A Multi-Biomarker Prediction Model Integrating Genetics, Hematological Indices, and Fibrosis Stage to Forecast Non-Response in HCV Genotype 4

Introduction: The Hepatitis C virus (HCV) genotype 4 is very widespread in Egypt, leading to considerable rates of cirrhosis, hepatocellular cancer, and death. Notwithstanding the efficacy of direct-acting antivirals (DAAs) in attaining cure rates over 90%, instances of treatment failure persist. Identifying predictors of non-response is essential for optimizing first-line therapy and advancing elimination objectives. Materials and Methods: A total of 143 patients with chronic HCV genotype 4 were enrolled, including 73 responders and 70 non-responders, along with 48 healthy controls. Baseline demographic, hematological, biochemical, and genetic data (rs2302254 and rs16949649 polymorphisms) were collected. Logistic regression and random forest models were applied to assess predictors of treatment non-response. Model performance was evaluated using ROC-AUC, PR-AUC. Results: Non-responders were markedly older (59.7 ± 5.7 years vs. 50.6 ± 8.5 years, p < 0.0001) and exhibited more severe fibrosis (median stage 5 vs. 3, p < 0.0001). Independent predictors of non-response were advanced fibrosis (OR 19.53, 95% CI 8.75–27.65, p < 0.001), low haemoglobin (OR 0.72, 95% CI 0.42–0.98, p = 0.041), decreased albumin (OR 2.44, 95% CI 1.45–4.14, p = 0.002), and increased AFP (OR 0.86, 95% CI 0.59–0.97, p = 0.049). The genetic polymorphisms rs2302254 (OR 1.19, 95% CI 0.57–2.43) and rs16949649 (OR 0.92, 95% CI 0.58–1.75) exhibited no significant correlation with treatment response. The integrated multi-biomarker logistic regression model attained a ROC-AUC of 0.968, a PR-AUC of 0.926, and a Brier score of 0.061, whilst the random forest classifier earned a ROC-AUC of 0.975 and a PR-AUC of 0.973. Conclusion: This is the first integrated multi-biomarker model created for Egyptian patients with HCV genotype 4 in the age of direct-acting antivirals. The fibrosis stage was the most significant predictor, supported by hemoglobin, albumin, and AFP, whereas the evaluated SNPs provided no further benefit. The model has exceptional predictive accuracy utilizing standard clinical and laboratory data, offering a scalable instrument for risk stratification and precision treatment methods in Egypt's HCV elimination initiatives.