Predicting severe renal dysfunction in alcohol-associated cirrhosis: Comparative performance of liver function scores
Julian Müller-Kühnle1,2,3, Moritz Schanz1, Severin Schricker1
1Department of General Internal Medicine and Nephrology, Robert Bosch Hospital, Stuttgart, Germany.
Insights
The MELD score effectively identifies advanced kidney dysfunction in alcoholic cirrhosis patients. Machine learning models using clinical data further improve prediction accuracy for better risk stratification.
Area of Science:
- Hepatology
- Nephrology
- Medical Informatics
Background:
- Renal dysfunction and chronic kidney disease (CKD) are common in cirrhosis but often underdiagnosed.
- Early identification of patients at risk for severe kidney dysfunction is crucial for timely intervention.
- Conventional scores like MELD, Child-Pugh, APRI, and FIB-4 have uncertain predictive value for advanced renal dysfunction in cirrhosis.
Purpose of the Study:
- To evaluate the predictive ability of MELD, Child-Pugh Score (CPS), APRI, and FIB-4 for severe renal dysfunction (CKD stage ≥3) in patients with alcoholic cirrhosis.
- To develop and assess machine learning (ML) models for identifying non-renal predictors of advanced CKD in this population.
Main Methods:
- Retrospective cohort study of 131 patients with alcoholic cirrhosis (2014-2021).
- Analysis of MELD, CPS, APRI, and FIB-4 for prediction of CKD stage ≥3 (KDIGO classification).
- Logistic/linear regression and machine learning (Random Forest) models were employed.
Main Results:
- 33% of patients had CKD stage ≥3.
- MELD score significantly predicted advanced CKD (OR=1.379, p<0.001), with prevalence rising from 17% (MELD ≤9) to 80% (MELD ≥20).
- An optimized Random Forest ML model achieved 75.7% AUC, 76% accuracy, 82% sensitivity, and 63% specificity.
Conclusions:
- MELD is the most reliable conventional score for detecting advanced renal dysfunction in alcoholic cirrhosis.
- ML models utilizing routine clinical parameters enhance predictive performance for CKD risk stratification.
- These findings support improved identification and management of renal dysfunction in high-risk cirrhosis patients.
Background:
Renal dysfunction is a frequent and clinically relevant complication of cirrhosis, yet chronic kidney disease (CKD) often remains underrecognized, particularly in non-acute settings. Early identification of at-risk patients is essential to guide timely interventions. Although MELD, Child-Pugh Score (CPS), APRI, and FIB-4 are widely used to assess hepatic disease severity, their predictive value for advanced renal dysfunction is uncertain.
Methods:
In this retrospective cohort study (2014-2021, Klinikum Stuttgart), we evaluated the ability of MELD, CPS, APRI, and FIB-4 to predict severe renal dysfunction (chronic kidney disease [CKD] stage ≥ 3, according to Kidney Disease: Improving Global Outcomes [KDIGO] classification) in patients with alcoholic cirrhosis. Logistic and linear regression analyses were performed. In addition, machine learning (ML) models were trained to identify non-renal predictors of CKD stage ≥ 3.
Results:
Among 131 patients (mean age 62.8 ± 11.3 years; 71% male), 33% met criteria for KDIGO stage ≥ 3. MELD was significantly associated with advanced CKD (OR = 1.379, p < 0.001), with prevalence increasing from 17% (MELD ≤ 9) to 80% (MELD ≥ 20). CPS showed an inverse association (p = 0.002), while APRI and FIB-4 were not predictive. The optimized Random Forest model, refined through ROSE oversampling and feature selection, achieved an AUC of 0.757, with 76% accuracy, 82% sensitivity (KDIGO < 3), and 63% specificity (KDIGO ≥ 3).
Conclusion:
MELD was the most reliable conventional score for identifying advanced renal dysfunction in alcoholic cirrhosis. ML-based models incorporating routinely available clinical parameters further improved predictive performance and may support risk stratification in this high-risk population.
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