Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models
Lingxiao Chen,
Claire E Ashton-James,
Baoyi Shi,
Maja R Radojčić,
David B Anderson,
Yujie Chen,
David B Preen,
John L Hopper,
Shuai Li,
Minh Bui,
Paula R Beckenkamp,
Nigel K Arden,
Paulo H Ferreira,
Hengxing Zhou,
Shiqing Feng,
Manuela L Ferreira
Affiliations
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
Claire E Ashton-James
Sydney Medical School, Faculty of Medicine and Health, University of Sydney
Baoyi Shi
Department of Biostatistics, Mailman School of Public Health, Columbia University
Maja R Radojčić
Division of Psychology and Mental Health, Faculty of Biology, Medicine and Health, University of Manchester
David B Anderson
School of Health Sciences, Faculty of Medicine and Health, University of Sydney
Yujie Chen
Program in Child Health Evaluative Sciences, The Hospital for Sick Children
David B Preen
School of Population and Global Health, The University of Western Australia
John L Hopper
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne
Shuai Li
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne
Minh Bui
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne
Paula R Beckenkamp
School of Health Sciences, Faculty of Medicine and Health, University of Sydney
Nigel K Arden
Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford
Paulo H Ferreira
School of Health Sciences, Faculty of Medicine and Health, University of Sydney
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
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
Manuela L Ferreira
Sydney Musculoskeletal Health, The Kolling Institute, Faculty of Medicine and Health, University of Sydney
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.
