Objectives: To examine the cross-sectional relationship of age, urinary and depressive symptoms and partner status on pain intensity and quality of life (QoL) in chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS).
Patients and methods: In all, 463 men enrolled in the National Institutes of Health (NIH) Chronic Prostatitis Cohort Study from seven clinical centres (six in the USA and one in Canada) reported baseline screening symptoms using the NIH Chronic Prostatitis Symptom Index (CPSI). The CPSI provides scores for pain, urinary symptoms and QoL. In addition, a demographic profile, including age and partner (living with another) status, and a depressive symptom score were obtained. Regression modelling of QoL, adjusting for between-centre variability, examined the unique effects of age, partner status, urological symptoms, depressive symptoms and pain.
Results: Urinary scores, depressive symptoms and pain intensity scores significantly predicted QoL for patients with CP/CPPS (higher CPSI QoL scores indicated more impairment; median 8.0, range 0-12). On average, for every 1-point increase in urinary scores, there was a corresponding increase in QoL score of 0.118 points (P = 0.001); for every 1-point increase in pain intensity score, there was a corresponding increase in QoL score of 0.722 points (P < 0.001); and for every 1-point decrease in depressive symptoms (lower scores equal poorer mood), the QoL score increased (poorer quality of life) by 0.381 points (P < 0.001). Age and partner status did not significantly contribute to poorer QoL. Urinary scores and depressive symptoms were significant predictors (P < 0.001) of pain in patients with CP/CPPS.
Conclusions: These data show that depressive symptoms and pain intensity significantly predict a poorer QoL in patients with CP/CPPS, and that these effects are independent of partner status, age and urinary status. In particular, pain intensity was the most robust predictor of a poorer QoL. Further data relating pain and psychological factors to CP/CPPS are highly recommended, to aid in determining specific factors for pain and its impact on QoL. These data are essential if empirically guided efforts to manage pain are to progress.