We then combined data sets. Risk quintiles were generated for the HIV biomarker and the combined models. JNK activity Each Poisson model (HIV, ‘non-HIV’, and combined) was used to generate a risk estimate for each subject. Using each set of model estimates in turn, subjects were ranked from highest to lowest risk and grouped into five quintiles designated by equal numbers of mortality events to ensure similar power to detect differences in risk. Observed mortality rates and 95% CIs were estimated. To determine the effect of differing survival intervals on its discrimination, we reran the Index in both development and validation samples censoring survival follow-up at 30 days, 6 months,

1 year, 2 years, 4 years, and 6 years in development and validation selleck chemicals llc samples. For each model, we calculated a C statistic and compared this with published C statistics (receiver operator characteristic area estimates) for two commonly used prognostic indices, Acute Physiology and Chronic Health Evaluation (APACHE) [36] and The Charlson Comorbidity Index [37]. We fitted a logistic model predicting missing data (0 if no data missing and 1 if at least one variable missing) and including all variables (HIV, ‘non-HIV’, substance abuse or dependence, age, mortality, and year of cART initiation). We used predictions from this model to inversely weigh observations in the development and validation sets and compared

these results with those of the complete case analyses. Of 13 586 HIV-infected veterans initiating cART between 1 January 1997 and 1 August 2002 with laboratory data, 9784 (72%) had complete data (analytic sample). Development and validation sets were clinically similar. Subjects were middle-aged (Table 1; median age 45 years), mainly male (98%), and predominantly black (51%). Over a third had CD4 counts below 200 cells/μL and 18% had HIV RNA above 5 log copies/mL. Diagnoses of alcohol or drug abuse or dependence were

common (31%), as were anaemia (21%), HBV infection (12%), and HCV infection (43%). Twelve per cent had likely liver fibrosis (FIB 4>3.25). Rutecarpine Two per cent had stage IV renal failure (eGFR<30 mL/min). AIDS diagnoses were relatively uncommon. In pairwise comparisons, CD4 cell count, HIV RNA and AIDS-defining illnesses were strongly associated with haemoglobin, FIB 4, and eGFR <30 mL/min (P<0.0001 for each; data not otherwise shown). In development and validation sets, HIV and ‘non-HIV’ biomarkers were associated with mortality when modelled separately (Table 2). In both sets, ‘non-HIV’ biomarkers, as a group, added discrimination to the HIV model when combined into a single index [C statistic improved from 0.68 to 0.72 in development (P<0.0001) and from 0.71 to 0.77 in validation (P<0.0001)]. In all cases, all biomarkers retained independent associations with mortality after full adjustment. When data sets were combined, and quintiles of risk estimated, the combined index offered improved differentiation of mortality (Fig. 1).