APASL‐ACLF Research Consortium–Artificial Intelligence (AARC‐AI) model precisely predicts outcomes in acute‐on‐chronic liver failure patients

摘要

Abstract Background and Aims We hypothesized that artificial intelligence (AI) models are more precise than standard models for predicting outcomes in acute‐on‐chronic liver failure (ACLF). Methods We recruited ACLF patients between 2009 and 2020 from APASL‐ACLF Research Consortium (AARC). Their clinical data, investigations and organ involvement were serially noted for 90‐days and utilized for AI modelling. Data were split randomly into train and validation sets. Multiple AI models, MELD and AARC‐Model, were created/optimized on train set. Outcome prediction abilities were evaluated on validation sets through area under the curve (AUC), accuracy, sensitivity, specificity and class precision. Results Among 2481 ACLF patients, 1501 in train set and 980 in validation set, the extreme gradient boost‐cross‐validated model (XGB‐CV) demonstrated the highest AUC in train (0.999), validation (0.907) and overall sets (0.976) for predicting 30‐day outcomes. The AUC and accuracy of the XGB‐CV model (Δ) were 7.0 and 6.9 higher than the standard day‐7 AARC model (p?