OBJECTIVE: It is difficult to predict the outcome in patients with acute liver failure (ALF) using existing prognostic models. This study investigated whether early changes in the levels of dynamic variables can predict outcome better than models based on static baseline variables. DESIGN: 380 patients with ALF (derivation cohort n=244, validation cohort n=136) participated in a prospective observational study. The derivation cohort was used to identify predictors of mortality. The ALF early dynamic (ALFED) model was constructed based on whether the levels of predictive variables remained persistently high or increased over 3 days above the discriminatory cut-off values identified in this study. The model had four variables: arterial ammonia, serum bilirubin, international normalised ratio and hepatic encephalopathy >grade II. The model was validated in a cohort of 136 patients with ALF. RESULTS: The ALFED model demonstrated excellent discrimination with an area under the receiver operator characteristic curve of 0.91 in the derivation cohort and of 0.92 in the validation cohort. The model was well calibrated in both cohorts and showed a similar increase in mortality with increasing risk scores from 0 to 6. The performance of the ALFED model was superior to the King's College Hospital criteria and the Model for End stage Liver Disease score, even when their 3-day serial values were taken into consideration. An ALFED score of ≥4 had a high positive predictive value (85%) and negative predictive value (87%) in the validation cohort. CONCLUSION: The ALFED model accurately predicted outcome in patients with ALF, which may be useful in clinical decision-making.
OBJECTIVE: It is difficult to predict the outcome in patients with acute liver failure (ALF) using existing prognostic models. This study investigated whether early changes in the levels of dynamic variables can predict outcome better than models based on static baseline variables. DESIGN: 380 patients with ALF (derivation cohort n=244, validation cohort n=136) participated in a prospective observational study. The derivation cohort was used to identify predictors of mortality. The ALF early dynamic (ALFED) model was constructed based on whether the levels of predictive variables remained persistently high or increased over 3 days above the discriminatory cut-off values identified in this study. The model had four variables: arterial ammonia, serum bilirubin, international normalised ratio and hepatic encephalopathy >grade II. The model was validated in a cohort of 136 patients with ALF. RESULTS: The ALFED model demonstrated excellent discrimination with an area under the receiver operator characteristic curve of 0.91 in the derivation cohort and of 0.92 in the validation cohort. The model was well calibrated in both cohorts and showed a similar increase in mortality with increasing risk scores from 0 to 6. The performance of the ALFED model was superior to the King's College Hospital criteria and the Model for End stage Liver Disease score, even when their 3-day serial values were taken into consideration. An ALFED score of ≥4 had a high positive predictive value (85%) and negative predictive value (87%) in the validation cohort. CONCLUSION: The ALFED model accurately predicted outcome in patients with ALF, which may be useful in clinical decision-making.