European Journal of Cardiovascular Medicine

Chronic liver disease (CLD) is a major cause of morbidity and mortality worldwide, with cirrhosis ranking among the leading causes of years of life lost in India. Early identification of fibrosis progression is critical to guide therapy, prognosis, and surveillance.

Liver biopsy, though the gold standard, is invasive, costly, and associated with complications. Transient elastography (FibroScan®) has improved non-invasive diagnosis but is not universally available in India.

The Bonacini score, developed in 1997, incorporates routine biochemical parameters (AST/ALT ratio, platelet count, INR) to predict fibrosis. While validated in Western populations, its utility in Indian patients remains underexplored.

This study evaluates the accuracy of the Bonacini score against FibroScan® in predicting fibrosis in CLD patients in South India.

Study design & setting: Prospective cross-sectional study at Department of General Medicine, ESIC MC & PGIMSR, Rajajinagar after institutional ethics approval and informed consent.

Population: Adults (>18 yrs) with clinical and ultrasonographic diagnosis of cirrhosis. Exclusion criteria: sepsis/acute infection, history of CAD/CHF/CKD, connective tissue disorder, steroid use in previous month, platelet transfusion prior to sampling, hepatocellular carcinoma.

Data collection: Demographic and clinical exam; laboratory investigations (CBC, LFTs, RFTs, coagulation profile, hepatitis panel). Bonacini score components: platelet count.

(categorical scoring per Bonacini method), AST/ALT ratio score, and INR score — summed to yield Bonacini total score. Liver stiffness measured by transient elastography (FibroScan®) on the same admission/visit.

Outcomes: Primary—association and predictive performance of Bonacini score with FibroScan liver stiffness in kPa (continuous). Secondary—mean FibroScan by Bonacini score groups.

Statistical analysis: Continuous variables described as mean ± SD or median (IQR) as appropriate. Correlation by Pearson (for continuous normally distributed) and Spearman (rank). Linear regression used to model FibroScan as dependent variable and Bonacini score as independent variable; results reported as coefficients, p-values and R². Group comparisons used ANOVA or Kruskal–Wallis as applicable. Two-sided p < 0.05 considered significant.

Analyses done in Python / statsmodels / scipy (details available on request).

Cohort: 143 patients included. Basic descriptive statistics:

Table 1. Descriptive statistics (N = 143)

Correlation:

• Pearson correlation between Bonacini score and FibroScan: r = 8006, p = 3.66 ×

10⁻³³.

• Spearman rank correlation: rho = 7644, p = 1.15 × 10⁻²⁸.

.

These indicate a strong positive correlation: higher Bonacini scores are associated with higher liver stiffness.

Linear regression: Predicting FibroScan (kPa) from Bonacini score:

• Regression equation: FibroScan = 7.303 (intercept) + 1.324 × Bonacini.
• Bonacini coefficient: β = 1.324, p < 0.000001.
• Model fit: R² = 0.641 (64.1% of variance in FibroScan explained by Bonacini score).

Interpretation: For each 1-point increase in Bonacini score, expected increase in FibroScan ~

1.32 kPa.

Table 2: FibroScan by Bonacini group (observed)

(See Table 2 — strong stepwise increase in mean FibroScan with increasing Bonacini score.)

Gender stratified: (summary) Both males and females showed the same positive relationship between Bonacini and FibroScan (detailed table available on request).

Note on thresholds: In this dataset, all patients had FibroScan values ≥ 12.5 kPa (range 12.5–23.4 kPa), i.e., values in the range typically considered advanced fibrosis / cirrhosis according to commonly used cut-offs (see Discussion and references). Because the cohort comprises clinically diagnosed cirrhosis patients, ROC analysis to differentiate low vs high fibrosis stages (e.g., using threshold 9.5 kPa) was not feasible here (lack of patients with low FibroScan values to form two groups). Instead the analysis focuses on continuous association and group means

Principal findings: The Bonacini score correlated strongly with transient elastography liver stiffness (r ≈ 0.80) in this cohort of clinically cirrhotic patients. The model shows that the Bonacini score explains ~64% of the variance in FibroScan values, a substantial proportion for a simple composite biochemical score.

Comparison with previous studies: The Bonacini discriminant score was originally developed and validated largely in HCV cohorts (Colli et al. and others) and shown to distinguish severe fibrosis/cirrhosis. Our results extend the utility of the score to a mixed- etiology cirrhotic cohort in a tertiary care Indian population, showing consistent stepwise increases in stiffness with rising Bonacini points.

