A prognostic model for development of significant liver fibrosis in HIV-hepatitis C co-infection.

PLoS One 2017 3;12(5):e0176282. Epub 2017 May 3.

Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.

Background: Liver fibrosis progresses rapidly in HIV-Hepatitis C virus (HCV) co-infected individuals partially due to heightened inflammation. Immune markers targeting stages of fibrogenesis could aid in prognosis of fibrosis.

Methods: A case-cohort study was nested in the prospective Canadian Co-infection Cohort (n = 1119). HCV RNA positive individuals without fibrosis, end-stage liver disease or chronic Hepatitis B at baseline (n = 679) were eligible. A random subcohort (n = 236) was selected from those eligible. Pro-fibrogenic markers and Interferon Lambda (IFNL) rs8099917 genotype were measured from first available sample in all fibrosis cases (APRI ≥ 1.5 during follow-up) and the subcohort. We used Cox proportional hazards and compared Model 1 (selected clinical predictors only) to Model 2 (Model 1 plus selected markers) for predicting 3-year risk of liver fibrosis using weighted Harrell's C and Net Reclassification Improvement indices.

Results: 113 individuals developed significant liver fibrosis over 1300 person-years (8.63 per 100 person-years 95% CI: 7.08, 10.60). Model 1 (age, sex, current alcohol use, HIV RNA, baseline APRI, HCV genotype) was nested in model 2, which also included IFNL genotype and IL-8, sICAM-1, RANTES, hsCRP, and sCD14. The C indexes (95% CI) for model 1 vs. model 2 were 0.720 (0.649, 0.791) and 0.756 (0.688, 0.825), respectively. Model 2 classified risk more appropriately (overall net reclassification improvement, p<0.05).

Conclusions: Including IFNL genotype and inflammatory markers IL-8, sICAM-1, RANTES, hs-CRP, and sCD14 enabled better prediction of the 3-year risk of significant liver fibrosis over clinical predictors alone. Whether this modest improvement in prediction justifies their additional cost requires further cost-benefit analyses.

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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176282PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5415136PMC
September 2017
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