|
1
|
Chen L, Ashton-James CE, Shi B, Radojčić MR, Anderson DB, Chen Y, Preen DB, Hopper JL, Li S, Bui M, Beckenkamp PR, Arden NK, Ferreira PH, Zhou H, Feng S, Ferreira ML. Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models. BMC Med 2024; 22:167. [PMID: 38637815 PMCID: PMC11027372 DOI: 10.1186/s12916-024-03388-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 04/11/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND 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. METHODS 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). RESULTS 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. CONCLUSIONS 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.
Collapse
Affiliation(s)
- Lingxiao Chen
- 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
- Sydney Musculoskeletal Health, The Kolling Institute, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Claire E Ashton-James
- Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Baoyi Shi
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, USA
| | - Maja R Radojčić
- Division of Psychology and Mental Health, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - David B Anderson
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Yujie Chen
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - David B Preen
- School of Population and Global Health, The University of Western Australia, Perth, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Paula R Beckenkamp
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Nigel K Arden
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Paulo H Ferreira
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Hengxing Zhou
- 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.
- The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250033, People's Republic of China.
| | - Shiqing Feng
- 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.
- The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250033, People's Republic of China.
| | - Manuela L Ferreira
- Sydney Musculoskeletal Health, The Kolling Institute, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| |
Collapse
|