Evidence map›Paper›PMID 33151406›Full record

Observational studyJournal of gastroenterology2021

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

Benjamin Rogers, Sabyasachi Samanta, Kevan Ghobadi, Amit Patel, Edoardo Savarino, Sabine Roman, Daniel Sifrim, C Prakash Gyawali

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in Journal of gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
3.8field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.

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  8. Mucosal impedance as a diagnostic tool for gastroesophageal reflux disease: an update for clinicians.Diseases of the esophagus : official journal of the International Society for Diseases of the Esophagus · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 4 countries.

Benjamin RogersDivision of Gastroenterology, Washington University School of Medicine, 660 South Euclid Ave., Campus Box 8124, Saint Louis, MO, 63110, USA.
Sabyasachi SamantaCrosswave Solutions, LLC., Lexington, KY, USA.
Kevan GhobadiCrosswave Solutions, LLC., Lexington, KY, USA.
Amit PatelDivision of Gastroenterology, Duke University School of Medicine, The Durham Veterans Affairs Medical Center, Durham, NC, USA.
Edoardo SavarinoDivision of Gastroenterology, Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua, Italy.
Sabine RomanDigestive Physiology, Hospices Civils de Lyon, Hopital E Herriot, Université de Lyon, 69437, Lyon, France.
Daniel SifrimBarts and The London School of Medicine and Dentistry Queen Mary, University of London, London, UK.
C Prakash GyawaliDivision of Gastroenterology, Washington University School of Medicine, 660 South Euclid Ave., Campus Box 8124, Saint Louis, MO, 63110, USA. cprakash@wustl.edu.ORCID http://orcid.org/0000-0002-3388-0660
Washington University in St. Louis · USDuke University · USQueen Mary University of London · GBUniversité Claude Bernard Lyon 1 · FRUniversity of Padua · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial 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.

methodspH-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.

resultsRecumbent, 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).

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

Indexed as

Artificial IntelligenceElectric ImpedanceAdultAgedCase-Control StudiesEsophageal pH MonitoringFeasibility StudiesFemaleGastroesophageal RefluxHumansLinear ModelsMaleMiddle AgedProof of Concept StudyProspective StudiesProton Pump InhibitorsProton Pump InhibitorsArtificial intelligenceGastroesophageal reflux diseaseMean nocturnal baseline impedancepH-impedance monitoring

Identifiers

PMID33151406
OpenAlexW3095363661

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.