Yang Fei1, Jian Hu2, Kun Gao1, Jianfeng Tu1, Wei Wang3, Wei-Qin Li4. 1. Surgical Intensive Care Unit, Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. 2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China. 3. Department of General Surgery, Bayi Hospital Affiliated Nanjing University of Chinese Medicine/ the 81st hospital of P.L.A., Nanjing, China. 4. Surgical Intensive Care Unit, Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China. Electronic address: chamskuler@163.com.
Abstract
BACKGROUND: Acute pancreatitis (AP) can induce portosplenomesenteric vein thrombosis (PVT), which may generate higher morbidity and mortality. However current diagnostic modalities for PVT are still controversial. In recent decades, artificial neural networks have been increasingly applied in medical research. The aim of this study is to predict the risk of AP-induced PVT by radial basis function (RBF) artificial neural networks (ANNs) model. METHODS: A retrospective or consecutive study of 426 individuals with AP at our unit between January 1, 2011 and July 31, 2016 was conducted. All individuals were subjected to RBF ANNs. Variables included age, gender, red blood cell specific volume (Hct), prothrombin time (PT), fasting blood glucose, D-Dimer, concentration of serum calcium ([Ca2+]), triglyceride, serum amylase (AMY), acute physiology and chronic health evaluation II score, and Ranson score. All outcomes were derived after subjecting the variables to a statistical analysis. RESULTS: In the RBF ANNs model, D-dimer, AMY, Hct, and PT were the important factors among all 11 independent variables for PVT. The normalized importance of them was 100%, 96.3%, 71.9%, and 68.2%, respectively. The predict sensitivity, specificity, and accuracy by RBF ANNs model for PVT were 76.2%, 92.0%, and 88.1%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (95% CI: 110.9% [-0.4 to 15.8%]; 8.4% [-3.3 to 19.2%]; and 12.8% [1.6-20.7%], respectively). In addition, the area under receiver operating characteristic curves value for identifying thrombosis when using the RBF ANNs model was 0.892 ± 0.091 (95% CI: 0.805-0.951), demonstrating better overall performance than the logistic regression model (0.762 ± 0.073; 95% CI: 0.662-0.839). CONCLUSIONS: The RBF ANNs model was a valuable tool in predicting the risk of PVT following AP. AMY, D-dimer, PT, and Hct were important prediction factors of approval for AP-induced PVT.
BACKGROUND: Acute pancreatitis (AP) can induce portosplenomesenteric vein thrombosis (PVT), which may generate higher morbidity and mortality. However current diagnostic modalities for PVT are still controversial. In recent decades, artificial neural networks have been increasingly applied in medical research. The aim of this study is to predict the risk of AP-induced PVT by radial basis function (RBF) artificial neural networks (ANNs) model. METHODS: A retrospective or consecutive study of 426 individuals with AP at our unit between January 1, 2011 and July 31, 2016 was conducted. All individuals were subjected to RBF ANNs. Variables included age, gender, red blood cell specific volume (Hct), prothrombin time (PT), fasting blood glucose, D-Dimer, concentration of serum calcium ([Ca2+]), triglyceride, serum amylase (AMY), acute physiology and chronic health evaluation II score, and Ranson score. All outcomes were derived after subjecting the variables to a statistical analysis. RESULTS: In the RBF ANNs model, D-dimer, AMY, Hct, and PT were the important factors among all 11 independent variables for PVT. The normalized importance of them was 100%, 96.3%, 71.9%, and 68.2%, respectively. The predict sensitivity, specificity, and accuracy by RBF ANNs model for PVT were 76.2%, 92.0%, and 88.1%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (95% CI: 110.9% [-0.4 to 15.8%]; 8.4% [-3.3 to 19.2%]; and 12.8% [1.6-20.7%], respectively). In addition, the area under receiver operating characteristic curves value for identifying thrombosis when using the RBF ANNs model was 0.892 ± 0.091 (95% CI: 0.805-0.951), demonstrating better overall performance than the logistic regression model (0.762 ± 0.073; 95% CI: 0.662-0.839). CONCLUSIONS: The RBF ANNs model was a valuable tool in predicting the risk of PVT following AP. AMY, D-dimer, PT, and Hct were important prediction factors of approval for AP-induced PVT.
Authors: Antonio Rivero-Juárez; David Guijo-Rubio; Francisco Tellez; Rosario Palacios; Dolores Merino; Juan Macías; Juan Carlos Fernández; Pedro Antonio Gutiérrez; Antonio Rivero; César Hervás-Martínez Journal: PLoS One Date: 2020-01-10 Impact factor: 3.240