Gynecology & Obstetrics
Open Access

Abstract

Validation of New GIMA Biomarker Signature of Endometriosis - Interim Data

Mark Noar, John Mathias and Ajit Kolatkar

Objective: Validate diagnostic accuracy of new unique biomarker, Gastrointestinal Myoelectrical Activity (GIMA), detected by Electroviscerography (EVG) with Ai-derived disease threshold score calculation to non-invasively diagnose endometriosis.

Design: Multicenter prospective blinded trial.

Setting: Women’s healthcare center.

Population of Sample: 165 patients with and without endometriosis diagnosis.

Methods: Initial 50 patients meeting inclusion criteria in 165-patients multicenter prospective GIMA biomarker trial were selected for interim analysis. Study population included women 27 years-55 years old, 25 with diagnosis of endometriosis and 25 non-endometriosis controls. Clinical and GIMA data were collected between February 2007 and September 2017, at all harvesting time points and frequency bands using EVG. Ai-derived threshold score calculations used Area Under The Curve (AUC), age and standardized pain scores variables.

Main Outcome Measures: Specificity, sensitivity, NPV, PPV and predictive probability or C-statistic from logistical regression analyses of all AUC frequency and time points.

Results: Non-endometriosis versus endometriosis cohort interim analysis differed significantly (p<0.001) for median (IQR), AUC values, and percent frequency power distribution at baseline, (10, 20, and 30) minute post water-load at frequency ranges (15-20, 30-40, 40-50 and 50-60) cpm. GIMA threshold scoring revealed sensitivity and PPV of 96%, specificity and NPV of 96% and C-statistic of 100%. Ai- PDF No. 626 derived GIMA biomarkers threshold scoring predicted 25/25 subjects positive and negative for endometriosis, with surgical confirmation. Hormonal therapy, surgical stage, age nor pain score affected diagnostic accuracy.

Conclusion: EVG GIMA biomarker data with Ai-derived threshold scoring accurately distinguished participants with and without endometriosis. This interim analysis supports continued investigation of GIMA biomarkers to diagnose endometriosis.

Published Date: 2024-09-30; Received Date: 2024-09-09