SPARC: Correlation between Patient Characteristics in Gastrointestinal Evaluation using Principal Component Analysis

Abstract

Introductionanorectal function is complex. Assessment of anorectal physiology and diagnostics for anorectal disorders face problems due to inconsistent results with different technologies and huge inter‐subject variability. This limits development of therapeutic strategies. Devices used to evaluate anorectal function include Fecobionics, EndoFLIP, Anorectal manometry and Ultrasound. The goal of this study was to find correlation in patient characteristics based on data obtained with these measurements.Methodsthe four tests mentioned above were used to evaluate anorectal function in normal subjects and patients with fecal incontinence (FI) or constipation. The data were cleaned by replacing unknown values with the average of measurements, and they were standardized. In total there were 90 measurements and 35 features. The collected data included sex, age (60.0±14.3 years), weight (59.0±9.0 kg), height (1.6±0.1m), FI score (16.8±12.8 with normal values =< 5), constipation score (6.5±4.4 with normal values =< 8), anorectal maximum pressure (46.9±10.1 mmHg), resting pressure (61±25.8 mmHg), expel time (46.3±63.2s), and absence or presentation of rectoanal inhibitory reflex (RAIR), etc. Principal component analysis (PCA) was used to find the contribution of each reduced feature in variance of data and the correlation between features, utilizing singular value decomposition. We used Python and its libraries.Results and Discussionthe variance in data was mostly explained using 25 PCs. The percentage of cumulative variance explained by up to the 1th, 25th and 35th PC was 15%, 98% and 100%, respectively. The PCs provided insights about the relation between features. For example, according to the first PC the maximum pressure, resting pressure, maximum tolerance and age were positively correlated. Also, weight, absence of RAIR and constipation score were positively correlated. However, the latter group of parameters were negatively correlated to the former group. Because the number of patients used in this study were limited results should be interpreted with caution. However, more data are currently being collected, which will be included in our analysis. We plan to extend our analysis to cluster patient data using machine learning.Conclusionsin this study we analyzed data gathered in anorectal evaluation using PCA. Data obtained provided insights about relation between characteristics of the patients. Analysis of data obtained in anorectal evaluation will help to better understand factors involved in anorectal and other gastrointestinal disorders, which could lead to better preventive, diagnostic and therapeutic strategies.Support or Funding InformationNIH SPARC fundingAcknowledgementsNIH SPARC funding.