Fibro predict a machine learning risk score for advanced liver fibrosis in the general population using Israeli electronic health records.

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.