Stratifying and predicting progression to acute liver failure during the early phase of acute liver injury.

Acute liver failure (ALF) is a serious disease that progresses from acute liver injury (ALI) and that often leads to multiorgan failure and ultimately death. Currently, effective treatment strategies for ALF, aside from transplantation, remain elusive, partly because ALI is highly heterogeneous. Furthermore, clinicians lack a quantitative indicator that they can use to predict which patients hospitalized with ALI will progress to ALF and the need for liver transplantation. In our study, we retrospectively analyzed data from 319 patients admitted to the hospital with ALI. By applying a machine-learning approach and by using the SHapley Additive exPlanations (SHAP) algorithm to analyze time-course blood test data, we identified prothrombin time activity percentage (PT%) as a biomarker reflecting individual ALI status. Unlike previous studies predicting the need for liver transplantation in patients with ALF, our study focused on PT% dynamics. Use of this variable allowed us to stratify the patients with highly heterogeneous ALI into six groups with distinct clinical courses and prognoses, i.e. self-limited, intensive care-responsive, or intensive care-refractory patterns. Notably, these groups were well predicted by clinical data collected at the time of admission. Additionally, utilizing mathematical modeling and machine learning, we assessed the predictability of individual PT% dynamics during the early phase of ALI. Our findings may allow for optimizing medical resource allocation and early introduction of tailored individualized treatment, which may result in improving ALF prognosis.