Kalka Iris N, Hazzan Rawi, Yacovzada Nancy-Sarah, Igbaria Saleh, Segal Eran, Weinberger Adina, Neeman Ziv
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
Sci Rep. 2025 Sep 1;15(1):32035. doi: 10.1038/s41598-025-17534-9.
Liver diseases, notably cirrhosis, pose a substantial global health challenge, resulting in millions of annual deaths. Existing diagnostic methods primarily target high-risk groups, leaving a significant portion of patients undiagnosed. This study aims to develop and validate an a machine-learning prediction model, Fibro-Predict, for the early detection of advanced liver fibrosis in the general population using nationwide electronic health records (EHRs). We constructed a diagnostic framework for liver cirrhosis by analyzing retrospective EHR data from 2,255,580 observations in Israel's largest healthcare provider. The Fibro-Predict model was trained using gradient boosted trees (XGBoost) predicting five-year disease diagnosis trajectories based on routine blood tests and validated both temporally and externally. We conducted a retrospective temporal validation of the model and an external prospective validation in clinical settings, employing transient elastography. The temporal validation of Fibro-Predict demonstrated a promising five-year AUC of 0.81 (95% CI 0.80-0.82) in the training set and 0.79 (95% CI 0.78-0.80) in the validation set. In a clinical context, our framework exhibited an impressive True Positive Rate (TPR) of 36.8% (28/76) when comparing predicted risk to observed outcomes, surpassing the widely used FIB-4, which had a TPR of only 3.7% (1/27). Fibro-Predict, relying solely on routine blood tests and standard demographics, emerges as a valuable tool for cost-effective patient prioritization in advanced fibrosis screening within the general population. By leveraging nationwide EHR data, this approach allows healthcare systems to flag potentially undiagnosed patients earlier and more broadly, streamlining clinical follow-up and expediting diagnosis. This approach holds the potential to significantly improve the early detection of advanced liver fibrosis and subsequently reduce its associated morbidity and mortality.Trial registration: ClinicalTrials.gov Identifier: NCT05218538.
肝脏疾病，尤其是肝硬化，是一项重大的全球健康挑战，每年导致数百万例死亡。现有的诊断方法主要针对高危人群，导致很大一部分患者未被诊断出来。本研究旨在开发并验证一种机器学习预测模型Fibro-Predict，用于利用全国范围的电子健康记录（EHR）在普通人群中早期检测晚期肝纤维化。我们通过分析以色列最大医疗服务提供商的2255580条观察记录的回顾性EHR数据，构建了一个肝硬化诊断框架。Fibro-Predict模型使用梯度提升树（XGBoost）进行训练，根据常规血液检测预测五年疾病诊断轨迹，并在时间上和外部进行了验证。我们对该模型进行了回顾性时间验证，并在临床环境中采用瞬时弹性成像进行了外部前瞻性验证。Fibro-Predict的时间验证显示，训练集中五年AUC为0.81（95%CI 0.80 - 0.82），验证集中为0.79（95%CI 0.78 - 0.80），前景良好。在临床环境中，将预测风险与观察结果进行比较时，我们的框架显示出令人印象深刻的真阳性率（TPR）为36.8%（28/76），超过了广泛使用的FIB-4，后者的TPR仅为3.7%（1/27）。Fibro-Predict仅依靠常规血液检测和标准人口统计学信息，成为普通人群晚期纤维化筛查中具有成本效益的患者优先级划分的宝贵工具。通过利用全国范围的EHR数据，这种方法使医疗系统能够更早、更广泛地标记潜在未诊断的患者，简化临床随访并加快诊断。这种方法有可能显著改善晚期肝纤维化的早期检测，并随后降低其相关的发病率和死亡率。试验注册：ClinicalTrials.gov标识符：NCT05218538。
