A physiological model for the investigation of esophageal motility in healthy and pathologic conditions. (September 2016)

• Record Type: Journal Article
• Authors: Carniel, Emanuele Luigi; Frigo, Alessandro; Costantini, Mario; Giuliani, Tommaso; Nicoletti, Loredana; Merigliano, Stefano; Natali, Arturo N
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 230:Number 9(2016:Sep.)
• Page Start: 892
• Page End: 899
• Publication Date: 2016-09
• Subjects: Esophageal motility -- high-resolution manometry -- model -- diagnosis -- autonomic procedure; Biomedical engineering -- Periodicals; Medical instruments and apparatus -- Periodicals; 610.28
• DOI: 10.1177/0954411916658915
• Languages: English
• ISSNs: 0954-4119

Abstract

Recent technological advances in esophageal manometry allowed the definition of new classification methods for the diagnosis of disorders of esophageal motility and the implementation of innovative computational tools for the autonomic, reliable and unbiased detection of different disorders. Computational models can be developed aiming to interpret the mechanical behavior and functionality of the gastrointestinal tract and to summarize the results from clinical measurements, as high-resolution manometry pressure plots, into model parameters. A physiological model was here developed to interpret data from esophageal high-resolution manometry. Such model accounts for parameters related to specific physiological properties of the biological structures involved in the peristaltic mechanism. The identification of model parameters was performed by minimizing the discrepancy between clinical data from high-resolution manometry and model results. Clinical data were collected from both healthy volunteers ( n = 35) and patients with different motor disorders, such as achalasia patterns 1 ( n = 13), 2 ( n = 20) and 3 ( n = 5), distal esophageal spasm ( n = 69), esophago-gastric junction outflow obstruction ( n = 25), nutcracker esophagus ( n = 11) and normal motility ( n = 42). The physiological model that was formulated in this work can properly explain high-resolution manometry data, as confirmed by the evaluation of the coefficient of determination R 2 = 0.83 − 0.96. The study finally led to identify the statistical distributions of model parameters for each healthy or pathologic conditions considered, addressing the applicability of the achieved results for the implementation of autonomic diagnosis procedures to support the medical staff during the traditional diagnostic process.