Validation of New GIMA Biomarker Signature of Endometriosis- Interim Data

Mark Noar, John Mathias, Ajit Kolatkar
2024 · doi:10.5281/zenodo.14274802 · W6968292195
Diagnostic accuracy OA: green CC0
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Interim analysis of 50 women demonstrated that electroviscerography-derived gastrointestinal myoelectrical activity biomarkers with AI threshold scoring accurately distinguished endometriosis from controls with high sensitivity and specificity.

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This interim analysis evaluated the diagnostic accuracy of Gastrointestinal Myoelectrical Activity (GIMA) detected via Electroviscerography to distinguish patients with endometriosis from healthy controls. The study included 50 women aged 27 to 55, comprising 25 individuals with surgically confirmed endometriosis and 25 without the condition, utilizing AI-derived threshold scoring based on area under the curve values and pain scores. Results demonstrated a sensitivity, specificity, positive predictive value, and negative predictive value of 96%, along with a C-statistic of 100%, indicating that the biomarker effectively differentiated the cohorts regardless of hormonal therapy or surgical stage. This paper is centrally about endometriosis — specifically the validation of a non-invasive electroviscerographic biomarker 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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