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Reevaluating cumulative HIV-1 viral load as a prognostic predictor: predicting opportunistic infection incidence and mortality in a Ugandan cohort

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dc.contributor.author Sempa, JB
dc.contributor.author Dushoff, J
dc.contributor.author Daniels, MJ
dc.contributor.author Castelnuovo, B
dc.contributor.author Kiragga, AN
dc.contributor.author Nieuwoudt, M
dc.contributor.author Bellan, SE
dc.date.accessioned 2018-06-13T13:16:12Z
dc.date.available 2018-06-13T13:16:12Z
dc.date.copyright 2016-04-06
dc.date.issued 2016-04-06
dc.identifier.citation Sempa, JB, Dushoff, J, Daniels, MJ, Castelnuovo, B, Kiragga, AN, Nieuwoudt, M & Bellan, SE 2016, 'Reevaluating cumulative HIV-1 viral load as a prognostic predictor: predicting opportunistic infection incidence and mortality in a Ugandan cohort', American Journal of Epidemiology, Vol. 184, No. 1, pp 67–77, Viewed online 13 June 2018, https://doi.org/10.1093/aje/kwv303 en_US
dc.identifier.issn 0002-9262
dc.identifier.uri https://academic.oup.com/aje/article/184/1/67/1739799
dc.identifier.uri http://hdl.handle.net/10907/2082
dc.description.abstract Recent studies have evaluated cumulative human immunodeficiency virus type 1 (HIV-1) viral load (cVL) for predicting disease outcomes, with discrepant results. We reviewed the disparate methodological approaches taken and evaluated the prognostic utility of cVL in a resource-limited setting. Using data on the Infectious Diseases Institute (Makerere University, Kampala, Uganda) cohort, who initiated antiretroviral therapy in 2004–2005 and were followed up for 9 years, we calculated patients' time-updated cVL by summing the area under their viral load curves on either a linear scale (cVL1) or a logarithmic scale (cVL2). Using Cox proportional hazards models, we evaluated both metrics as predictors of incident opportunistic infections and mortality. Among 489 patients analyzed, neither cVL measure was a statistically significant predictor of opportunistic infection risk. In contrast, cVL2 (but not cVL1) was a statistically significant predictor of mortality, with each log10 increase corresponding to a 1.63-fold (95% confidence interval: 1.02, 2.60) elevation in mortality risk when cVL2 was accumulated from baseline. However, whether cVL is predictive or not hinges on difficult choices surrounding the cVL metric and statistical model employed. Previous studies may have suffered from confounding bias due to their focus on cVL1, which strongly correlates with other variables. Further methodological development is needed to illuminate whether the inconsistent predictive utility of cVL arises from causal relationships or from statistical artifacts. en_US
dc.description.sponsorship National Research Foundation (South Africa) en_US
dc.format.extent pagination, illustrations, tables: ii, 11 p. : iII. (some col.). en_US
dc.format.medium PDF en_US
dc.language.iso en en_US
dc.publisher Oxford University Press (OUP) en_US
dc.subject Cox proportional hazards models en_US
dc.subject HIV en_US
dc.subject Human immunodeficiency virus en_US
dc.subject Martingale residuals en_US
dc.subject Mortality en_US
dc.subject Opportunistic infections en_US
dc.subject Viral load en_US
dc.subject Viremia copy-years en_US
dc.title Reevaluating cumulative HIV-1 viral load as a prognostic predictor: predicting opportunistic infection incidence and mortality in a Ugandan cohort en_US
dc.type Article en_US
dc.rights.holder Oxford University Press en_US


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