Abstract

Although most drug-induced liver injury (DILI) cases resolve after the offending medication is discontinued, time to recovery varies among patients, with 6 -12% developing a chronic disease. Herein, we investigated clinical factors and drug properties as potential risk determinants that influence the time course for DILI recovery and developed a model to predict its trajectory. We applied an accelerated failure time model to 294 cases collected by the International Drug-Induced Liver Network Consortium (iDILIC). Factors included in the multivariate recovery score model were selected through univariate analysis. The model was externally validated using 257 cases from the Spanish DILI Registry and 191 cases from the LiverTox database. Higher serum bilirubin and alkaline phosphatase (ALP) at DILI onset, a longer time to onset, and non-significant drug metabolism were associated with a longer recovery and were included in the recovery score model. We defined high- and low-risk groups based on the scores assigned by the model. The estimated probability of recovery by 6 months was 0.46 (95% CI 0.26-0.61) for the high-risk group and 0.93 (95% CI 0.58-0.99) for the low-risk group in the iDILIC. Model performance was validated in both validation sets. The high- and low-risk cases identified bythe model showed a significantly different time course for recovery, with a majority of low-risk cases recovering sooner. The trajectory of biochemical recovery from DILI is predicted by the extent of drug metabolism, serum bilirubin and ALP at DILI onset. The model can be used to compute an estimated DILI recovery and, when a significant delay is predicted, clinicians may consider additional investigations such as histologic evaluation or extended follow-up. In this study, we investigated whether drug properties and clinical factors are associated with the time it takes to recover from drug-induced liver injury (DILI). We found that total bilirubin, alkaline phosphatase level at DILI onset, time to onset, and extent of drug metabolism were consistently associated with recovery time. Using these factors, we built a model to predict the trajectory of recovery from DILI and validated this model in 2 independent cohorts. Our findings offer important insights into the factors influencing the trajectory of recovery from DILI. Additional investigations and longer follow-ups can be planned in those for whom a delayed recovery is predicted.

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