Widespread pain phenotypes impact treatment efficacy results in randomized clinical trials for interstitial cystitis/bladder pain syndrome: a Multidisciplinary Approach to the Study of Chronic Pelvic Pain network study

Abstract

Pain clinical trials are notoriously complex and often inefficient in demonstrating efficacy, even for known efficacious treatments. A major issue is the difficulty in the a priori identification of specific phenotypes to include in the study population. Recent work has identified the extent of widespread pain as an important determinant of the likelihood of response to therapy, but it has not been tested in clinical trials for the treatment of interstitial cystitis/bladder pain syndrome (IC/BPS). We explored this hypothesis using data from 3 previously published trials testing treatments for IC/BPS, which suggested modest benefits but did not meet a priori primary outcome statistical significance criteria. Importantly, these studies also collected symptom questionnaire data that allowed us to retrospectively identify participants with and without widespread pain. Analyzing the treatment by the degree of widespread pain revealed a difference in outcome and statistical significance level for each trial. Participants with predominately local pain (ie, limited widespread pain symptoms) responded to therapy targeting local symptoms, whereas those with widespread pain did not. Alternatively, participants with widespread pain beyond their local pelvic pain responded to more centrally acting treatments. Our results suggest that differentiating patients based on widespread vs more localized pain is a key consideration for designing future clinical trials for conditions with variable pain profiles, such as IC/BPS and potentially other pain-based syndromic disorders.

Keywords: Interstitial cystitis; Pain measurement; Pelvic pain; Reanalysis of clinical trial results; Widespread pain.

Figure 1.

Predicted change and observed percentage improvement in pelvic pain from baseline by RCT and level of widespread pain. The mean trajectory plots (left within the cell) represent the estimated change in pelvic pain from the average baseline for the average participant within RCT, accounting for baseline outcome, age, race, and sex. The treatment effect, corresponding P-value, and 95% confidence interval are displayed within the plot. Mean trajectories were derived from the primary analysis models for change in outcome at the end of the study from the average baseline using multiple imputations for missing data. The 1 − ECDF plots (right within the cell) represent the proportion of participants whose observed percentage improvement in end-of-study outcome from the average baseline is beyond the value on the x-axis. Participants missing end-of-study data are assigned a percentage change of 0. The P-value for testing the difference in observed percentage improvement curves for treatment (blue) and control (red) are displayed within the plot and are derived from the nonparametric Wilcoxon rank sum test (see Supplemental Methods, http://links.lww.com/PAIN/C165). ECDF, empirical cumulative distribution function; RCT, randomized controlled trial.

Figure 2.

Predicted change and observed percentage improvement in urinary urgency from baseline by RCT and widespread pain. The mean trajectory plots (left within the cell) represent the estimated change in urinary urgency from the average baseline for the average participant within RCT, accounting for baseline outcome, age, race, and sex. The treatment effect, corresponding P-value, and 95% confidence interval are displayed within the plot. Mean trajectories were derived from the primary analysis models for change in outcome at the end of the study from the average baseline using multiple imputations for missing data. The 1 − ECDF plots (right within the cell) represent the proportion of participants whose observed percentage improvement in end-of-study outcome from the average baseline is beyond the value on the x-axis. Participants missing end-of-study data are assigned a percentage change of 0. The P-value for testing the difference in observed percentage improvement curves for treatment (blue) and control (red) are displayed within the plot and are derived from the nonparametric Wilcoxon rank sum test (see Supplemental Methods, http://links.lww.com/PAIN/C165). ECDF, empirical cumulative distribution function; RCT, randomized controlled trial.

Figure 3.

Predicted change and observed percentage improvement in urinary frequency from baseline for amitriptyline trial by widespread pain. The mean trajectory plots (left within the cell) represent the estimated change in urinary frequency from the average baseline for the average participant within the amitriptyline train to account for baseline outcome, age, race, and sex. The treatment effect, corresponding P-value, and 95% confidence interval are displayed within the plot. Mean trajectories were derived from the primary analysis models for change in outcome at the end of the study from the average baseline using multiple imputations for missing data. The 1 − ECDF plots (right within the cell) represent the proportion of participants whose observed percentage improvement in end-of-study outcome from the average baseline is beyond the value on the x-axis. Participants missing end-of-study are assigned a percentage change of 0. The P-value for testing the difference in observed percentage improvement curves for treatment (blue) and control (red) are displayed within the plot and are derived from the nonparametric Wilcoxon rank sum test (see Supplemental Methods, http://links.lww.com/PAIN/C165). ECDF, empirical cumulative distribution function.