ART-CC is a carefully validated prognostic model based upon data from cohorts in Europe and North America [3,13,32]. It is focused on markers of HIV disease severity

and includes CD4 count (<50, 50–99, 100–199, 200–349 and ≥350 cells/μL), HIV-1 RNA of five log or more and the presence of AIDS-defining illness. For ‘non-HIV’ biomarkers we considered only: (1) clinical markers that are ordered as part of routine clinical management and (2) markers that have been previously demonstrated to be associated with mortality among patients with HIV infection. We employed previously validated specifications of these markers consistent with major organ system injury. For liver injury, we employed the Fibrosis Index (FIB) 4 [33]. FIB 4 uses aspartate and alanine transaminase (AST and ALT, respectively), Inhibitor Library cell line platelets and age to estimate likely liver fibrosis [FIB 4: (years of age × AST)/(platelets in 109/L × square root of ALT)]. Two thresholds of FIB 4 are recommended: >3.25, consistent with high risk for fibrosis/cirrhosis; and <1.45, consistent with low risk for fibrosis/cirrhosis. For renal injury, we employed the Modified Diet in Renal Disease (MDRD) estimation which uses age, race, gender and creatinine to estimate creatinine clearance [estimated Glomerular Filtration Rate (eGFR):

186.3 × (serum creatinine−1.154) × (age−0.203) × (0.742 for women) × (1.21 if African American)] [34]. Two levels of anaemia were defined: moderate and severe SP600125 chemical structure (haemoglobin 10-12 and <10 g/dL, respectively). Finally, we included a combined indicator variable for chronic hepatitis B virus (HBV) or hepatitis C virus (HCV) infection. We created a single indicator because 51% of those with chronic HBV infection also had HCV infection, and coefficients for HBV and HCV infections were similar in preliminary models. The Tolmetin ART-CC model also adjusts for two demographic factors: age ≥50 years and history of injecting drug use. Because our sample is older [3,13], we adjusted both models for age 50–64 and ≥65 years.

We did not have information available in Virtual Cohort on injecting drug use. As a proxy, we adjusted both models for a diagnosis of substance (drug or alcohol) abuse or dependence. We created a single indicator for substance abuse or dependence because 67% of those with a diagnosis of drug abuse or dependence also had a diagnosis of alcohol abuse or dependence [35] and coefficients in preliminary models were similar. Proportions were compared using the χ2 test. Medians were compared using the rank-sum test. Discriminations were compared using C statistics. The C statistic can be interpreted as the probability that any random pair of uncensored subjects in the data will be ranked correctly by the index with respect to their risk of mortality.