Machine learning outperforms serum creatinine for early risk stratification of hepatorenal syndrome: a prospective single-center validation.

BACKGROUND: The early diagnosis of hepatorenal syndrome (HRS) is constrained by the reliance on serum creatinine, a biomarker with well-documented limitations in sensitivity and timeliness, contributing to diagnostic delays and adverse outcomes. Machine learning (ML) offers a potential solution, but its translation is hindered by two critical gaps: the absence of prospective, head-to-head validation against the clinical standard, and inadequate assessment of model robustness against temporal data distribution shift-a pivotal challenge for real-world deployment.

METHODS: We developed an XGBoost model using a retrospective cohort of patients with decompensated cirrhosis (n = 464). Its performance was then prospectively validated in a completely independent, temporally distinct cohort (n = 269) in a direct comparison against the serum creatinine diagnostic standard. The prospective cohort was further split into sequential subsets to assess short-term internal temporal robustness. The evaluation framework encompassed diagnostic performance, interpretability (SHAP analysis), clinical utility (decision curve analysis), and health economic evaluation.

RESULTS: The model demonstrated exceptional discriminatory accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.994 (95% bias-corrected and accelerated bootstrap CI: 0.985-0.998). This represented a substantial and statistically significant improvement over serum creatinine-based diagnosis (AUC = 0.803; p < 0.001). The model's predictions were well-calibrated, and its performance remained stable in the limited internal temporal robustness assessment. The health economic evaluation established the model's decisive cost-effectiveness, with an incremental cost-effectiveness ratio (ICER) of 571 EUR per quality-adjusted life year (QALY), substantially below major international willingness-to-pay thresholds, including Indonesia's benchmark of 3853 EUR/QALY.

CONCLUSION: This study provides three key contributions: (1) conclusive, prospective evidence that an ML model significantly outperforms the current serum creatinine standard for HRS risk stratification; (2) a novel methodological framework for proactively assessing temporal robustness, which demonstrated stability in a short-term, single-center setting; and (3) compelling evidence of the model's accuracy, clinical utility, and cost-effectiveness within a rigorous, single-center prospective validation. While these results establish a high-fidelity proof of concept, the essential next steps are external validation across multiple centers to confirm generalizability and the assessment of resilience to longer-term, real-world dataset shifts before any consideration of broader clinical implementation.