Women with chronic pelvic pain can be stratified using multimodal assessment

Abstract

Chronic pelvic pain (CPP) is a common and burdensome symptom in women yet current clinical management frequently leaves many with persistent pain. The Translational Research in Pelvic Pain (TRiPP) project adopts a pain-focused strategy, aiming to better phenotype CPP through multimodal assessment. Here, we integrated questionnaire, physiological, and biological data to (1) determine whether perturbations in the function of pain-relevant systems in women with CPP can be demonstrated and (2) explore whether these data can stratify women with CPP into meaningful subgroups, independent of diagnostic group. Participants included 108 women, aged 18 to 50 years, with CPP including endometriosis-associated pain (EAP), bladder pain syndrome (BPS), comorbid EAP and BPS, and pelvic pain with no underlying pathology alongside 50 pain-free controls. Analyses were conducted in 3 stages: (1) group comparisons, (2) latent profile analysis to identify CPP subgroups, and (3) clinical characterization of resulting clusters. Compared with controls, CPP participants reported significantly greater fatigue, poorer sleep, higher anxiety, depression, pain catastrophising, and more childhood trauma (P < 0.01). However, no significant differences were observed in physiological measures. Latent profile analysis revealed 3 distinct CPP subgroups, differentiated by questionnaires rather than physiological measures. Cluster 1 represents a group with predominantly higher-impact pain compared with others, that may be associated with nociplastic mechanisms. These findings support alternative approaches to stratification of CPP separate from standard diagnostic groupings. The role of questionnaire measures in this stratification facilitates translation to clinical settings; however, further work is required to determine whether differing therapeutic approaches are appropriate for each cluster.

Keywords: Bladder pain; Chronic pelvic pain; Endometriosis; Latent profile analysis; Multimodal assessment.