Yang Fei1, Kun Gao1, Wei-Qin Li2. 1. Medical School of Nanjing University, Jinling Hospital/Nanjing General Hospital of Nanjing Military Region of P.L.A., P.L.A. Research Institute of General Surgery, Nanjing 210002, China. 2. Medical School of Nanjing University, Jinling Hospital/Nanjing General Hospital of Nanjing Military Region of P.L.A., P.L.A. Research Institute of General Surgery, Nanjing 210002, China. Electronic address: chamskuler@163.com.
Abstract
BACKGROUND: To predict the risk and severity of acute respiratory distress syndrome (ARDS) following severe acute pancreatitis (SAP) by artificial neural networks (ANNs) model. METHODS: ANNs model was constructed by clinical data of 217 SAP patients. The model was first trained on 152 randomly chosen patients, validated and tested on the 33 patients and 32 patients respectively. Statistical analysis was used to assess the value of it. RESULTS: The training, validation, and test set were not significantly different for 13 variables. After training, ANNs retained excellent pattern recognition ability. When ANNs model was applied to the test set, it revealed a sensitivity of 87.5%, and an accuracy of 84.43%. Significant differences were found between ANNs model and logistic regression model. When ANNs model is used to identify ARDS, the area under ROC was 0.859 + 0.048. Meanwhile, pancreatic necrosis rate, lactic dehydrogenase and oxyhemoglobin saturation were the most important independent variables. Compared with the Berlin definition, the ANN model shows a good accuracy of 73.1% for total severity of ARDS. CONCLUSION: ANNs model is a valuable tool in dealing with risk prediction of ARDS following SAP. In addition, it can extract informative risk factors of ARDS via the ANNs model.
BACKGROUND: To predict the risk and severity of acute respiratory distress syndrome (ARDS) following severe acute pancreatitis (SAP) by artificial neural networks (ANNs) model. METHODS: ANNs model was constructed by clinical data of 217 SAP patients. The model was first trained on 152 randomly chosen patients, validated and tested on the 33 patients and 32 patients respectively. Statistical analysis was used to assess the value of it. RESULTS: The training, validation, and test set were not significantly different for 13 variables. After training, ANNs retained excellent pattern recognition ability. When ANNs model was applied to the test set, it revealed a sensitivity of 87.5%, and an accuracy of 84.43%. Significant differences were found between ANNs model and logistic regression model. When ANNs model is used to identify ARDS, the area under ROC was 0.859 + 0.048. Meanwhile, pancreatic necrosis rate, lactic dehydrogenase and oxyhemoglobin saturation were the most important independent variables. Compared with the Berlin definition, the ANN model shows a good accuracy of 73.1% for total severity of ARDS. CONCLUSION: ANNs model is a valuable tool in dealing with risk prediction of ARDS following SAP. In addition, it can extract informative risk factors of ARDS via the ANNs model.
Authors: Nicole L Komara; Pedram Paragomi; Phil J Greer; Anette S Wilson; Cameron Breze; Georgios I Papachristou; David C Whitcomb Journal: Am J Physiol Gastrointest Liver Physiol Date: 2020-09-02 Impact factor: 4.052