Comparison of Aspartate Aminotransferase Platelet Ratio Index (APRI) to Neutrophil Lymphocyte Ratio (NLR) and Platelet Lymphocyte Ratio (PLR) with Fibroscan as a Non-Invasive Marker for Predicting Liver Fibrosis in Chronic Hepatitis C
DOI:
https://doi.org/10.70749/ijbr.v2i02.2812Keywords:
APRI, NLR, PLR, Fibroscan, Chronic Hepatitis CAbstract
Background: Chronic hepatitis C (CHC) is the leading cause of liver fibrosis, cirrhosis, and hepatocellular carcinoma worldwide. In resource-constrained settings, non-invasive serum markers such as the AST-to-platelet ratio index (APRI), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) provide viable alternatives to biopsy. Objective: To compare the diagnostic accuracy of APRI, NLR, and PLR to FibroScan for staging liver fibrosis in CHC patients at a tertiary care center in Karachi, Pakistan. Methods: This cross-sectional study (Dec 2024-May 2025) included 355 adult CHC patients. APRI, NLR, and PLR were calculated from routine blood counts, while fibrosis staging (F0-F4) was determined using transient elastography. Youden's index defined the optimal cut-offs for ROC curves, which were used to assess performance. Confounding was addressed using multivariate logistic regression. Subgroup analyses assessed marker performance based on age, gender, and ALT level. Results: The average age was 42 ± 10 years, with 60% being male. Distribution: F0-F1 42%, F2 28%, F3 17%, and F4 13%. APRI achieved AUC 0.79 (95% CI 0.74-0.84), NLR 0.74 (0.68-0.79), and PLR 0.71 (0.65-0.77). Optimal cut-offs are APRI ≥ 1.0 (sens 78%, spec 73%), NLR ≥ 2.1 (sens 70%, spec 68%), and PLR ≥ 120 (sens 65%, spec 66%). Combining APRI and NLR improved AUC to 0.83. In patients over 50 years, the APRI AUC increased to 0.82. APRI ≥ 1.0 is an independent predictor of significant fibrosis (OR 5.2; 95% CI 3.1-8.7; p < 0.001). Conclusions: APRI is the most reliable non-invasive marker for liver fibrosis in CHC, and combining it with NLR improves accuracy even further. These markers can help with fibrosis assessment and treatment prioritization in low-resource settings.
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