BackgroundLevels 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.MethodsPre-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.ResultsMedian 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).ConclusionsAmong 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 RegistrationParent Study: ClinicalTrials.gov NCT00342355
Female sex and increased BMI were associated with severe LA in this large randomized trial of first-line ARV in South Africa. While female sex, increased BMI and d4T are previously described risk factors for the development of clinically significant lactate elevations, the independent risk associated with EFV is a novel observation warranting further investigation.
BackgroundShort-term morbidity and mortality rates for HIV positive soldiers in the South African National Defence Force (SANDF) would inform decisions about deployment and HIV disease management. Risks were determined according to the latest CD4+ cell count and use of antiretroviral therapy (ART) for HIV positive individuals in the SANDF and their dependents.Methods and FindingsA total of 7,114 participants were enrolled and followed for mortality over a median of 4.7 years (IQR: 1.9, 7.1 years). For a planned subset (5,976), progression of disease (POD) and grade 4, potentially life-threatening events were also ascertained. CD4+ count and viral load were measured every 3 to 6 months. Poisson regression was used to compare event rates by latest CD4+ count (<50, 50–99, 100–199, 200–349, 350–499, 500+) with a focus on upper three strata, and to estimate relative risks (RRs) (ART/no ART). Median entry CD4+ was 207 cells/mm3. During follow-up over 70% were prescribed ART. Over follow-up 1,226 participants died; rates ranged from 57.6 (< 50 cells) to 0.8 (500+ cells) per 100 person years (py). Compared to those with latest CD4+ 200–349 (2.2/100py), death rates were significantly lower (p<0.001), as expected, for those with 350–499 (0.9/100py) and with 500+ cells (0.8/100py). The composite outcome of death, POD or grade 4 events occurred in 2,302 participants (4,045 events); rates were similar in higher CD4+ count strata (9.4 for 350–499 and 7.9 for 500+ cells) and lower than those with counts 200–349 cells (13.5) (p<0.001). For those with latest CD4+ 350+ cells, 63% of the composite outcomes (680 of 1,074) were grade 4 events.ConclusionRates of morbidity and mortality are lowest among those with CD4+ count of 350 or higher and rates do not differ for those with counts of 350–499 versus 500+ cells. Grade 4 events are the predominant morbidity for participants with CD4+ counts of 350+ cells.
BackgroundSelecting the optimal combination of HIV drugs for an individual in resource-limited settings is challenging because of the limited availability of drugs and genotyping.ObjectiveThe 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.MethodsCases 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.ResultsThe 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.ConclusionThe 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.
These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings.
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