A retrospective study to evaluate Hy's Law, DrILTox ALF Score, Robles-Diaz model and a new logistic regression model for predicting acute liver failure in Chinese patients with drug-induced liver injury.

OBJECTIVES: To evaluate Hy's law, DrILTox ALF Score, Robles-Diaz Model and a new logistic regression model for predicting acute liver failure (ALF) in Chinese patients with drug-induced liver injury (DILI).

METHODS: we conducted a retrospective study among 514 hospitalized DILI patients from 2011 to 2020. Logistic regression analysis was used to develop a predictive model for ALF. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of these models were compared. Another 304 DILI patients were used for external validation.

OUTCOMES: 26 of 514 DILI patients progressed to ALF. Among these models, Hy's law had 84.6% sensitivity, 59.8% specificity, 10.1% PPV and 98.6% NPV. DrILTox ALF Score had 92.3% sensitivity, 51.8% specificity, 9.3% PPV and 99.2% NPV, while Robles-Diaz Model had 50.0% sensitivity, 77.7% specificity, 10.7% PPV and 96.7% NPV. The logistic regression model described as P = 1 / (1+e1.643-0.006*TBIL (μmol/L)-1.302*INR+0.095*ALB (g/L)) had 88.5% sensitivity, 73.1% specificity, 16.3% PPV and 99.1% NPV at the cut-off of 0.04778 and kept 94.4% sensitivity, 66.8% specificity, 15.2% PPV and 99.5% NPV in external validation.

CONCLUSIONS: The logistic regression model provided superior performance than Hy's law, DrILTox ALF Score and Robles-Diaz Model for predicting DILI related ALF.