Establishment of prediction model of acute gastrointestinal injury classification of critically ill patients based on digital gastrointestinal sounds monitoring

Abstract

To develop the prediction model of acute gastrointestinal injury (AGI) classification of critically ill patients. The binary channel gastrointestinal sounds (GIS) monitor system was used to gather and analyze the GIS of 60 consecutive critically ill patients who were admitted in Critical Care Medicine of PLA General Hospital from April 2015 to November 2015 (patients with chronic gastrointestinal disease or history of gastrointestinal surgery were excluded). Meanwhile, the AGI grades were evaluated according to the ESICM guidelines of AGI grading system. Correlations between GIS and AGI classification were examined with Spearman rank correlation. Then principal component analysis was performed on the significantly correlated parameters after standardization. The top 3 post-normalized main components were selected for back-propagation (BP) neural network training to establish primary AGI grade model of critically ill patients based on the neural network model. A total of 1 132 GIS and 333 AGI were collected from 60 patients. The number (P = 0.0005), percentage of time (P = 0.0004), mean power (P = 0.0088), maximum power (P = 0.0101) and maximum time (P = 0.0025) of GIS wave from the channel located at the stomach were negatively correlated with the AGI grades, while the parameters of GIS wave from the channel located at the intestine had no significant correlation with the AGI grades(all P > 0.05). Three main components were selected after principal component analysis of these five correlated parameters. An AGI grade network model including 9 hide layers, with a fitting degree of 0.981 64 was built by BP artificial neural network based on the analysis of these three main components of GIS. The accuracy rate of the model to predict the AGI grade was 70.83%. The preliminary model based on GIS in classifying AGI grade is established successfully, which can help predict the classification of AGI grade of critically ill patients.