Development of a warning model for drug-induced liver injury in the older patients.

Introduction: Drug-induced liver injury (DILI) is a significant adverse drug reaction, ranging from mild liver enzyme elevations to severe outcomes such as liver failure, transplantation, or death. This condition is especially concerning in older adults, who may exhibit increased susceptibility to adverse medication effects. This study aimed to develop and compare eight machine learning (ML) models using routine clinical, pharmacological, and laboratory data to predict DILI in older hospitalized patients. Methods: We conducted a retrospective analysis of older patients hospitalized in 2022 who exhibited abnormal liver function tests. A total of 421 clinical, pharmacological, and laboratory variables were utilized for model development, with missing data addressed through multiple imputation techniques. The performance of 8 ML algorithms-XGBoost, LightGBM, Random Forest, AdaBoost, CatBoost, Gradient Boosting Decision Trees, Artificial Neural Network, and TabNet-was assessed. The dataset was randomly partitioned into a training set (80%, n = 2,880) and an independent testing set (20%, n = 720). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Results: Out of the 3,600 older patients with abnormal liver function, 654 patients experienced DILI. The best-performing model, LightGBM combined with Random Forest imputation, achieved an AUC of 0.9829. SHapley Additive exPlanations (SHAP) analysis identified critical predictors for DILI, including the timing of DILI relative to surgery, undergoing surgery, and maximum rate of change (slope) in liver enzymes, albumin, lipoprotein cholesterol, total bilirubin, proBNP, and total bile acids. Additional significant factors included administration of liver-protective medications upon admission; use of diuretics, antibiotics, and narcotic analgesics; and pre-existing liver or gallbladder diseases or malignancies. Discussion: The predictive model developed demonstrated excellent performance in identifying DILI in older adults. Leveraging machine learning techniques, this model holds significant potential for clinical implementation to effectively warn clinicians of DILI risk among older hospitalized patients.