| Title: | Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models |
| Journal: | BMC Medicine |
| Published: | 19 Apr 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38637815/ |
| DOI: | https://doi.org/10.1186/s12916-024-03388-x |
| URL: | https://bmcmedicine.biomedcentral.com/counter/pdf/10.1186/s12916-024-03388-x |
Publication 9861
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
BackgroundThe 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.MethodsParticipants 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).ResultsAnalyses 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.ConclusionsThere 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.</p>
16 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 80584 | Deciphering the role of emotional distress and functional disability among patients with chronic musculoskeletal pain |
Enabling scientific discoveries that improve human health