Plasma HIV RNA levels (viral load) are an integral clinical indicator for HIV care practice and research in the current era of combination antiretroviral therapy (cART) [1–4]. Early change in viral load levels following cART initiation can predict long-term treatment response [1,2], and HIV viral replication while on cART increases the risk of disease progression and mortality [5–8]. In clinical and epidemiologic surveillance studies, a recently measured viral load is commonly used to assess the prevalence of HIV virologic suppression, a marker of cART effectiveness and HIV transmission risk [9–11]. However, despite being an independent predictor of mortality risk , a single, recent viral load measure fails to capture an individual's long-term exposure to viral replication .
Interest in measures of cumulative HIV viral burden has increased in recent years with the demonstration of their added value as a prognostic marker of disease progression over single time-point viral loads. In particular, viremia copy-years (VCY), a measure of cumulative HIV viral burden, has shown stronger associations with mortality and morbidity compared with the most recent viral load and other single time-point viral load assessments [14–17]. Yet, the meaningful window of a patient's viral load history that should be included in VCY for optimal prognostic performance remains unclear. No studies have addressed whether a patient's entire viral load history is necessary to accurately predict mortality or morbidity risk or whether VCY measures over limited time periods of disease history may suffice in predicting these outcomes.
In the current study, we used data from a large, prospective cohort of cART-initiating HIV-positive MSM to assess VCY using alternative windows of viral load history, which vary in terms of both the temporal relationship with the outcome assessment and the duration of viral load accumulation. We evaluated the prognostic value for mortality risk of these abridged VCY measures and compared with VCY using the complete viral load history since cART initiation, as well as to three single time-point viral load measures.
The Multicenter AIDS Cohort Study (MACS), as previously described [18–20], is an ongoing prospective cohort study of the natural and treated history of HIV infection. Participants attend semi-annual research visits that include standardized interviews and collection of blood specimens. Local institutional review boards approved research protocols. All participants provided written informed consent.
As of March 2015, a total of 7343 MSM (3897 HIV-positive) were enrolled at four US sites (Baltimore/Washington, Chicago, Pittsburgh and Los Angeles). Of these, 1296 met the eligibility criteria: initiated cART after or 1 year or less prior to MACS enrollment between July 1995 and March 2015 (Supplementary Fig. 1, https://links.lww.com/QAD/B347). The analytic sample consisted of 841 men after excluding those with gaps of at least 2 years between the dates of last report of no cART use and first report of cART use (N = 111), one viral load or less after cART initiation (N = 196), cART treatment for more than 1 year before the first available viral load (N = 69) or no viral load 6 months or less prior to cART initiation (N = 79). cART was defined as any regimen of at least three antiretroviral agents that included at least one protease inhibitor or one nonnucleoside reverse transcriptase inhibitor or abacavir or tenofovir, or an integrase inhibitor or an entry inhibitor . Individuals were censored if they had no viral load measured for more than 1 year during the follow-up for this study. The date of cART initiation was abstracted from medical records or, when an exact date was not available, estimated as the midpoint between the last reported cART-naive date and first reported cART-treated date.
The outcome of interest was all-cause mortality. Mortality data were ascertained through a review of death certificates and use of National Death Index records.
Plasma HIV-1 RNA measures
One of three assays were used for measuring viral load: Amplicor HIV-1 monitor test [Roche Diagnostics, lower limit of detection (LLD) = 400 copies/ml, pre-2002: 11%]; Amplicor HIV-1 monitor ultrasensitive test (Roche Diagnostics, LLD = 50, 2002–2010: 64%); TaqMan HIV-1 test (Roche Diagnostics, LLD = 20, 2011–2015: 25%). Viral loads below the LLD were set to 300, 40 and 10 copies/ml, respectively, per MACS operating protocols. Longitudinal viral load data were measured at semiannual study visits. For cART-prevalent individuals, the last viral loads within 6 months before cART initiation were abstracted from medical records.
