Machine learning-based model for predicting the esophagogastric variceal bleeding risk in liver cirrhosis patients

Abstract

Background Liver cirrhosis patients are at risk for esophagogastric variceal bleeding (EGVB). Herein, we aimed to estimate the EGVB risk in patients with liver cirrhosis using an artificial neural network (ANN). Methods We included 999 liver cirrhosis patients hospitalized at the Beijing Ditan Hospital, Capital Medical University in the training cohort and 101 patients from Shuguang Hospital in the validation cohort. The factors independently affecting EGVB occurrence were determined via univariate analysis and used to develop an ANN model. Results The 1-year cumulative EGVB incidence rates were 11.9 and 11.9% in the training and validation groups, respectively. A total of 12 independent risk factors, including gender, drinking and smoking history, decompensation, ascites, location and size of varices, alanine aminotransferase (ALT), γ-glutamyl transferase (GGT), hematocrit (HCT) and neutrophil-lymphocyte ratio (NLR) levels as well as red blood cell (RBC) count were evaluated and used to establish the ANN model, which estimated the 1-year EGVB risk. The ANN model had an area under the curve (AUC) of 0.959, which was significantly higher than the AUC for the North Italian Endoscopic Club (NIEC) (0.669) and revised North Italian Endoscopic Club (Rev-NIEC) indices (0.725) (all P < 0.001). Decision curve analyses revealed improved net benefits of the ANN compared to the NIEC and Rev-NIEC indices. Conclusions The ANN model accurately predicted the 1-year risk for EGVB in liver cirrhosis patients and might be used as a basis for risk-based EGVB surveillance strategies.

Assessment of nomogram model for the prediction of esophageal variceal hemorrhage in hepatitis B-induced hepatic cirrhosis

“…Hemoglobin (HB) is reported as another independent risk factor to evaluate EVH in HBV-induced cirrhosis patients [41]. Spleen width (SW) has also been suggested as an independent predictor of EVH [42]. In our study, we found that there were significant differences in PLT, ALB, HB and SW between patients in the EVs without hemorrhage (EVH−) and EVs with hemorrhage (EVH+) in the single factor analysis.…”

Trends of hospitalisation among new admission inpatients with oesophagogastric variceal bleeding in cirrhosis from 2014 to 2019 in the Affiliated Hospital of Southwest Medical University: a single-centre time-series analysis

“…This is the first time it has been used in patients with EGVB in cirrhosis. In addition, other models such as clinical-radiomics nomogram16 35 and machine learning-based model15 have been used to predict the risk of EGVB in patients with liver cirrhosis in previous studies. However, the majority of them aimed at the prediction of bleeding risk in patients to prevent the occurrence of EGVB and promote EGVB surveillance strategies.…”

“… 13 Therefore, we speculate that this may be the reason for the increasing annual incidence of EGVB. Extensive coverage has been given to customised treatments and interventions aimed at averting the development of additional secondary combinations, 14 diverse perdition models of EVGB 15 16 and evolving treatment techniques. 17 18 These enlighten us that developing a forecasting system for evaluating bleeding risk based on local data, enhancing the technology of intraregional endoscopic and interventional treatment and close follow-up are all significant breakthroughs in managing the escalating incidence rate of EGVB.…”

Future directions of noninvasive prediction of esophageal variceal bleeding: No worry about the present computed tomography inefficiency

“…The authors showed that the sensitivity and specificity could be greater than 70%. Another team tried to manipulate an ANN model, which included both demographic and laboratory parameters, to estimate the 1-year EVB risk[ 19 ]. The model was able to perform the prediction with an AUC of 0.959.…”