When Two Viruses Collide: Impact of HCV Infection on SARS-CoV-2 Disease Progression
Bibliographic record
Abstract
Introduction: The interaction between hepatitis C virus (HCV) infection and coronavirus disease 2019 (COVID-19) is little defined, despite the worldwide prevalence of both diseases. Chronic HCV is linked to immunological dysregulation and hepatic dysfunction, potentially worsening COVID-19 results. Moreover, the potential preventive impact of previous antiviral therapy against severe COVID-19 remains inadequately demonstrated. This study examined the correlation between HCV infection, antiviral therapy, and the severity of COVID-19 in an Egyptian cohort. Materials and Methods: We conducted a retrospective cohort study of 238 patients with RT-PCR–confirmed COVID-19 admitted to Badr University Hospital and Beni-Suef University Hospital between June 2021 and October 2022. Clinical data, comorbidities, liver function tests (AST, ALT, bilirubin, albumin), and HCV infection status along with antiviral treatment history were gathered. The severity of COVID-19 was categorized as mild/moderate or severe/critical. Multivariable logistic regression was conducted to ascertain independent predictors of severe/critical disease, controlling for age, sex, diabetes, and hypertension. The predictive accuracy was evaluated using ROC curve analysis. Results: In the cohort, 37 patients (15.5%) tested positive for HCV. Severe or critical COVID-19 was more prevalent in HCV-positive patients compared to HCV-negative patients (35.1% vs. 25.4%, p=0.03). Multivariate analysis revealed that HCV infection independently elevated the risks of severe COVID-19 (adjusted OR = 1.85, 95% CI: 1.05–3.30, p = 0.034). Age beyond 60 years was the most significant predictor (aOR = 2.60, 95% CI: 1.50–4.50, p < 0.001). Diabetes mellitus was a significant predictor of severity (aOR = 1.90, 95% CI: 1.10–3.20, p = 0.018), although hypertension did not reach statistical significance (aOR = 1.40, 95% CI: 0.90–2.10, p = 0.120). Prior antiviral medication in HCV patients significantly diminished the risk of severe illness by approximately 50% (aOR = 0.55, 95% CI: 0.30–0.95, p = 0.041). ROC analysis demonstrated exceptional prediction accuracy (AUC: 0.97 for logistic regression; 0.98 for random forest). Conclusion: HCV infection independently elevates the chance of severe COVID-19; however, previous antiviral treatment significantly mitigates this risk. Liver indicators indicate disease progression, highlighting the prognostic significance of hepatic involvement. These findings underscore the necessity of enhancing HCV eradication initiatives and prioritizing HCV-positive persons for prompt COVID-19 interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".