Viral suppression was defined as consistent viral load measurements below the LLD, allowing one blip less than 400 copies/ml. Using this definition, proportions of virally suppressed participants were calculated at single time-points (the last pre-cART, first post-cART and most recent visits) and over various time windows (as explained below).
Assessment of overall viremia copy-years
The overall VCY was approximated using the trapezoidal rule [14,16]. Briefly, we calculated the area-under-the-curve (AUC) of the segment between each two consecutive viral loads by taking the mean of the two viral loads (on the natural scale) multiplied by the in-between time interval. If viral load was missing at cART initiation, the last available value within the prior 6 months was used. Time-updated VCY was computed by summing the AUC of all segments prior to the current visit.
Assessment of viremia copy-years using abridged history
We created 20 VCY measures that estimated HIV viral burden during different post-cART time periods. The first 10 VCYs used viral loads measured in the 1–10 years immediately following cART initiation. The other 10 VCYs used viral loads measured in the most recent 1–10 years. Examples of four VCYs are illustrated in Fig. 1. All VCYs were treated as time-varying: for VCYs following cART initiation, the values were updated up to the end of the targeted exposure window and projected forward using the last available value; for the most recent VCYs, the values were updated as more post-cART history was accumulated and the window of most recent history moved relative to the time of the outcome assessment. If the length of the targeted exposure window exceeded the total post-cART follow-up time, all available viral loads during the post-cART period were used in the calculation of VCYs. An example is illustrated in Fig. 1, in which the total follow-up time at visit 2 was less than 1 year, so only the first three viral loads were used to calculate VCY such that all four VCYs had the same value. Missing viral loads at the beginning or end of a time period were replaced with a value proportionally interpolated using the two viral loads obtained closest in time to the missing measurement .
To evaluate the association of the various VCY measures with all-cause mortality, we conducted survival analysis with time accrued from cART initiation (considered baseline for the study). Because MACS participants were most often not observed on the exact date of cART initiation, all observations were treated as late entries with entry time defined by date of first post-cART viral load assessment. VCYs were log10-transformed and treated as a continuous exposure in analyses. We fit separate conventional lognormal survival models with cluster-robust variance for each VCY measure under the assumption that survival times were proportional  and estimated the percentage change in survival times for each 10-fold increase in VCYs. To account for other differences in participant characteristics that could affect both VCYs and mortality, models were adjusted for age (per 10 years), race (white vs. non-white), MACS study site, cohort enrollment (pre-2001 vs. post-2001), most recent CD4+ cell count (per 100 cells/μl with a spline at 200), baseline CD4+ cell count (per 100 cells/μl) and history of clinically defined AIDS diagnosis (Model A). To evaluate the degree to which VCYs provided additional prognostic information beyond that provided by selected specific single time-point viral loads, we added the last pre-cART viral load, first post-cART viral load and most recent viral load jointly to Model A (Model B). The Akaike information criteria (AIC) statistic was calculated to compare model fit. We further stratified analyses by baseline CD4+ cell levels to evaluate whether pre-cART immunodeficiency (<200 cells/μl) affected the prognostic performance of VCYs . Statistical analyses were performed using Stata/SE version 13.1 (StataCorp, College Station, Texas, USA) and R version 3.3.2 (The R Foundation for Statistical Computing, Vienna, Austria). P values less than 0.05 guided statistical interpretation. To facilitate future research and clinical activities, we provide the programing codes that allow the computation of VCYs during various time periods on the MACS website (https://statepi.jhsph.edu/software/software.html).
To evaluate the sensitivity to the handling of undetectable viral load, we repeated the analyses with VCYs calculated after substituting values of 0 or 10 (half of the TaqMan assay LLD) for undetectable viral loads. We then repeated analysis in a subset of participants with no prior exposure to suboptimal regimens, to enhance the generalizability of our results to treatment-naive patients currently seen in clinical care. We also conducted an exploratory analysis for cause-specific mortality by limiting to AIDS-related, and then non-AIDS-related mortality events, while censoring the other competing event.
