Area under the curve higher for biomarker measurements and patient-reported outcomes versus outcomes or biomarkers alone<\/p>\n<\/h3>\n
By Elana Gotkine HealthDay Reporter<\/b><\/p>\n
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TUESDAY, May 7, 2024 (HealthDay News) — The integration of biomarkers and patient-reported outcomes (PROs) improves prediction of interstitial cystitis (IC)\/bladder pain syndrome, according to a study published online April 24 in Urology<\/em>.<\/p>\n Laura E. Lamb, Ph.D., from the Oakland University William Beaumont School of Medicine in Rochester, Michigan, and colleagues generated a machine learning predictive classification model (the Interstitial Cystitis Personalized Inflammation Symptom score), which uses PRO and cytokine levels, and then compared it to a challenger model. The machine learning model was based on 1,264 urine samples (536 IC and 728 age-matched controls) with corresponding PRO pain and symptom scores and 296 urine samples (78 IC and 218 controls) from three academic centers.<\/p>\n The researchers found that the top-performing model using biomarker measurements and PROs was a support vector classifier and had an area under the curve (AUC) of 0.87; the model offered better IC prediction than PROs alone (AUC, 0.83). Biomarkers alone did not exhibit strong predictive performance (AUC, 0.58).<\/p>\n “Our study demonstrates its effectiveness in accurately distinguishing individuals with nonulcerative or ulcerative IC from both normal asymptomatic controls and other bladder-centric confusable disorders, surpassing the capabilities of traditional PROs alone,” the authors write.<\/p>\n One author disclosed ties to Stata Oncology; several authors have intellectual property associated with methods for diagnosing IC.<\/p>\n