Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques

Background/objective Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients during the first week of hospitalization often presents significant challenges. Current models such as the King's College Criteria (KCC) and the Acute Liver Failure Study Group (ALFSG) Prognostic Index are developed to predict outcome using only a single time point on hospital admission. Models using longitudinal data are not currently available for APAP-ALF patients. We aim to develop and compare performance of prediction models for outcomes during the first week of hospitalization for APAP-ALF patients. Methods Models are developed for the ALFSG registry data to predict longitudinal outcomes for 1042 APAP-ALF patients enrolled 01/1998–02/2016. The primary outcome is defined as daily low versus high coma grade. Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP) and area under the receiver operating curve (AUC) are compared between the following models: classification and regression tree, random forest, frequentist generalized linear mixed model (GLMM), Bayesian GLMM, BiMM tree, and BiMM forest using original and imputed datasets. Results BiMM tree offers predictive (test set) 63% AC, 72% SP and 53% SN for the original dataset, whereas BiMM forest offers predictive (test set) 69% AC, 63% SP and 74% SN for the imputed dataset. BiMM tree has the highest AUC for the original testing dataset (0.697), whereas BiMM forest and standard random forest have the highest AUC for the imputed testing dataset (0.749). The three most important predictors of daily outcome for the BiMM tree are pressor use, bilirubin and creatinine. The BiMM forest model identifies lactate, ammonia and ALT as the three most important predictors of outcome. Conclusions BiMM tree offers a prognostic tool for APAP-ALF patients, which has good accuracy and simple interpretation of predictors which are consistent with clinical observations. BiMM tree and forest models are developed using the first week of in-patient data and are appropriate for predicting outcome over time. While the BiMM forest has slightly higher predictive AC, the BiMM tree model is simpler to use at the bedside.