Table 1 summarizes participant characteristics. Among the 841 cART initiators, 22% were black; 52% initiated cART prior to 2001. At cART initiation, the median age and CD4+ cell count were 43 years and 319 cells/μl; 10% had a clinically defined AIDS diagnosis; 48% had prior exposure to antiretroviral mono-therapy or dual-therapy.
Seventy-four deaths (58% attributed to AIDS) occurred over a median [interquartile range (IQR)] follow-up of 5 (2–12) years (6736 person-years). The median (IQR) time to death was 5 (2–8) years. Among those who died, the median baseline CD4+ cell count was 191 cells/μl; 22% had an AIDS diagnosis.
Table 2 shows the distributions of VCY and viral load measures and the proportions of individuals who achieved viral suppression during various time periods following cART initiation. At cART initiation, the median log10 (viral load) was 4.5 (34 438 copies/ml). This had decreased to 1.9 (81 copies/ml) at the first post-cART visit. The median overall log10 (VCY) was 4.3 (17 944 copy-years/ml). At the most recent visit, 72% of men were virally suppressed. However, when considering all viral loads obtained in the most recent 2 and 3 years, the proportion of men with viral suppression decreased to 61 and 55%, respectively (Table 2). Only 33% maintained viral suppression during the entire study follow-up.
Supplementary Fig. 2, https://links.lww.com/QAD/B347 shows a Spearman's rank correlation matrix for the 21 VCYs and three viral loads. To summarize, the overall VCY correlated strongly with the 10 VCYs from cART initiation (0.76–0.99) but to a lesser extent with the 10 recent VCYs (0.31–0.79). Within the 10 recent VCYs, variation started to be observed when the time windows used in VCY calculation differed by at least 4 years (0.71–0.86).
Evaluating various time frames of viremia copy-years assessment for predicting mortality
Table 3 shows the associations of mortality risk with three single time-point viral loads, the overall VCY and the 20 abridged VCYs. After adjusting for demographic covariates and the baseline and most recent CD4+ cell count, each 10-fold increase in the most recent viral load and the first post-cART viral load was significantly associated with 16 and 14% decrease in survival time, respectively. The overall VCY was associated with a comparable magnitude of decrease in survival time (18%), but this was not statistically significant (Model A).
Among the 20 abridged VCYs, only the 10 measures using recent viral load history were significantly associated with mortality independent of CD4+ cell count (Model A). For each 10-fold increase in these recent VCYs, the decrease in survival times varied between 17 and 26%, with the strongest prognostic value observed for VCYs based on viral loads obtained during the most recent 3–8 years (23–26%). In contrast, the 10 VCYs based on viral loads in the first 1–10 years following cART initiation were not significantly associated with mortality (Model A).
When each of the 21 VCYs was evaluated in a combined viral load model with the three single time-point viral loads (Model B), each 10-fold increase in VCYs based on viral loads in the most recent 3–8 years remained associated with a 20–25% decrease in survival time. By contrast, the overall VCY and the VCYs from period following cART initiation were not predictive of mortality. None of the three single time-point viral loads was significantly associated with mortality when they were evaluated in the same model with the most recent VCYs (Supplementary Table 1, https://links.lww.com/QAD/B347). Assessments of model fit (AIC) indicated better fits to the data using the recent VCYs measures; the model with VCY using the most recent 3-year period alone had the lowest AIC value (Table 3).
Mortality by baseline CD4+ cell levels
Table 4 displays the results of analyses stratified by baseline CD4+ cell count. The follow-up time was comparable in the two CD4+ groups (P = 0.69). Men with less than 200 cells/μl (27%) accounted for 1793 person-years and 39 deaths. Among all VCY and single time-point viral load measures, the VCY based on viral loads in the most recent 3 years was the only one that was independently associated with mortality risk in both baseline CD4+ groups. Among men with CD4+ cell count at least 200 cells/μl, a stronger association with mortality was generally observed for VCYs based on viral loads that were accumulated over longer time periods.
