Publication Date
3-1-2024
Journal
International Urogynecology Journal
DOI
10.1007/s00192-024-05735-1
PMID
38300276
PMCID
PMC11023803
PubMedCentral® Posted Date
2-1-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Introduction and hypothesis: As interstitial cystitis/bladder pain syndrome (IC/BPS) likely represents multiple pathophysiologies, we sought to validate three clinical phenotypes of IC/BPS patients in a large, multi-center cohort using unsupervised machine learning (ML) analysis.
Methods: Using the female Genitourinary Pain Index and O'Leary-Sant Indices, k-means unsupervised clustering was utilized to define symptomatic phenotypes in 130 premenopausal IC/BPS participants recruited through the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) research network. Patient-reported symptoms were directly compared between MAPP ML-derived phenotypic clusters to previously defined phenotypes from a single center (SC) cohort.
Results: Unsupervised ML categorized IC/BPS participants into three phenotypes with distinct pain and urinary symptom patterns: myofascial pain, non-urologic pelvic pain, and bladder-specific pain. Defining characteristics included presence of myofascial pain or trigger points on examination for myofascial pain patients (p = 0.003) and bladder pain/burning for bladder-specific pain patients (p < 0.001). The three phenotypes were derived using only 11 features (fGUPI subscales and ICSI/ICPI items), in contrast to 49 items required previously. Despite substantial reduction in classification features, unsupervised ML independently generated similar symptomatic clusters in the MAPP cohort with equivalent symptomatic patterns and physical examination findings as the SC cohort.
Conclusions: The reproducible identification of IC/BPS phenotypes, distinguishing bladder-specific pain from myofascial and genital pain, using independent ML analysis of a multicenter database suggests these phenotypes reflect true pathophysiologic differences in IC/BPS patients.
Keywords
Female, Humans, Chronic Pain, Cystitis, Interstitial, Myofascial Pain Syndromes, Pelvic Pain, Phenotype, Urinary Bladder, Multicenter Studies as Topic, Symptom phenotypes, Bladder pain syndrome, Chronic pelvic pain syndrome, Interstitial cystitis, Machine learning analysis, MAPP research network cohort
Published Open-Access
yes
Recommended Citation
Nettey, Oluwarotimi Sewedo; Gu, Cindy; Jackson, Nicholas James; et al., "Validation of Distinct Bladder Pain Phenotypes Utilizing the MAPP Research Network Cohort" (2024). Faculty, Staff and Students Publications. 6192.
https://digitalcommons.library.tmc.edu/baylor_docs/6192