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Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease

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Abstract

Background

Artificial intelligence (AI) has potential to streamline interpretation of pH-impedance studies. In this exploratory observational cohort study, we determined feasibility of automated AI extraction of baseline impedance (AIBI) and evaluated clinical value of novel AI metrics.

Methods

pH-impedance data from a convenience sample of symptomatic patients studied off (n = 117, 53.1 ± 1.2 years, 66% F) and on (n = 93, 53.8 ± 1.3 years, 74% F) anti-secretory therapy and from asymptomatic volunteers (n = 115, 29.3 ± 0.8 years, 47% F) were uploaded into dedicated prototypical AI software designed to automatically extract AIBI. Acid exposure time (AET) and manually extracted mean nocturnal baseline impedance (MNBI) were compared to corresponding total, upright, and recumbent AIBI and upright:recumbent AIBI ratio. AI metrics were compared to AET and MNBI in predicting  ≥ 50% symptom improvement in GERD patients.

Results

Recumbent, but not upright AIBI, correlated with MNBI. Upright:recumbent AIBI ratio was higher when AET  > 6% (median 1.18, IQR 1.0–1.5), compared to  < 4% (0.95, IQR 0.84–1.1), 4–6% (0.89, IQR 0.72–0.98), and controls (0.93, IQR 0.80–1.09, p ≤ 0.04). While MNBI, total AIBI, and the AIBI ratio off PPI were significantly different between those with and without symptom improvement (p < 0.05 for each comparison), only AIBI ratio segregated management responders from other cohorts. On ROC analysis, off therapy AIBI ratio outperformed AET in predicting GERD symptom improvement when AET was  > 6% (AUC 0.766 vs. 0.606) and 4–6% (AUC 0.563 vs. 0.516) and outperformed MNBI overall (AUC 0.661 vs. 0.313).

Conclusions

BI calculation can be automated using AI. Novel AI metrics show potential in predicting GERD treatment outcome.

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Authors and Affiliations

  1. Division of Gastroenterology, Washington University School of Medicine, 660 South Euclid Ave., Campus Box 8124, Saint Louis, MO, 63110, USA

    Benjamin Rogers & C. Prakash Gyawali

  2. Crosswave Solutions, LLC., Lexington, KY, USA

    Sabyasachi Samanta & Kevan Ghobadi

  3. Division of Gastroenterology, Duke University School of Medicine, The Durham Veterans Affairs Medical Center, Durham, NC, USA

    Amit Patel

  4. Division of Gastroenterology, Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy

    Edoardo Savarino

  5. Digestive Physiology, Hospices Civils de Lyon, Hopital E Herriot, Université de Lyon, 69437, Lyon, France

    Sabine Roman

  6. Digestive Physiology, Université de Lyon, Lyon I University, 69008, Lyon, France

    Sabine Roman

  7. Université de Lyon, Inserm U1032, LabTAU, 69008, Lyon, France

    Sabine Roman

  8. Barts and The London School of Medicine and Dentistry Queen Mary, University of London, London, UK

    Daniel Sifrim

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  1. Benjamin Rogers
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  2. Sabyasachi Samanta
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  4. Amit Patel
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  5. Edoardo Savarino
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  6. Sabine Roman
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  7. Daniel Sifrim
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  8. C. Prakash Gyawali
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Contributions

BDR: study concept, data analysis, drafting of manuscript, and critical review of manuscript; SS: creation of AI algorithm, data analysis, and critical review of manuscript; KG: creation of AI algorithm, data analysis, and critical review of manuscript; AP: data collection and final review of manuscript; ES: data collection, key intellectual content, critical review of manuscript; SR: key intellectual content and critical review of manuscript; DS: key intellectual content and critical review of manuscript; CPG: study concept, data analysis, key intellectual content, drafting, and finalization of manuscript.

Corresponding author

Correspondence to C. Prakash Gyawali.

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Conflict of interest

No conflicts of interest exist. Partial funding through the Thomas and Mary Eckert Fellowship Clinical Research Fund, through the Division of Gastroenterology, Washington University School of Medicine, St. Louis, awarded to BDR. BDR: no disclosures; SS: no disclosures; KG: co-founder of ClearifiRx; ES: Lecture Fee: Medtronic, Takeda, Janssen, MSD, Abbvie, Malesci; Consulting: Medtronic, Takeda, Janssen, MSD, Reckitt Bencikser, Sofar, Unifarco, SILA, Oftagest ; SR: consulting Medtronic, research support Diversatek Healthcare, Medtronic; DS: research grants: Reckitt Benckiser UK, Jinshan Technology China, Alfa Sigma Italy; CPG: Consulting: Medtronic, Diversatek, Isothrive, Ironwood, Quintiles.

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Rogers, B., Samanta, S., Ghobadi, K. et al. Artificial intelligence automates and augments baseline impedance measurements from pH-impedance studies in gastroesophageal reflux disease. J Gastroenterol 56, 34–41 (2021). https://doi.org/10.1007/s00535-020-01743-2

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  • DOI: https://doi.org/10.1007/s00535-020-01743-2

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