Three-dimensional analysis of pharyngeal high-resolution manometry data.

OBJECTIVES/HYPOTHESIS High-resolution manometry (HRM) represents a critical advance in the quantification of swallow-related pressure events in the pharynx. Previous analyses of the pressures measured by HRM, though, have been largely two-dimensional, focusing on a single sensor in a given region. We present a three-dimensional approach that combines information from adjacent sensors in a region. Two- and three-dimensional methods were compared for their ability to classify data correctly as normal or disordered. STUDY DESIGN Case series evaluating new method of data analysis. METHODS A total of 1,324 swallows from 16 normal subjects and 61 subjects with dysphagia were included. Two-dimensional single sensor integrals of the area under the curves created by rises in pressure in the velopharynx, tongue base, and upper esophageal sphincter (UES) were calculated. Three-dimensional multi-sensor integrals of the volume under all curves corresponding to the same regions were also computed. The two sets of measurements were compared for their ability to classify data correctly as normal or disordered using an artificial neural network (ANN). RESULTS Three-dimensional parameters yielded a maximal classification accuracy of 86.71% ± 1.47%, while two-dimensional parameters achieved a maximum accuracy of 83.36% ± 1.42%. Combining two- and three-dimensional parameters with all other variables, including three-dimensional parameters, yielded a classification accuracy of 96.99% ± 0.51%. Including two-dimensional parameters yielded a classification accuracy of 96.32% ± 1.05%. CONCLUSION Three-dimensional analysis led to improved classification of swallows based on pharyngeal HRM. Artificial neural network performance with both two-dimensional and three-dimensional analyses was effective, classifying a large percentage of swallows correctly, thus demonstrating its potential clinical utility.