Frequency of post treatment control varies by antiretroviral therapy restart and viral load criteria : AIDS

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Research Letter

Frequency of post treatment control varies by antiretroviral therapy restart and viral load criteria

Fajnzylber, Jessea; Sharaf, Radwaa; Hutchinson, John N.b; Aga, Evgeniab; Bosch, Ronald J.b; Hartogensis, Wendyc; Jacobson, Jeffrey M.d; Connick, Elizabethe; Volberding, Paulc; Skiest, Daniel J.f; Margolis, Davidg; Sneller, Michael C.h; Little, Susan J.i; Gulick, Roy M.j; Mellors, John W.k; Gandhi, Rajesh T.l; Schooley, Robert T.i; Henry, Keithm; Tebas, Pablon; Deeks, Stevec; Chun, Tae-Wookh; Collier, Ann C.o; Hecht, Frederick M.c; Li, Jonathan Z.a

Author Information
doi: 10.1097/QAD.0000000000002978


Analytical treatment interruption (ATI) is an essential strategy to determine the effectiveness of HIV cure strategies. Although previous work has demonstrated that ATI does not lead to an increase in the reservoir size [1,2], minimizing unnecessary prolonged exposure to viremia is an important consideration for all studies. Historically, there has been little concordance in antiretroviral therapy (ART)-restart criteria among ATI studies and our understanding of how different ART-restart criteria influence viral rebound dynamics remains incomplete [3]. The interactive viral rebound calculator ( was created as a pooled analysis of plasma viral loads (pVLs) of more than 700 participants from 12 ATI trials to predict HIV rebound after stopping ART [4–16].

The tool allows the user to set the ART-restart criteria to predict the percentage of all participants, posttreatment noncontrollers, and posttreatment controllers (PTCs) that would remain off therapy from week 1 through week 48. The interactive tool also allows the user to set an absolute pVL threshold or a multiweek threshold (e.g. pVL>1000 for a duration of 4 weeks) as well as customize results based on the timing of ART initiation, frequency of pVL measurements, ART regimens, therapeutic intervention arms and PTC frequency [the default is the frequency identified in the CHAMP study of PTCs based on the criteria: pVL<400 copies/ml at ≥2/3 time points for ≥24 weeks post-ATI [4]]. We also assessed how varying the threshold of suppressed time points and pVLs affected the frequency of PTC identification.

During ATI, investigators aim to balance safety issues of prolonged viremia with characterizing the effect of cure interventions such as: time to viral rebound, HIV viral set point, and identification of PTCs. Although the time to viral rebound and set point data are easily quantifiable, PTC frequency calculations remain elusive as ART is often restarted before confirming controller status. Here, we compared the impact of several commonly used threshold pVL ART restart criteria (1000 pVL, 1000 pVL for 2 weeks, 1000 pVL for 4 weeks [17] and 50 000 pVL for 4 weeks) on the ability of an ATI trial to detect PTCs as defined by the CHAMP definition [4]. The calculator applies the user's ART restart criteria to the dataset containing the 700+ participants pVL data to estimate the proportion of participants experiencing viral rebound and remaining off ART after treatment discontinuation (see Supplemental methods, In the CHAMP study, PTCs frequently had an early viral load peak before subsequent viral control off ART. Some of these PTCs may be missed depending on the ART restart criteria, which would have mandated the resumption of ART prior to demonstrating their natural ability to suppress virus. Our calculator predicted that these criteria would fail to identify 47%, 18%, 0%, and 0% of PTCs, respectively, due to premature ART restart. Of the four criteria, the 1000 pVL for 1-week criterion had high specificity (99%), but low sensitivity (53%), while the 50 000 pVL for 4-week criterion had low specificity (12%), but high sensitivity (100%). The 1000 pVL for 4-week criterion achieved a balance with 90% specificity and 100% sensitivity for identifying PTCs.

The definition of posttreatment control remains fluid and not yet standardized within the field. In addition to the calculator's ability to predict the number of CHAMP-defined PTCs identified by each ART restart criteria, we also evaluated five alternative PTC definitions, each changing one aspect of the CHAMP criteria (Supplemental Table 1, viral load suppression for 100% of timepoints; viral load suppression for 90% of timepoints; viral load threshold of 200 copies/ml; viral load threshold of 1000 copies/ml; suppression for 48 weeks (Fig. 1). Significantly fewer PTCs were identified in both the chronic and early-treated arms in definition 1 (100% suppression ≤ 400 pVL, P = 0.04 and 0.01, respectively) and significantly more PTCs were identified in the chronic-treated arm in definition 4 (≥2/3 suppression ≤ 1000 pVL, P = 0.03). PTCs were more frequently identified in early-treated participants compared with chronic-treated participants in every iteration except for the final case, suppression for 48 weeks. Importantly, key characteristics (pre-ART viral load, CD4+ cell count decline, baseline CD4+ cell count, peak viral load and peak viral load week) remained comparable for the PTCs regardless of the specific PTC definition used (Supplemental Table 2,

Fig. 1:
Effect of posttreatment controller definitions on estimated frequency of control.

One limitation of this analysis was the heterogeneity in frequency of viral load measurements during the ATI among studies, with some studies using weekly viral load monitoring, but other studies using less frequent monitoring. We also excluded participants on nonnucleoside reverse transcriptase inhibitor (NNRTI)-based therapy as it has been shown to impact viral rebound timing, likely due to the prolonged half-life of NNRTIs [18–20].

In summary, the results provide insights on the chances of identifying PTCs given different ART restart criteria and demonstrate that the expected frequency of posttreatment control is highly dependent on the viral load definitions used. The online calculator provides an interactive tool for estimating viral rebound outcomes and for supporting the design of ATI trials.


We thank the participants, investigators and site staff for all of the included studies.

This work was supported in part by the Harvard University Center for AIDS Research (to Drs Li and Gandhi, NIAID 5P30AI060354–08); National Institutes of Health (NIH) grants UM1 AI068634 (Statistical and Data Management Center of the AIDS Clinical Trials Group), UM1 AI068636 (AIDS Clinical Trials Group), subcontract from UM1 AI106701 to the Harvard Virology Support Laboratory (to Dr Li); The Foundation for AIDS Research (amfAR to Dr Li); Harvard University Center for AIDS Research (CFAR, to Dr Hutchinson); and an NIH funded program P30 AI060354 (to Dr Hutchinson), which is supported by the following NIH Co-Funding and Participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, NIDDK, NIGMS, FIC, and OAR.

A list of the CHAMP study team contributors is provided in the Supplementary Appendix,

Conflicts of interest

There are no conflicts of interest.


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