Login to Access Video or Poster Abstract: MP29-07
Sources of Funding: We would like to thank the Taubman family for their generous support of interstitial cystitis research including this project.

Introduction

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a multifactorial syndrome of severe pelvic and genitalia pain and compromised urinary function. A fraction of IC patients who are the most severe present with Hunner's ulcers or patches on their bladder walls, termed ulcerative IC (UIC). UIC is diagnosed by cystoscopy, however this is a painful and expensive procedure. The objective of this study was to determine if a calculated Interstitial Cystitis Ulcerative Risk Score (ICUS) based on non-invasive urinary cytokines could discriminate UIC patients from controls and non-ulcerative IC patients.

Methods

A national crowdsourcing effort targeting IC/BPS patients resulted in 442 urine samples consisting of 153 IC patients (146 female, 7 male), 155 female controls, and 134 male controls were collected. Controls were age-matched. This consisted of 52 UIC patients (48 females, 4 male). Urinary cytokine levels were determined using Luminex assay. A predictive classification model was generated from this data using the scikit-learn machine learning library and the Python programming language. It provides a probability of ulcerative IC when the algorithm is supplied with the levels of three different proteins found in the urine.

Results

A defined ICUS Score of 0 to 1 was calculated to predict UIC, or a bladder permeability defect etiology (Figure 1). If the ICUS is ≥ 0.5, then there is an 87% chance that the patient has ulcerative IC. If the ICUS is <0.5, there is an 87% chance that the patient does not have ulcerative IC. The three protein levels combined provide a much better prediction model versus any of the individual protein levels alone.

Conclusions

The ICUS Score quantifies UIC risk, indicative of a bladder permeability defect etiology, in a subset of IC patients. This provides a new clinical tool to improve diagnosing patients with suspected IC symptoms.

Funding

We would like to thank the Taubman family for their generous support of interstitial cystitis research including this project.