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Accuracy of a predictive model for severe hepatic fibrosis or cirrhosis in chronic hepatitis C
Agostino Colli1, Alice Colucci, Silvia Paggi
1Postgraduate School of Gastroenterology, Padiglione Granelli 3 piano, IRCCS -Ospedale Maggiore, Via F. Sforza 35, 20122 Milan, Italy.
Insights
A new model accurately identifies patients with low or high probabilities of severe fibrosis/cirrhosis in chronic hepatitis C virus (HCV) infection, reducing the need for liver biopsy in many cases.
Area of Science:
- Hepatology
- Medical Diagnostics
- Virology
Background:
- Chronic hepatitis C virus (HCV) infection can lead to severe liver fibrosis and cirrhosis.
- Accurate diagnosis of advanced fibrosis is crucial for patient management and treatment decisions.
- Non-invasive methods are sought to reduce reliance on liver biopsy.
Purpose of the Study:
- To evaluate the diagnostic accuracy of a novel model for identifying severe fibrosis/cirrhosis in chronic HCV patients.
- To assess the model's ability to stratify patients into probability categories for advanced liver disease.
Main Methods:
- A diagnostic model combining the Bonacini score (ALT/AST ratio, platelets, INR) and liver surface ultrasonography was developed.
- The model was applied to 176 chronic HCV patients, calculating post-test probabilities of severe fibrosis/cirrhosis.
- Predicted probabilities were compared against histological METAVIR staging.
Main Results:
- Severe fibrosis/cirrhosis was present in 38% of the study cohort.
- The model effectively categorized patients: 34% had very low (<10%) or very high (>90%) probability, 33% had high (75-90%) probability, and 33% had an uncertain diagnosis (10-74%).
- Observed frequencies of severe fibrosis/cirrhosis aligned with model predictions.
Conclusions:
- The model accurately identifies a significant proportion of patients (67%) with either high (>75%) or low (<10%) probability of cirrhosis.
- This diagnostic model can potentially reduce the number of patients requiring invasive liver biopsy by one-third.
Aim:
To assess the accuracy of a model in diagnosing severe fibrosis/cirrhosis in chronic hepatitis C virus (HCV) infection.
Methods:
The model, based on the sequential combination of the Bonacini score (BS: ALT/AST ratio, platelet count and INR) and ultrasonography liver surface characteristics, was applied to 176 patients with chronic HCV infection. Assuming a pre-test probability of 35%, the model defined four levels of post-test probability of severe fibrosis/cirrhosis: <10% (low), 10-74% (not diagnostic), 75-90% (high) and >90% (almost absolute). The predicted probabilities were compared with the observed patientso distribution according to the histology (METAVIR).
Results:
Severe fibrosis/cirrhosis was found in 67 patients (38%). The model discriminated patients in three comparable groups: 34% with a very high (>90%) or low (<10%) probability of severe fibrosis, 33% with a probability ranging from 75% to 90%, and 33% with an uncertain diagnosis (i.e., a probability ranging from 10% to 74%). The observed frequency of severe fibrosis/cirrhosis was within the predefined ranges.
Conclusion:
The model can correctly identify 67% of patients with a high (>75%) or low (<10%) probability of cirrhosis, leaving only 33% of the patients still requiring liver biopsy.
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