Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

Background: Selecting the optimal combination of HIV drugs for an individual in resourcelimited settings is challenging because of the limited availability of drugs and genotyping.Objective: The evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa.Methods: Cases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load, details of failing and previous antiretroviral drugs, drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative’s (RDI’s) models used these data to predict the probability of a viral load < 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.Results: The models achieved accuracy (area under the receiver–operator  characteristic curve) of 0.72 when predicting response to therapy, which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.Conclusion: The predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI’s models have the potential to optimise treatment selection and reduce virological failure in different patient populations, without the use of a genotype

536Systematic Review and Meta-Analysis of Randomized Controlled Trials (RCTs) Comparing Initial Non-Nucleoside Reverse-Transcriptase Inhibitor (NNRTI)- versus Ritonavir Boosted Protease Inhibitor (PI/r)-based Anti-Retroviral Therapy (ART)

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Pre-ART Levels of Inflammation and Coagulation Markers Are Strong Predictors of Death in a South African Cohort with Advanced HIV Disease

BACKGROUND: Levels of high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and D-dimer predict mortality in HIV patients on antiretroviral therapy (ART) with relatively preserved CD4+ T cell counts. We hypothesized that elevated pre-ART levels of these markers among patients with advanced HIV would be associated with an increased risk of death following the initiation of ART. METHODS: Pre-ART plasma from patients with advanced HIV in South Africa was used to measure hsCRP, IL-6 and D-dimer. Using a nested case-control study design, the biomarkers were measured for 187 deaths and two controls matched on age, sex, clinical site, follow-up time and CD4+ cell counts. Odds ratios were estimated using conditional logistic regression. In addition, for a random sample of 100 patients, biomarkers were measured at baseline and 6 months following randomization to determine whether ART altered their levels. RESULTS: Median baseline biomarkers levels for cases and controls, respectively, were 11.25 vs. 3.6 mg/L for hsCRP, 1.41 vs. 0.98 mg/L for D-dimer, and 9.02 vs. 4.20 pg/mL for IL-6 (all p<0.0001). Adjusted odds ratios for the highest versus lowest quartile of baseline biomarker levels were 3.5 (95% CI: 1.9-6.7) for hsCRP, 2.6 (95%CI 1.4-4.9) for D-dimer, and 3.8 (95% CI: 1.8-7.8) for IL-6. These associations were stronger for deaths that occurred more proximal to the biomarker measurements. Levels of D-dimer and IL-6, but not hsCRP, were significantly lower at month 6 after commencing ART compared to baseline (p<0.0001). CONCLUSIONS: Among patients with advanced HIV disease, elevated pre-ART levels of hsCRP, IL-6 and D-dimer are strongly associated with early mortality after commencing ART. Elevated levels of inflammatory and coagulation biomarkers may identify patients who may benefit from aggressive clinical monitoring after commencing ART. Further investigation of strategies to reduce biomarkers of inflammation and coagulation in patients with advanced HIV disease is warranted. TRIAL REGISTRATION: Parent study: ClinicalTrials.gov NCT00342355

Baseline levels of hsCRP, IL-6 and D-dimer for Deaths and Matched Controls.

a<p>p-value obtained from a conditional logistic model with a single covariate corresponding to log₁₀ transformed biomarker level.</p

Risk of Death Associated with Baseline Biomarker Levels.

<p>Odds ratios are derived from separate conditional logistic regression models for each biomarker (two unadjusted univariate and two adjusted models for each biomarker, one with quartiles and one with continuous level of biomarker after log₁₀ transformation); adjusted models include HIV disease state, hemoglobin, platelet, aspartate aminotransferase, and white blood cell count (dichotomized at the median). Percentile cut-off points (IQR on log10 scale) are hsCRP: <1.80, 1.80–5.15, 5.15–20.05, ≥20.05 (0.36–1.31); D-dimer: <0.71, 0.71–1.11, 1.11–1.92, ≥1.92 (0.15–0.28); IL-6: <2.14, 2.14–4.92, 4.92–11.22, ≥11.22 (0.33–1.05).</p

Risk of Early Death Associated with Baseline Biomarker Levels.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024243#pone-0024243-t003" target="_blank">Table 3</a>, footnote.</p

Phidisa II Study Design and Flow Diagram for Case-Control Substudy.

<p>Phidisa II Study Design and Flow Diagram for Case-Control Substudy.</p

Median Baseline Characteristics of Deaths and Matched Controls.

a<p>p-value obtained from univariate conditional logistic model. NA = not applicable as characteristic was a matching factor.</p

Risk of Late Death Associated with Baseline Biomarker Levels.

<p>See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024243#pone-0024243-t003" target="_blank">Table 3</a>, footnote 1.</p