Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, People's Republic of China.
BMC Med. 2024 Apr 19;22(1):167. doi: 10.1186/s12916-024-03388-x.
The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain.
Participants were from the UK Biobank. The primary outcome was a "lifetime" history of depression. The model's performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot).
Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a "lifetime" history of depression was 45.7% and varied (25.0-66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a "lifetime" history of depression was 30.2% and varied (21.4-70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI.
There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients' treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them.
由于研究样本和抑郁定义的异质性，慢性疼痛人群中抑郁的患病率尚不清楚。我们旨在确定慢性疼痛人群中抑郁患病率的变化来源，并生成临床预测模型来估计慢性疼痛个体抑郁的概率。
参与者来自英国生物库。主要结局是“终生”抑郁史。使用区分度（乐观校正 C 统计量）和校准（校准图）来评估模型的性能。
分析包括 24405 名慢性疼痛患者（平均年龄 64.1 岁）。在患有慢性广泛性疼痛的患者中，有“终生”抑郁史的患病率为 45.7%，并根据患者特征而有所不同（25.0-66.7%）。最终的临床预测模型（乐观校正 C 统计量：0.66；校准图上的良好校准）包括年龄、BMI、吸烟状况、身体活动、社会经济地位、性别、哮喘史、心力衰竭史和外周动脉疾病史。在患有慢性区域性疼痛的患者中，有“终生”抑郁史的患病率为 30.2%，并根据患者特征而有所不同（21.4-70.6%）。最终的临床预测模型（乐观校正 C 统计量：0.65；校准图上的良好校准）包括年龄、性别、疼痛性质、吸烟状况、常规阿片类药物使用、哮喘史、最困扰您的疼痛部位和 BMI。
慢性疼痛患者中抑郁的患病率存在很大差异。选择了有临床意义的因素来开发预测模型。临床医生可以使用这些模型来评估患者的治疗需求。这些预测因子在日常实践中收集方便，便于忙碌的临床医生使用。
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