Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects 30-40% of US adults and is the most common chronic liver disease. Although often asymptomatic, progression can lead to cirrhosis. The objective of the study was to develop and evaluate an electronic health record (EHR) based prediction model to support early detection of MASLD in primary care settings. Methods: We evaluated LASSO logistic regression, random forest, XGBoost, and a neural network model for MASLD prediction using clinical feature subsets from a large EHR database, including the top 10 ranked features. To reduce disparities in true positive rates across racial and ethnic subgroups, we applied an equal opportunity postprocessing method in a prediction model called MASLD EHR Static Risk Prediction (MASER). Results: This retrospective cohort study included 59,492 participants in the training data, 24,198 in the validating data, and 25,188 in the testing data. The LASSO logistic regression model with the top 10 features was selected for its interpretability and comparable performance. Before fairness adjustment, the model achieved AUROC of 0.84, accuracy of 78%, sensitivity of 72%, specificity of 79%, and F1-score of 0.617. After equal opportunity postprocessing, accuracy modestly increased to 81% and specificity to 94%, while sensitivity decreased to 41% and F1-score to 0.515, reflecting the fairness trade-off. Conclusions: MASER achieved competitive performance for MASLD prediction, comparable to previously reported ensemble and tree-based models, while using a limited and routinely collected feature set and a diverse study population. The model is designed to support early detection and potential integration into primary care workflows. MASER demonstrates EHR-ready MASLD prediction with fairness adjustments, supporting future primary care implementation pending prospective validation.

翻译：研究背景：代谢功能障碍相关脂肪性肝病（MASLD）影响美国30%-40%的成年人，是最常见的慢性肝病。尽管通常无症状，但病情进展可能导致肝硬化。本研究旨在开发并评估一种基于电子健康记录（EHR）的预测模型，以支持初级诊疗中对MASLD的早期检测。研究方法：我们采用LASSO逻辑回归、随机森林、XGBoost和神经网络模型，利用大型EHR数据库中的临床特征子集（包括排名前10的特征）进行MASLD预测。为减少不同种族和民族亚组间真阳性率的差异，我们在一项名为MASLD电子健康记录静态风险预测（MASER）的预测模型中应用了机会均等后处理方法。研究结果：本回顾性队列研究包含训练数据中的59,492名参与者、验证数据中的24,198名参与者和测试数据中的25,188名参与者。最终选择基于前10个特征的LASSO逻辑回归模型，因其具有良好的可解释性和可比性能。在公平性调整前，该模型受试者工作特征曲线下面积（AUROC）为0.84，准确率为78%，灵敏度为72%，特异度为79%，F1分数为0.617。经过机会均等后处理，准确率适度提高至81%，特异度提升至94%，但灵敏度下降至41%，F1分数降至0.515，体现了公平性权衡。研究结论：MASER在MASLD预测中取得了与既往报道的集成模型和基于树的模型相当的性能，同时仅使用有限且常规采集的特征集及多样化研究人群。该模型旨在支持早期检测并可能整合至初级诊疗工作流程。MASER展示了经过公平性调整且适用于EHR系统的MASLD预测能力，可在前瞻性验证后支撑未来的初级诊疗应用。