【摘要】 目的 开发并验证预测模型以识别他汀类药物新用药者在180 d内发生急性肝损伤（ALI）的高风险个体，为临床早期干预提供依据。方法 选取2010年1月1日—2021年10月31日在宁波市鄞州区域健康信息平台中具有诊疗记录的18岁及以上他汀类药物新用药者，按用药时间分为开发队列和时序验证队列。采用LASSO方法筛选预测变量，利用极限梯度提升（XGBoost）算法并结合代价敏感学习构建模型。通过Brier分数、Harrell’s C指数和校准曲线评估模型性能。结果 共纳入126 440例他汀类药物新用药者，其中开发队列90 542例，验证队列35 898例。首次用药后180 d内，共412例（0.33%）发生ALI，其中开发队列305例（0.34%），验证队列107例（0.30%）。最终模型纳入16个预测变量，涵盖人口学特征、生活方式、家族史、既往史、他汀类药物用药情况及合并用药情况。模型在内部验证中表现出良好的整体性能[Brier分数=0.004 3,95%CI(0.003 8,0.004 9)]、区分度[Harrell’s C指数=0.761,95%CI(0.725,0.794)]和校准度。时序验证中，模型的性能同样良好[Brier分数=0.004 4,95%CI(0.003 6,0.005 2);Harrell’s C指数=0.703,95%CI(0.614,0.781)]。结论 本研究开发并验证的他汀类药物新用药者ALI预测模型，可为临床医生提供可靠的个体化风险评估工具，有助于实现风险分层并减少ALI的发生。更多还原

【Abstract】 Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI) within 180 days in new statin users, and to support early clinical intervention. Methods Data were sourced from the Yinzhou Regional Health Information Platform, covering statin initiators aged 18 years and older from January 1, 2010, to October 31, 2021. The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation. Predictors were selected using LASSO regression, and the model was constructed using the extreme gradient boosting(XGBoost) algorithm combined with cost-sensitive learning. Model performance was evaluated using Brier scores, Harrell’s C-index, and calibration curves. Results A total of 126,440 statin initiators were included, with 90,542 in the derivation cohort and 35,898 in the validation cohort. Within 180 days of initial statin use, 412(0.33%) patients developed ALI, including 305(0.34%) in the derivation cohort and 107(0.30%) in the validation cohort. The final model incorporated 16 predictors, which included demographic characteristics, lifestyle factors, family history, medical history, statin use, and concomitant medication use. The model demonstrated excellent overall performance [Brier score=0.0043, 95%CI(0.0038, 0.0049)], discrimination [Harrell’s C-index=0.761, 95%CI(0.725, 0.794)], and calibration in internal validation. In temporal validation, the model also performed well [Brier score=0.0044, 95%CI(0.0036, 0.0052), Harrell’s C-index= 0.703, 95%CI(0.614, 0.781)]. Conclusion This study develope and validate a prediction model for ALI in statin users, providing clinicians with a reliable tool for individualized risk assessment. This model can help achieve risk stratification and reduce the occurrence of ALI.更多还原