Substituting undetectable viral loads with values of 0 or 10 had minimal impact on the observed association between the overall VCY and mortality risk (Supplementary Table 2, https://links.lww.com/QAD/B347). When limited to the 432 treatment-naive participants (18 deaths; 2549 person-years), survival times were significantly shortened by 22–31% per 10-fold increase in VCY based on viral loads in the recent 3–8 years – results similar to the main analysis (Supplementary Table 3, https://links.lww.com/QAD/B347). When stratified by the type of mortality events, VCYs seemed to have better prognostic value for AIDS-related over non-AIDS-related deaths. However, the difference in prognostic power appeared to diminish when longer recent viral load history was used in VCY calculation (Supplementary Table 4, https://links.lww.com/QAD/B347).
To our knowledge, this is the first study that has systemically evaluated the mortality prognostic value of VCYs derived from varying windows of viral load history. We showed that all 10 recent VCYs were significantly associated with mortality after adjusting for the baseline and most recent CD4+ cell count. In addition, after taking into account single time-point viral loads, VCYs using viral loads measured during the most recent 3–8 years remained significantly predictive of mortality risk. By contrast, the overall VCY since cART initiation, or VCYs based on viral loads measured immediately following cART initiation, were not independent predictors of mortality risk. After stratifying by baseline CD4+ cell count (as a proxy for pre-cART immunodeficiency), only the VCY based on viral loads in the most recent 3 years showed consistent associations with mortality. These findings have potentially important implications for both HIV research and care practice.
A number of recent observational studies examined the prognostic values of VCY measures. Although some did not show improved prognostic performance of VCY over that of the most recent viral load [23–25], others have demonstrated VCY as an independent predictor for AIDS and non-AIDS clinical events, including mortality [15,16,21,26–30]. Although there's a growing interest in the potential use of VCY in clinical research, it is unclear whether knowing the entire viral load history provides improved prognosis for mortality risk that could justify the greater challenge of assessing VCY. We demonstrated that among cART-treated individuals, recent trends in viral load seem to be more predictive of mortality risk compared with high viral load occurring earlier in a patient's history. These findings highlight the importance of recent viral load history in mortality risk prognosis among cART-treated individuals. The lack of independent relevance of temporally distant viral loads may be explained by the fact that treatment-induced viral suppression can partially reverse some of the pathological consequences of HIV infection, such as opportunistic infections and high levels of inflammatory markers [31–34].
However, despite the apparent relevance of recent viral loads, a single viral load assessment at the most recent time point can still lead to misclassification of HIV viral burden . In our study, the proportion of virally suppressed participants decreased from 72% on the most recent visit to 55% over the past 3 years, indicating that a single most recent viral load underestimated viral nonsuppression in the past 3 years by approximately 17%. Furthermore, when considering the viral load history in these past 3 years, VCY showed a stronger association with mortality than the most recent viral load (24 vs. 16% reduced survival time per 10-fold increase), and this association remained significant after accounting for the last pre-cART, first post-cART and most recent viral loads. These data support the notion that VCYs based on recent viral loads are better than a single viral load assessment at capturing recent viral nonsuppression as a result of virologic failure or treatment nonadherence, which may have important clinical implications for the development of adverse outcomes such as mortality.
