title: Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree models.
abstract: OBJECTIVE
  Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients often presents significant challenges. King's College (KCC) has been validated on hospital admission, but little has been published on later phases of illness. We aimed to improve determinations of prognosis both at the time of and following admission for APAP-ALF using Classification and Regression Tree (CART) models.
  
  
  METHODS
  CART models were applied to US ALFSG registry data to predict 21-day death or liver transplant early (on admission) and post-admission (days 3-7) for 803 APAP-ALF patients enrolled 01/1998-09/2013. Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP), and area under receiver-operating curve (AUROC) were compared between 3 models: KCC (INR, creatinine, coma grade, pH), CART analysis using only KCC variables (KCC-CART) and a CART model using new variables (NEW-CART).
  
  
  RESULTS
  Traditional KCC yielded 69% AC, 90% SP, 27% SN, and 0.58 AUROC on admission, with similar performance post-admission. KCC-CART at admission offered predictive 66% AC, 65% SP, 67% SN, and 0.74 AUROC. Post-admission, KCC-CART had predictive 82% AC, 86% SP, 46% SN and 0.81 AUROC. NEW-CART models using MELD (Model for end stage liver disease), lactate and mechanical ventilation on admission yielded predictive 72% AC, 71% SP, 77% SN and AUROC 0.79. For later stages, NEW-CART (MELD, lactate, coma grade) offered predictive AC 86%, SP 91%, SN 46%, AUROC 0.73.
  
  
  CONCLUSIONS
  CARTs offer simple prognostic models for APAP-ALF patients, which have higher AUROC and SN than KCC, with similar AC and negligibly worse SP. Admission and post-admission predictions were developed.
  
  
  CONCLUSIONS
  • Prognostication in acetaminophen-induced acute liver failure (APAP-ALF) is challenging beyond admission • Little has been published regarding the use of King's College Criteria (KCC) beyond admission and KCC has shown limited sensitivity in subsequent studies • Classification and Regression Tree (CART) methodology allows the development of predictive models using binary splits and offers an intuitive method for predicting outcome, using processes familiar to clinicians • Data from the ALFSG registry suggested that CART prognosis models for the APAP population offer improved sensitivity and model performance over traditional regression-based KCC, while maintaining similar accuracy and negligibly worse specificity • KCC-CART models offered modest improvement over traditional KCC, with NEW-CART models performing better than KCC-CART particularly at late time points.
authors.affiliation: Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, United States of America.
authors.forename: Jaime Lynn
authors.initials: JL
authors.lastname: Speiser
authors.affiliation: Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.
authors.forename: William M
authors.initials: WM
authors.lastname: Lee
authors.affiliation: Divisions of Hepatology and Critical Care Medicine, University of Alberta, Edmonton, Canada.
authors.forename: Constantine J
authors.initials: CJ
authors.lastname: Karvellas
mesh_terms: D000082:Acetaminophen / Q000633:toxicity*; D000328:Adult; D000368:Aged; D019540:Area Under Curve; D016208:Databases, Factual; D003710:Demography; D005260:Female; D006801:Humans; D017114:Liver Failure, Acute / Q000209:etiology* / Q000473:pathology / Q000628:therapy; D016031:Liver Transplantation; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012372:ROC Curve; D012042:Registries; D012720:Severity of Illness Index
chemical_list: D000082:Acetaminophen