Robust Regression and Optimal Transport Methods to Predict Gastrointestinal Disease Etiology From High Resolution EGG and Symptom Severity

• Published in: IEEE transactions on biomedical engineering Vol. 69; no. 11; pp. 3313 - 3325
• Main Authors: Agrusa, Anjulie S., Kunkel, David C., Coleman, Todd P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Autonomic nervous system; Clustering; Correlation; Diseases; Dyspepsia; Dyspepsia - diagnosis; Electrodes; electrogastrogram; Etiology; Gastric Emptying - physiology; gastroenterology; Gastrointestinal diseases; Gastrointestinal Motility - physiology; Gastroparesis; High resolution; Humans; Identification methods; machine learning; Myoelectricity; Neuromodulation; optimal transport theory; Pacemakers; Patients; Probability distribution; Quality of Life; Questionnaires; Recording; Regression; robust regression; Robustness (mathematics); Signs and symptoms; Stomach; Wave propagation
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2022.3167338

Objective: Gastric functional and motility disorders are highly prevalent, with gastroparesis (GP) and functional dyspepsia (FD), affecting 1.5-3% and 10% of the population, respectively. Multiple disease etiologies with overlapping symptoms, such as antral hypomotility, pylorospasm, autonomic dysfunction, and gastric myoelectric dysfunction underlie GP and FD. There is an unmet need to differentiate these etiologies non-invasively to tailor treatment strategies and predict treatment response. Methods: We performed cutaneous high-resolution electrogastrogram (HR-EGG) recordings on 32 human subjects (controls, GP, and FD) and computed gastric slow wave propagation patterns. We implemented robust regression and clustering methods to identify one group of patients with symptoms well explained by spatial slow wave features and another with symptom severity significantly exceeding predictions from spatial slow wave features. Five patients were re-assessed with validated symptom questionnaires after pyloric and prokinetic interventions. Results: A group of seven patients was identified whose spatial slow wave features lie within the same range as control subjects but whose symptom severity significantly exceeded what is predicted from spatial slow wave features. We hypothesize that gastric myoelectric dysfunction is not a prominent disease etiology in this group. A highly accurate regression holds in the other group of patients (r=0.8). Of the patients with repeat questionnaires, patients with symptom severity exceeding the regression line reported symptom improvement, whereas patients with symptoms in close proximity to the regression line experienced no improvement. Conclusion: These findings suggest that patients with symptom severity significantly exceeding the robust regression line have symptoms that cannot be explained by gastric myoelectric dysfunction alone, and vice versa. Significance: This methodology may provide clinicians with an opportunity to screen patients to determine when existing interventions will be effective, and on the flipside, when slow wave restoration interventions, such as gastric neuromodulation, may be most effective in improving symptoms and quality of life.