We also observed that the mortality prognostic value of VCY measures may vary by the degree of immunological suppression at cART initiation – an important consideration when assessing VCYs. Although almost all recent VCYs were strongly associated with mortality risk in individuals with baseline CD4+ cell levels at least 200 cells/μl, only VCYs based upon viral loads over the most recent 1–3 years significantly predicted death in individuals with less than 200 cells/μl. This observation may be explained by differences in clinical disease pathogenesis in individuals with varying degrees of immunodeficiency. Although prolonged viremic periods with attendant increases in systemic inflammation and immune activation may be a driving force behind mortality risk regardless of the baseline CD4+ cell counts [35,36], pre-cART immunodeficiency can represent a primary risk for AIDS-related deaths [37,38]. Thus, the prognostic value of VCY measures, which may serve as a proxy of elevated levels of chronic inflammation and immune activation, can be diminished in patients with more advanced immunodeficiency, although we cannot rule out the possibility of unstable estimation due to smaller sample sizes in the stratified analysis. Apart from the degree of immunologic suppression, we demonstrated that the prognostic value of recent VCYs is robust to prior exposure to suboptimal mono or dual antiretroviral therapy and changing assumptions for handling unobserved or undetectable viral loads. Accumulating viral loads on the natural or log scale may also affect the prognostic value of VCYs . In well treated HIV-positive individuals, high viral load values as a result of virologic failure have important implications for mortality risk as compared with low detectable viral loads [1,39]. Therefore, calculating VCYs from viral loads on the natural scale, which assigns greater weight to high viral load values, is appropriate in this study setting.
Plasma HIV RNA levels are important indicators of treatment response and can inform clinical decision-making in the management of HIV-positive patients [1,12]. Although there is no consensus on the routine use of VCY measures in HIV care practice, our findings serve as a proof of concept that such measures may be feasible in clinical settings and may improve clinicians’ ability to identify patients who are at increased risk of clinical progression. Better discrimination of high-risk patients can facilitate the development of individualized disease monitoring and treatment strategies. For maximal clinical utility of VCY measures based on recent viral load data, future work should validate their predictive accuracy and precision for individual mortality risk classification . In addition, further effort should be dedicated to formally assess the prognostic power of VCY measures for cause-specific mortality. In our exploratory analysis, VCY measures in general appeared to have better prognostic performance for AIDS-related mortality. However, this difference in prognostic power seemed to be diminishing for recent VCYs based on longer recent viral load history, suggesting that recent VCYs might be a reliable clinical predictor for both types of mortality.
The current study benefited from the extensive participant follow-up and careful ascertainment of mortality, as well as longitudinal viral load data, allowing us to assess VCY over various time periods. We focused on cART-treated participants, as routine viral load assessments often only occur after entry into HIV care and cART initiation . In prior studies evaluating VCY as a primary exposure, Cox proportional hazards regression models have been the standard method of analysis [14,16,17]. However, we found that relative hazards were not proportional throughout follow-up. A possible violation of this key assumption would lead to model misspecification and its inferential consequences. Here we used the lognormal accelerated failure time models to avoid proportionality assumptions.
The current study has several limitations. First, as in all observational studies, we cannot infer causality as there may be uncontrolled confounders such as behavioral factors that could lead to both poor cART adherence and higher mortality risk. Second, due to the inconsistent association between VCY and mortality across studies published to date, the generalizability of our results to all HIV-positive persons needs to be confirmed in larger studies that include participants more representative of the overall HIV-positive population. Third, we used a simple deterministic method to handle viral loads below the limit of detection, which will underrepresent variability in low-level viremia. This could have influenced results, as low-level viremia may represent an important mechanism of the persistent immune active state among suppressed HIV-positive populations . Finally, larger studies with longer follow-up are warranted to assess the prognostic performance of VCY measures among individuals with baseline CD4+ cell count more than 500 cells/μl and also to discriminate between VCYs based on viral load history longer than 5 years due to the strong positive correlations between these VCY measures.
In summary, our results suggest that, among cART-initiating HIV-positive men, VCY based upon viral load information in the recent 3 years predicts mortality risk better than single time-point viral loads and the overall VCY and is less influenced by the degree of immunological suppression. Therefore, it may represent a strong indicator of mortality risk that can be feasibly calculated in the analysis of observational data and in care management of HIV-positive patients. A clinical online tool to calculate VCY automatically from medical records could be developed or potentially integrated it into existing mortality risk scoring systems, such as the Veterans Aging Cohort Study index , to facilitate use in clinical practice.
