Pain and Urinary Symptoms Should Not be Combined into a Single Score: Psychometric Findings from the MAPP Research Network

Abstract

Purpose The purpose of this study was to create symptom indices – that is, scores derived from questionnaires – to accurately and efficiently measure symptoms of interstitial cystitis/bladder pain syndrome and chronic prostatitis/chronic pelvic pain syndrome, collectively referred to as urologic chronic pelvic pain syndromes (UCPPS). We created these indices empirically, by investigating the structure of symptoms using exploratory factor analysis. Materials and Methods As part of the Multi-Disciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network, participants (N = 424) completed questionnaires including the Genitourinary Pain Index (GUPI), the Interstitial Cystitis Symptom Index (ICSI), and the Interstitial Cystitis Problem Index (ICPI). Individual items from questionnaires about bladder and pain symptoms were evaluated by principal components and exploratory factor analysis to identify indices with fewer questions to comprehensively quantify symptom severity. Additional analyses included correlating symptom indices with symptoms of depression, a known comorbidity of patients with pelvic pain. Results and Conclusions Exploratory factor analyses suggested that two factors, pain severity and urinary severity, provided the best psychometric description of items contained in the GUPI, the ICSI, and the ICPI. These factors were used to create two symptom indices for pain and urinary symptoms. Pain, but not urinary symptoms, was associated with symptoms of depression in a multiple regression analysis, suggesting that these symptoms may impact patients with UCCPS differently; for pain severity, B (SE) = 0.24 (0.04), 95% CI of B = 0.16–0.32, β = 0.32, p < .001. Our results suggest that pain and urinary symptoms should be assessed separately, rather than combined into one total score. Total scores that combine the separate factors of pain and urinary symptoms into one score may be limited for clinical and research purposes.

Correlates of Health Care Seeking Activities in Patients with Urological Chronic Pelvic Pain Syndromes: Findings from the MAPP Cohort

“…Perhaps patients self-treat for urinary symptoms, or maybe they are provided with therapies to use as needed (e.g., antibiotics, urinary analgesics) that do not prompt a new provider interaction. These findings reinforce our previous observations that pain symptoms and urinary symptoms in UCPPS are separate, unique constructs of these syndromes 11,15 .…”

“…Separate scores for UCPPS pain severity and urinary severity were obtained from biweekly responses to the Genitourinary Pain Index 9 and the Interstitial Cystitis Symptom index 10 as reported previously 11 . At the time of each biweekly assessment, the within-person pain and urologic symptom variability was determined based on the standard deviation of pain scores and urinary scores, respectively, for the preceding 6-week time period 8 .…”

Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: a MAPP network study

“…We first calculated summary scores of pain symptoms and urinary symptoms, the primary dimensions of UCPPS [13], at each time-point of the bi-weekly internet-based symptom assessments. UCPPS pain and urinary symptom scores were developed and published by the MAPP Research Network [13].…”

Clinical and Psychosocial Predictors of Urological Chronic Pelvic Pain Symptom Change in 1 Year: A Prospective Study from the MAPP Research Network

“…UCPPS severity was characterized on two distinct dimensions, pain and urinary symptoms, based on a recent psychometric analysis of the MAPP baseline data. 13 A functional clustering procedure was applied to the biweekly pain and urinary severity scores to classify the overall symptom trajectory for each participant as worsening, stable or improving over the study period (for details see Supplemental Appendix C). To decrease the influence of study entry effects on outcome classification the first month of symptom ratings were not used in the clustering algorithm 14 and the symptom ratings at study week 4 were used as the baseline symptom level for the modeling.…”