Methods: CHC patients from 1997 to 2012 were included and randomised into a training and validation set (2:1 ratio). Clinical outcomes were determined using population based

data-linkage system. Hyaluronic acid (HA), bilirubin, GGT, α2-macroglobulin, ALT, AST, platelet count, prothrombin time, INR, ALP, creatinine and albumin results were available at entry into the study. The models were developed using cox regression analysis. Results: 617 patients were included: 411 in the training set and 206 in the validation set. Mean follow up was 6yr (range 0.1-14) during which 22 LRD, 23 HCC and 27 LD were observed. Using the training set albumin, GGT, HA, age and sex were chosen in the final model C646 purchase to predict 10, 5 and 3yr LRD with AUROC of 0.95 this website (95% CI, 0.91-0.99), 0.95 (95%CI, 0.9-1) and 0.96 (95% CI, 0.91-1) respectively. A cut point of 32.5 had a sensitivity of 80% and specificity of 97% to predict 3yr LRD. A cut point of 31 had a sensitivity of 93% and specificity

of 85% to predict 10yr LRD. Using these two cut points, patients were categorised into 3 risk groups with an annual incidence rate for LRD of 0.1% (95%CI, 0.04-0.2%), 2% (95%CI, 0.3-3.8%) and 13.2% (95%CI, 4.1-22.3%) respectively (p<0.001). Albumin, GGT, HA, age and sex were used to predict 10, 5 and 3yr LD with AUROC of 0.89 (95%CI, 0.8-0.98), 0.9 (95%CI, 0.8-1) and 0.96 (95%CI, 0.93-0.99) respectively. A cut point of 33.5 achieved a sensitivity of 94% and a specificity of 84% to predict 5yr

LD. Using this cut point patients were divided into two risk groups with an annual incidence rate for LD of 0.2% (95%CI, 0.02-0.3%) and 5.8% (95%CI, 2.5-9.1%) respectively (p<0.001). ALP, α2-macroglobulin, age and sex were chosen to predict 10, 5 MCE公司 and 3yr HCC occurrence with AUROC of 0.93 (95%CI, 0.89-0.98), 0.95 (95%CI, 0.91-0.99) and 0.94 (95%CI, 0.90-0.99) respectively. A cut point of 12 had a sensitivity of 90% and specificity of 88% to predict 5yr HCC occurrence. Using this cut point patients were divided into two risk groups with an annual incidence rate for HCC of 0.2% (95%CI, 0.02-0.3%) and 5.6% (95%CI, 3-8.2%) respectively (p<0.001). Similar results were obtained using the validation set. Conclusion: All three simple models had excellent predictive accuracy and were able to stratify risk into clinical meaningful categories. Disclosures: Enrico Rossi – Patent Held/Filed, UNIVERSITY OF WA Gary P. Jeffrey – Advisory Committees or Review Panels: MSD, Novartis The following people have nothing to disclose: Yi Huang, Leon Adams, Gerry C. MacQuillan, Max K. Bulsara Background and Aims: Although HCV-RNA levels are predictive of spontaneous and treatment-induced HCV clearance, factors associated with HCV-RNA levels during early infection remain poorly understood.