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

doi:10.1186/s12916-024-03388-x

Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models

BMC Med. 2024 Apr 19;22(1):167. doi: 10.1186/s12916-024-03388-x.

Authors

Lingxiao Chen^#^{1

2

3}, Claire E Ashton-James^#⁴, Baoyi Shi⁵, Maja R Radojčić⁶, David B Anderson⁷, Yujie Chen^{8

9}, David B Preen¹⁰, John L Hopper¹¹, Shuai Li¹¹, Minh Bui¹¹, Paula R Beckenkamp⁷, Nigel K Arden¹², Paulo H Ferreira⁷, Hengxing Zhou^{13

14}, Shiqing Feng^{15

16}, Manuela L Ferreira^{3

7}

Affiliations

¹ 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.
² Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, People's Republic of China.
³ Sydney Musculoskeletal Health, The Kolling Institute, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
⁴ Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia.
⁵ Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, USA.
⁶ Division of Psychology and Mental Health, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
⁷ School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, Australia.
⁸ Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.
⁹ Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
¹⁰ School of Population and Global Health, The University of Western Australia, Perth, Australia.
¹¹ Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.
¹² Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
¹³ 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. zhouhengxing@sdu.edu.cn.
¹⁴ The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250033, People's Republic of China. zhouhengxing@sdu.edu.cn.
¹⁵ 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. shiqingfeng@sdu.edu.cn.
¹⁶ The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250033, People's Republic of China. shiqingfeng@sdu.edu.cn.

^# Contributed equally.

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.

Keywords: Big data; Chronic pain; Clinical prediction model; Depression; Prevalence; Variability.

MeSH terms

Adult
Asthma*
Biological Specimen Banks
Chronic Pain* / epidemiology
Depression / epidemiology
Humans
Middle Aged
Models, Statistical
Prevalence
Prognosis
UK Biobank

Abstract

MeSH terms

Grants and funding