Background & Aims
Three-dimensional high-definition anorectal manometry (3D-HDAM) is used to assess anal sphincter function; it determines profiles of regional pressure distribution along the length and the circumference of the anal canal. There is no consensus, however, on the best way to analyze data from 3D-HDAM to distinguish healthy individuals from persons with sphincter dysfunction. We developed a computer analysis system to analyze 3D-HDAM data and to aid in the diagnosis and assessment of patients with fecal incontinence (FI).
Methods
In a prospective study, we performed 3D-HDAM analysis of 24 asymptomatic healthy subjects (controls; all women; mean age, 39±10 years) and 24 patients with symptoms of fecal incontinence symptoms (all women, mean age, 58±13 years). Patients completed a standardized questionnaire (fecal incontinence severity index to score the severity of FI symptoms. We developed and evaluated a robust prediction model to distinguish patient with FI from controls using linear discriminant, quadratic discriminant, and logistic regression analyses. In addition to collecting pressure information from the HDAM data, we assessed regional features based on shape characteristics and the anorectal symmetry index.
Results
Low FI severity index scores correlated with low rest pressure (r=0.34), and peak squeeze pressure of the anal canal(r=0.28). The combination of pressure values, anal sphincter area, and reflective symmetry values was identified in patients with FI vs controls with an area under the curve value of 1.0. In logistic regression analyses using different predictors, the model identified patients with FI with an area under the curve value of 0.96 (interquartile range [IQR], 0.22). In discriminant analysis, results were classified with a minimum error of 0.02, calculated using 10-fold cross validation; different combinations of predictors produced median classification errors of 0.16 in linear discriminant analysis (IQR, 0.25) and 0.08 in quadratic discriminant analysis (IQR, 0.25).
Conclusion
We developed and validated a novel prediction model to analyze 3D-HDAM data. This system can accurately distinguish patients with FI from controls.