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Title: [Establishment of comprehensive prediction model of acute gastrointestinal injury classification of critically ill patients].
Author: Wang Y, Wang J, Liu W, Zhang G.
Journal: Zhonghua Wei Chang Wai Ke Za Zhi; 2018 Mar 25; 21(3):325-330. PubMed ID: 29577222.
Abstract:
OBJECTIVE: To develop the comprehensive prediction model of acute gastrointestinal injury (AGI) grades of critically ill patients. METHODS: From April 2015 to November 2015, the binary channel gastrointestinal sounds (GIS) monitor system which has been developed and verified by the research group was used to gather and analyze the GIS of 60 consecutive critically ill patients who were admitted in Critical Care Medicine of Chinese PLA General Hospital. Also, the AGI grades (Grande I(-IIII(, the higher the level, the heavier the gastrointestinal dysfunction) were evaluated. Meanwhile, the clinical data and physiological and biochemical indexes of included patients were collected and recorded daily, including illness severity score (APACHE II( score, consisting of the acute physiology score, age grade and chronic health evaluation), sequential organ failure assessment (SOFA score, including respiration, coagulation, liver, cardioascular, central nervous system and kidney) and Glasgow coma scale (GCS); body mass index, blood lactate and glucose, and treatment details (including mechanical ventilation, sedatives, vasoactive drugs, enteral nutrition, etc.) Then principal component analysis was performed on the significantly correlated GIS (five indexes of gastrointestinal sounds were found to be negatively correlated with AGI grades, which included the number, percentage of time, mean power, maximum power and maximum time of GIS wave from the channel located at the stomach) and clinical factors after standardization. The top 5 post-normalized main components were selected for back-propagation (BP) neural network training, to establish comprehensive AGI grades models of critically ill patients based on the neural network model. RESULTS: The 60 patients aged 19 to 98 (mean 54.6) years and included 42 males (70.0%). There were 22 cases of multiple fractures, 15 cases of severe infection, 7 cases of cervical vertebral fracture, 7 cases of aortic repair, 5 cases of post-toxicosis and 4 cases of cerebral trauma. There were 33 emergency operation, 10 cases of elecoperectomy and 17 cases of drug treatment. There were 56 cases of diabetes(93.3%). Forty-five cases (75.0%) used vasoactive drugs, 37 cases (61.7%) used mechanical ventilation and 44 cases (73.3%) used enteral nutrition. APACHE II( score were 4.0 to 28.0(average 16.8) points. Four clinical factors were significantly positively related with AGI grades, including lactic acid level (r=0.215, P=0.000), SOFA score (r=0.383, P=0.000), the use of vascular active drugs (r=0.611, P=0.000) and mechanical ventilation (r=0.142, P=0.014). In addition to the five indexes of gastric bowel sounds which were found to be negatively correlated with AGI grades, the characteristics of 333 by 9 were composed of these nine indexes with high correlation of AGI grades. Five main components were selected after principal component analysis of these nine correlated indexes. A comprehensive AGI grades model of critically ill patients with a fitting degree of 0.967 3 and an accuracy rate of 82.61% was built by BP artificial neural network. CONCLUSION: The comprehensive model to classify AGI grades with the GIS is developed, which can help further predicting the classification of AGI grades of critically ill patients.

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