Development and validation of a dynamic outcome prediction model for paracetamol-induced acute liver failure: a cohort study. (November 2016)

• Record Type: Journal Article
• Authors: Bernal, William; Wang, Yanzhong; Maggs, James; Willars, Christopher; Sizer, Elizabeth; Auzinger, Georg; Murphy, Nicholas; Harding, Damian; Elsharkawy, Ahmed; Simpson, Kenneth; Larsen, Fin Stolze; Heaton, Nigel; O'Grady, John; Williams, Roger; Wendon, Julia
• Is Part Of: Lancet gastroenterology and hepatology. Volume 1:Number 3(2016)
• Page Start: 217
• Page End: 225
• Publication Date: 2016-11
• DOI: 10.1016/S2468-1253(16)30007-3
• Languages: English
• ISSNs: 2468-1253

Abstract

Summary: Background: Early, accurate prediction of survival is central to management of patients with paracetamol-induced acute liver failure to identify those needing emergency liver transplantation. Current prognostic tools are confounded by recent improvements in outcome independent of emergency liver transplantation, and constrained by static binary outcome prediction. We aimed to develop a simple prognostic tool to reflect current outcomes and generate a dynamic updated estimation of risk of death. Methods: Patients with paracetamol-induced acute liver failure managed at intensive care units in the UK (London, Birmingham, and Edinburgh) and Denmark (Copenhagen) were studied. We developed prognostic models, excluding patients who underwent transplantation, using Cox proportional hazards in a derivation dataset, and tested in initial and recent external validation datasets. Mortality was estimated in patients who had emergency liver transplantation. Model discrimination was assessed using area under receiver operating characteristic curve (AUROC) and calibration by root mean square error (RMSE). Admission (day 1) variables of age, Glasgow coma scale, arterial pH and lactate, creatinine, international normalised ratio (INR), and cardiovascular failure were used to derive an initial predictive model, with a second (day 2) model including additional changes in INR and lactate. Findings: We developed and validated new high-performance statistical models to support decision making in patients with paracetamol-induced acute liver failure. Applied to the derivation dataset (n=350), the AUROC for 30-day survival was 0·92 (95% CI 0·88–0·96) using the day 1 model and 0·93 (0·88–0·97) using the day 2 model. In the initial validation dataset (n=150), the AUROC for 30-day survival was 0·89 (0·84–0·95) using the day 1 model and 0·90 (0·85–0·95) using the day 2 model. Assessment of calibration using RMSE in prediction of 30-day survival gave values of 0·1642 for the day 1 model and 0·0626 for the day 2 model. In the external validation dataset (n=412), the AUROC for 30-day survival was 0·91 (0·87–0·94) using the day 1 model and 0·91 (0·88–0·95) using the day 2 model, and assessment of calibration using RMSE gave values of 0·079 for the day 1 model and 0·107 for the day 2 model. Applied to patients who underwent emergency liver transplantation (n=116), median predicted 30-day survival was 51% (95% CI 33–85). Interpretation: The models developed here show very good discrimination and calibration, confirmed in independent datasets, and suggest that many patients undergoing transplantation based on existing criteria might have survived with medical management alone. The role and indications for emergency liver transplantation in paracetamol-induced acute liver failure require re-evaluation. Funding: Foundation for Liver Research.