Validation of New GIMA Biomarker Signature of Endometriosis- Interim Data

Mark Noar, John Mathias, Ajit Kolatkar
2024 · doi:10.5281/zenodo.14274803 · W6949331722
Diagnostic accuracy OA: green CC0
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Interim analysis of a multicenter trial demonstrates that electroviscerography-derived GIMA biomarkers with AI scoring accurately diagnose endometriosis with 96% sensitivity and specificity, independent of hormonal therapy or pain scores.

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This interim analysis evaluated the diagnostic accuracy of Gastrointestinal Myoelectrical Activity (GIMA) detected via Electroviscerography for identifying endometriosis in a multicenter prospective blinded trial. The study examined 50 patients, comprising 25 with surgically confirmed endometriosis and 25 controls, using AI-derived threshold scores based on AUC frequency bands and pain metrics. Results indicated that GIMA biomarkers achieved 96% sensitivity and specificity, correctly predicting all subjects in this subset, with diagnostic performance unaffected by hormonal therapy, surgical stage, or age. This paper is centrally about endometriosis — specifically validating a non-invasive electroviscerography-based biomarker signature for its diagnosis.

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Abstract

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 diagnoseendometriosis.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 wereselected for interim analysis. Study population included women 27 years-55 years old, 25 with diagnosis of endometriosisand 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 UnderThe Curve (AUC), age and standardized pain scores variables.Main Outcome Measures: Specificity, sensitivity, NPV, PPV and predictive probability or C-statistic from logisticalregression 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 atfrequency 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-derived GIMA biomarkers threshold scoring predicted 25/25subjects positive and negative for endometriosis, with surgical confirmation. Hormonal therapy, surgical stage, age norpain score affected diagnostic accuracy.Conclusion: EVG GIMA biomarker data with Ai-derived threshold scoring accurately distinguished participants with andwithout endometriosis. This interim analysis supports continued investigation of GIMA biomarkers to diagnoseendometriosis.
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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-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. Files mark q2 accepted file.pdf Files (1.3 MB) | Name | Size | Download all | |---|---|---| | md5:85e8166aba52a97b6379cb3a61dd975d | 1.3 MB | Preview Download |

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