Normal values and regional differences in oesophageal impedance-pH metrics: a consensus analysis of impedance-pH studies from around the world

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

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.

Esophageal motility disorders on high-resolution manometry: Chicago classification version 4.0©

Chicago Classification v4.0 (CCv4.0) is the updated classification scheme for esophageal motility disorders using metrics from high-resolution manometry (HRM). Fifty-two diverse international experts separated into seven working subgroups utilized formal validated methodologies over two-years to develop CCv4.0. Key updates in CCv.4.0 consist of a more rigorous and expansive HRM protocol that incorporates supine and upright test positions as well as provocative testing, a refined definition of esophagogastric junction (EGJ) outflow obstruction (EGJOO), more stringent diagnostic criteria for ineffective esophageal motility and description of baseline EGJ metrics. Further, the CCv4.0 sought to define motility disorder diagnoses as conclusive and inconclusive based on associated symptoms, and findings on provocative testing as well as supportive testing with barium esophagram with tablet and/or functional lumen imaging probe. These changes attempt to minimize ambiguity in prior iterations of Chicago Classification and provide more standardized and rigorous criteria for patterns of disorders of peristalsis and obstruction at the EGJ.