Data in this article were collected by the Multicenter AIDS Cohort Study (MACS) with centers at Baltimore (U01-AI35042): The Johns Hopkins University Bloomberg School of Public Health: J.B.M. (PI), Todd Brown (PI), Jay Bream, Adrian Dobs, Michelle Estrella, W. David Hardy, Lisette Johnson-Hill, Sean Leng, Anne Monroe, Cynthia Munro, Michael W. Plankey, Wendy Post, Ned Sacktor, Jennifer Schrack, Chloe Thio; Chicago (U01-AI35039): Feinberg School of Medicine, Northwestern University and Cook County Bureau of Health Services: Steven M. Wolinsky (PI), Sheila Badri, Dana Gabuzda, F.J.P., Sudhir Penugonda, John P. Phair, Susheel Reddy, Matthew Stephens, Linda Teplin; Los Angeles (U01-AI35040): University of California, UCLA Schools of Public Health and Medicine: Roger Detels (PI), O.M.-M. (PI), Otto Yang (Co-PI), Peter Anton, Robert Bolan, Elizabeth Breen, Anthony Butch, Shehnaz Hussain, Beth Jamieson, John Oishi, Harry Vinters, Dorothy Wiley, Mallory Witt, Stephen Young, Zuo Feng Zhang; Pittsburgh (U01-AI35041): University of Pittsburgh, Graduate School of Public Health: Charles R. Rinaldo (PI), Lawrence A. Kingsley (PI), Jeremy J. Martinson (PI), James T. Becker, Phalguni Gupta, Kenneth Ho, Susan Koletar, John W. Mellors, Anthony J. Silvestre, Ronald D. Stall; Data Coordinating Center (UM1-AI35043): The Johns Hopkins University Bloomberg School of Public Health: L.P.J. (PI), Gypsyamber D'Souza (PI), A.G.A, Keri Althoff, Michael Collaco, Priya Duggal, S.A.H., Eithne Keelaghan, Heather McKay, Alvaro Muñoz, Derek Ng, Anne Rostich, Eric C. Seaberg, Sol Su, Pamela Surkan, Nicholas Wada. Institute of Allergy and Infectious Diseases: Robin E. Huebner; National Cancer Institute: Geraldina Dominguez. The MACS is funded primarily by the National Institute of Allergy and Infectious Diseases (NIAID), with additional cofunding from the National Cancer Institute (NCI), the National Institute on Drug Abuse (NIDA) and the National Institute of Mental Health (NIMH). Targeted supplemental funding for specific projects was also provided by the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Deafness and Communication Disorders (NIDCD). MACS data collection is also supported by UL1-TR001079 (JHU ICTR) from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH), Johns Hopkins ICTR or NCATS. The MACS website is located at http://aidscohortstudy.org/.
R.W., S.A.H. and A.G.A. contributed to the analysis and composition of the article. R.W. and A.G.A. were responsible for the design and conduct of the study. S.A.H., F.J.P., M.J.M., J.B.M., B.J.M., O.M.-M., L.P.J. and A.G.A. provided critical intellectual comments on revisions of the article.
This work was supported by grants U01-AI35039, U01-AI35040, U01-AI35041, U01-AI35042, UM1-AI35043 from the National Institute of Allergy and Infectious Diseases (NIAID), with additional cofunding from the National Cancer Institute (NCI), the National Institute on Drug Abuse (NIDA) and the National Institute of Mental Health (NIMH). Targeted supplemental funding for specific projects in the MACS was provided by the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Deafness and Communication Disorders (NIDCD). MACS data collection is also supported by UL1-TR001079 (JHU ICTR) from the National Center for Advancing Translational Sciences (NCATS) a component of the National Institutes of Health (NIH).
Conflicts of interest
There are no conflicts of interest.
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