Clinical relevance: In resource-limited settings where FibroScan or liver biopsy may not be available, the Bonacini score (readily derived from routine labs) can provide a reliable indication of fibrosis severity/cirrhosis. The approximate regression coefficient (~1.3 kPa per Bonacini point) provides a quantitative sense of expected change in stiffness with score increments.

FibroScan cutoffs (context): Published guideline and pooled analyses commonly use cutoffs such as ~9.5 kPa for advanced fibrosis (≥F3) and ~12.5 kPa for cirrhosis (F4), though thresholds vary by etiology and local calibration. Several expert documents and reviews provide these thresholds and caveats (EASL 2021; FibroScan reporting guidance; recent reviews). wp.uthscsa.edu+2EASL-The Home of Hepatology.+2

Limitations:

1. Single-centre cohort, all clinically diagnosed cirrhosis patients — range of fibrosis stages skewed to advanced disease (no low-stiffness controls). This precluded ROC analysis to assess diagnostic sensitivity/specificity across the full spectrum of
2. Liver biopsy (histology) — the reference standard — was not available for all patients; we compared against FibroScan (widely accepted non-invasive reference).
3. Potential selection bias (only patients referred for evaluation).
4. Results may not generalize to early-stage disease or to etiologies where FibroScan thresholds differ markedly (e.g., NAFLD, cholestatic disease).

Implications & future work: Prospective evaluation across the entire fibrosis spectrum (including patients with no/minimal fibrosis) and comparison with histology would be valuable. Also, combining Bonacini with other widely used biochemical scores (FIB-4, APRI) in sequential algorithms could improve discrimination and reduce need for elastography/biopsy.

The Bonacini score shows a strong and significant association with liver stiffness measured by FibroScan and explains a substantial proportion of variability in stiffness among clinically cirrhotic patients. It can be a practical, low-cost tool to help identify patients likely to have advanced fibrosis/cirrhosis, particularly in resource-limited settings — but further validation across the full fibrosis spectrum is recommended.

Conflict of interest: None

Funding: None.

1. Liguori A, et al. (Lancet Gastroenterology commentary on actionable FibroScan cutoffs). The Lancet Colli A, Fraquelli M, Andreoletti M, Marino B, Zuccoli E, Colombo Severe liver fibrosis or cirrhosis: accuracy of US for detection—analysis of 300 cases. Radiology. 2003;227(1):89-94. doi:10.1148/radiol.2271020374
2. Bonacini M, Hadi G, Govindarajan S, Lindsay Utility of a discriminant score for diagnosing advanced fibrosis or cirrhosis in patients with chronic hepatitis C virus infection. Am J Gastroenterol. 1997;92(8):1302-4. PMID: 9260793
3. Colli A, Colucci A, Paggi S, Fraquelli M, Conte D. Accuracy of a predictive model for severe hepatic fibrosis or cirrhosis in chronic hepatitis World J Gastroenterol. 2005;11(46):7318-22. doi:10.3748/wjg.v11.i46.7318
4. Gudowska-Sawczuk M, Gruszewska E, Panasiuk A, Cylwik B, Flisiak R, Chrostek Non-invasive markers of liver fibrosis in chronic hepatitis. Clin Exp Hepatol. 2016;2(1):12-20. doi:10.5114/ceh.2016.58963
5. Asrani SK, Devarbhavi H, Eaton J, Kamath Burden of liver diseases in the world. J Hepatol. 2019;70(1):151-71. doi:10.1016/j.jhep.2018.09.014

6. European Association for the Study of the Liver (EASL). EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update. J Hepatol. 2021;75(3):659-689. doi:10.1016/j.jhep.2021.05.025
7. Liguori A, Boursier J, Elshaarawy O, et Role of FibroScan in clinical practice: a review. Lancet Gastroenterol Hepatol. 2020;5(4):389-402. doi:10.1016/S2468- 1253(19)30419-0
8. Castera L, Forns X, Alberti A. Non-invasive evaluation of liver fibrosis using transient J Hepatol. 2008;48(5):835-47. doi:10.1016/j.jhep.2008.02.008
9. Rockey DC, Caldwell SH, Goodman ZD, Nelson RC, Smith Liver biopsy.Hepatology. 2009;49(3):1017-44. doi:10.1002/hep.22742

10. Shaheen AA, Myers Diagnostic accuracy of the aspartate aminotransferase–to– platelet ratio index for the prediction of hepatitis C–related fibrosis: a systematic review. Hepatology. 2007;46(3):912-21. doi:10.1002/hep.21835.