Use of Non-Invasive Biomarkers and Clinical Scores to Predict the Complications of Liver Cirrhosis: A Bicentric Experience.

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Background and objectives: Liver cirrhosis is a chronic, progressive condition characterized by fibrosis and architectural distortion of the liver, leading to impaired liver function and severe complications. Accurately predicting these complications is crucial to the improvement of patient outcomes. Therefore, this study aimed to evaluate the accuracy of various non-invasive biomarkers and clinical scores in assessing the risk of complications among cirrhotic patients. Materials and methods: We conducted an observational retrospective study involving 236 cirrhotic patients from two tertiary care hospitals in Italy and Romania, in a timespan ranging from January 2021 to March 2024. Data on clinical characteristics, liver function tests, hematological indices, various non-invasive biomarkers, and clinical scores were collected and analyzed. Receiver operating characteristic analysis was performed to assess the accuracy of these biomarkers and clinical scores in predicting complications, including the presence of varices and hepato-renal syndrome. Results: The Child-Pugh score showed the highest accuracy for cirrhosis-related complications, with an area under curve (AUC) = 0.667. The red cell distribution width coefficient of variation followed closely with an AUC = 0.646. While the Child-Pugh score had a high specificity (85.42%), its sensitivity was low (37.97%). In patients with varices, non-invasive scores such as platelet distribution width (PDW) and the RDW-to-platelet ratio (RPR) showed modest predictive ability, with an AUC = 0.594. For hepato-renal syndrome, the Model for End-Stage Liver Disease (MELD) score showed the highest diagnostic accuracy with an AUC = 0.758. Conclusions: The most reliable biomarkers for detecting complications, varices, and hepato-renal syndrome, are, respectively, the Child-Pugh Score, PDW along with RPR, and the MELD score. However, while these scores remain valuable, the moderate diagnostic accuracy of other indices suggests the need for a more integrated approach to risk stratification. Future research should focus on validating these tools across different populations and incorporating emerging biomarkers to enhance predictive accuracy and inform more effective clinical decision-making.