Introduction: Interstitial cystitis/bladder pain syndrome (IC/BPS) manifests as urinary symptoms including urgency, frequency, and pain. The IP4IC Study aimed to establish a urine-based biomarker score for diagnosing IC/BPS. To accomplish this objective, we investigated the parallels and variances between patients enrolled via physician/hospital clinics and those recruited through online crowdsourcing.
Methods: Through a nationwide crowdsource effort, we collected surveys from patients with history of IC/BPS. Study participants were asked to complete the validated instruments of Interstitial Cystitis Symptom Index (ICSI) and Interstitial Cystitis Problem Index (ICPI), as well as provide demographic information. We then compared the survey responses of patients recruited through crowdsourcing with those recruited from three specialized tertiary care urology clinics engaged in clinical research.
Results: Survey responses of 1300 participants were collected from all 50 states of the USA via crowdsourcing and 319 from a clinical setting. ICSI and ICPI were similar for IC/BPS patients diagnosed by the physicians in clinic and self-reported by subjects via crowdsourcing stating they have a history of previous physician diagnosis of IC/BPS. Surprisingly, ICSI and ICPI were significantly lower in crowdsourced control than in-clinic control subjects.
Conclusion: The IP4IC Study provides valuable insights into the similarities and differences between patients recruited through clinics and those recruited through online crowdsourcing. There were no significant differences in disease symptoms among these groups. Individuals who express an interest in digital health research and self-identify as having been previously diagnosed by physicians with IC/BPS can be regarded as reliable candidates for crowdsourcing research.
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http://dx.doi.org/10.1177/20552076231216280 | DOI Listing |
Diagnostics (Basel)
December 2024
Underactive Bladder Foundation, Pittsburgh, PA 15235, USA.
This study aimed to improve machine learning models for diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) by comparing classical machine learning methods with newer AutoML approaches, utilizing biomarker data and patient-reported outcomes as features. We applied various machine learning techniques to biomarker data from the previous IP4IC and ICRS studies to predict the presence of IC/BPS, a disorder impacting the urinary bladder. Data were sourced from two nationwide, crowd-sourced collections of urine samples involving 2009 participants.
View Article and Find Full Text PDFDigit Health
November 2023
Department of Urology, Oakland University William Beaumont School of Medicine, Rochester, MI, USA.
Introduction: Interstitial cystitis/bladder pain syndrome (IC/BPS) manifests as urinary symptoms including urgency, frequency, and pain. The IP4IC Study aimed to establish a urine-based biomarker score for diagnosing IC/BPS. To accomplish this objective, we investigated the parallels and variances between patients enrolled via physician/hospital clinics and those recruited through online crowdsourcing.
View Article and Find Full Text PDFJ Urol
May 2018
Oakland University William Beaumont School of Medicine, Rochester Hills, Michigan.
Purpose: Biomarker discovery is limited by readily assessable, cost efficient human samples available in large numbers that represent the entire heterogeneity of the disease. We developed a novel, active participation crowdsourcing method to determine BP-RS (Bladder Permeability Defect Risk Score). It is based on noninvasive urinary cytokines to discriminate patients with interstitial cystitis/bladder pain syndrome who had Hunner lesions from controls and patients with interstitial cystitis/bladder pain syndrome but without Hunner lesions.
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