健康与营养检查调查（NHANES）的1671名参与者。数据按7:3的比例分为训练集和测试集。采用LASSO回归在训练集中识别最佳预测因子。随后，进行单因素和多因素逻辑回归分析，以确定构建列线图的独立预测因子。然后使用C指数、校准曲线、Hosmer-Lemeshow检验和决策曲线分析（DCA）对模型进行评估。并进行敏感性分析以评估模型预测性能的稳健性。最后，使用约登指数确定最佳预测阈值。
为模型选择了8个预测因子，包括年龄、性别、婚姻状况、贫困收入比（PIR）、体重指数（BMI）、睡眠障碍、工作限制和社会障碍。训练集和测试集的C指数分别为0.71和0.72，表明具有显著的分类性能。所有四种评估方法均表明该模型具有很强的鉴别能力、校准能力和临床实用性。此外，预测风险的阈值和列线图对应的分数分别为0.57和93。敏感性分析表明结果具有稳健性，该模型在不同性别和年龄组中均表现出良好的鉴别能力和校准能力。
该列线图在初步预测COPD患者抑郁症风险方面具有潜在价值，有助于早期开展抑郁症预防干预。未来的研究应侧重于优化模型并在更大、更多样化的人群中验证其性能。
