Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome Using Machine Learning Based Predictions

Objective: To improve diagnosis of interstitial cystitis (IC)/bladder pain syndrome(IC) we hereby developed an improved IC risk classification using machine learning algorithms. Methods: A national crowdsourcing resulted in 1264 urine samples consisting of 536 IC (513 female, 21 male, 2 unspecified), and 728 age-matched controls (318 female, 402 male, 8 unspecified) with corresponding patient-reported outcome (PRO) pain and symptom scores. In addition, 296 urine samples were collected at three academic centers: 78 IC (71 female, 7 male) and 218 controls (148 female, 68 male, 2 unspecified). Urinary cytokine biomarker levels were determined using Luminex assay. A machine learning predictive classification model, termed the Interstitial Cystitis Personalized Inflammation Symptom (IC-PIS) Score, that utilizes PRO and cytokine levels, was generated and compared to a challenger model. Results: The top-performing model using biomarker measurements and PROs (area under the curve [AUC] = 0.87) was a support vector classifier, which scored better at predicting IC than PROs alone (AUC = 0.83). While biomarkers alone (AUC = 0.58) did not exhibit strong predictive performance, their combination with PROs produced an improved predictive effect. Conclusion: IC-PIS represents a novel classification model designed to enhance the diagnostic accuracy of IC/bladder pain syndrome by integrating PROs and urine biomarkers. The innovative approach to sample collection logistics, coupled with one of the largest crowdsourced biomarker development studies utilizing ambient shipping methods across the US, underscores the robustness and scalability of our findings.