Li Shanshan, Zhang Le, Yang Boyi, Huang Yi, Guan Yuqi, Huang Nanbo, Wu Yingnan, Wang Wenshuo, Wang Qing, Cai Haochen, Sun Yong, Xu Zijun, Wu Qin
Medical College, Jiangsu Vocational College of Medicine, Yancheng, People's Republic of China.
Jiangsu Engineering Research Centers for Cardiovascular and Cerebrovascular Disease and Cancer Prevention and Control, Jiangsu Vocational College of Medicine, Yancheng, People's Republic of China.
Diabetes Metab Syndr Obes. 2024 Jul 1;17:2627-2638. doi: 10.2147/DMSO.S465052. eCollection 2024.
In elderly diabetic patients, depression is often overlooked because professional evaluation requires psychiatrists, but such specialists are lacking in the community. Therefore, we aimed to create a simple depression screening model that allows earlier detection of depressive disorders in elderly diabetic patients by community health workers.
The prediction model was developed in a primary cohort that consisted of 210 patients with diabetes, and data were gathered from December 2022 to February 2023. The independent validation cohort included 99 consecutive patients from February 2023 to March 2023. Multivariable logistic regression analysis was used to develop the predictive model. We incorporated common demographic characteristics, diabetes-specific factors, family structure characteristics, the self-perceived burden scale (SPBS) score, and the family APGAR (adaptation, partnership, growth, affection, resolution) score. The performance of the nomogram was assessed with respect to its calibration (calibration curve, the Hosmer-Lemeshow test), discrimination (the area under the curve (AUC)), and clinical usefulness (Decision curve analysis (DCA)).
The prediction nomogram incorporated 5 crucial factors such as glucose monitoring status, exercise status, monthly income, sleep disorder status, and the SPBS score. The model demonstrated strong discrimination in the primary cohort, with an AUC of 0.839 (95% CI, 0.781-0.897). This discriminative ability was further validated in the validation cohort, with an AUC of 0.857 (95% CI, 0.779-0.935). Moreover, the nomogram exhibited satisfactory calibration. DCA suggested that the prediction of depression in elderly patients with diabetes mellitus was of great clinical value.
The prediction model provides precise and user-friendly guidance for community health workers in preliminary screenings for depression among elderly patients with diabetes.
在老年糖尿病患者中，抑郁症常常被忽视，因为专业评估需要精神科医生，但社区缺乏此类专家。因此，我们旨在创建一个简单的抑郁症筛查模型，以便社区卫生工作者能够更早地发现老年糖尿病患者的抑郁症。
预测模型在一个由210名糖尿病患者组成的初级队列中开发，数据收集于2022年12月至2023年2月。独立验证队列包括2023年2月至2023年3月的99名连续患者。采用多变量逻辑回归分析来开发预测模型。我们纳入了常见的人口统计学特征、糖尿病特异性因素、家庭结构特征、自我感知负担量表（SPBS）评分和家庭APGAR（适应、伙伴关系、成长、情感、解决）评分。通过校准（校准曲线、Hosmer-Lemeshow检验）、区分度（曲线下面积（AUC））和临床实用性（决策曲线分析（DCA））来评估列线图的性能。
预测列线图纳入了5个关键因素，如血糖监测状况、运动状况、月收入、睡眠障碍状况和SPBS评分。该模型在初级队列中表现出很强的区分度，AUC为0.839（95%CI，0.781-0.897）。这种区分能力在验证队列中得到进一步验证，AUC为0.857（95%CI，0.779-0.935）。此外，列线图表现出令人满意的校准。DCA表明，预测老年糖尿病患者的抑郁症具有很大的临床价值。
该预测模型为社区卫生工作者在老年糖尿病患者抑郁症初步筛查中提供了精确且用户友好的指导。
