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Epidemiology and Prevention

Timing of Antiretroviral Treatment, Immunovirologic Status, and TB Risk: Implications for Testing and Treatment

Pettit, April C. MD, MPH*,†; Mendes, Adell MPH; Jenkins, Cathy MS†,§; Napravnik, Sonia PhD; Freeman, Aimee MA; Shepherd, Bryan E. PhD†,§; Dowdy, David MD; Gill, John MB, ChB, MSc; Rachlis, Anita MD#; Moore, Richard MD**; Sterling, Timothy R. MD*,†; for the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) investigators of International epidemiologic Databases to Evaluate AIDS (IeDEA)

Author Information
JAIDS Journal of Acquired Immune Deficiency Syndromes: August 15, 2016 - Volume 72 - Issue 5 - p 572-578
doi: 10.1097/QAI.0000000000001018

Abstract

INTRODUCTION

Tuberculosis (TB) remains an important public health problem, particularly among HIV-infected persons.1 Highly active antiretroviral therapy (HAART) is associated with decreases in TB risk by restoring CD4+ T lymphocytes and cell-mediated immunity.2–5 However, TB risk and mortality have been observed to increase transiently in the 3–6 months after HAART initiation.6–14 One hypothesis for this short-term increased risk is the unmasking of subclinical TB through immune restoration on HAART, which manifests in its most severe form as TB-associated immune reconstitution inflammatory syndrome (IRIS).15,16 In contrast, confounding by low CD4+ counts and high HIV viral loads before HAART initiation and in the 6 months after HAART initiation could explain the increased risk of TB after starting HAART.

Two randomized controlled trials have evaluated the effect of HAART initiation at higher CD4+ counts on incident TB risk as a secondary outcome.17–19 Although the number of cases of TB were higher among the delayed treatment arms, the exact timing of the cases of TB in relation to HAART initiation and the exact CD4+ count and viral load at the time of TB diagnosis are unclear (because the CD4+ count and viral load may have varied from enrollment to the time of TB diagnosis). These studies were not designed to evaluate the impact of HAART initiation and laboratory biomarkers (CD4+ counts and viral load) on TB incidence during the first months after HAART initiation.

Alternatively, observational data can be used to assess the effect of HAART initiation on TB risk, while controlling for the time-dependent confounding of CD4+ count and viral load. Four previous observational studies have assessed the TB risk in the same population of adults before and after HAART initiation, appropriately adjusting for time-updated covariates using marginal structural models.20 The first study found a decreased risk of incident TB after HAART initiation, but HAART duration was not incorporated into the adjusted model.21 The next 3 studies all found no effect of HAART initiation on short-term incident TB. However, these studies assessed HAART use in an intention-to-treat (ITT) format in which gaps in HAART use were not incorporated,22,23 assessed only a 3-month duration of HAART exposure in the adjusted model,22,23 or were limited by a low number of cases of TB.24 Assessment of HAART exposure in the ITT format may lead to an underestimation of the protective effective of HAART on TB risk compared with assessment in the as-treated (AT) format.

Given the varying methods and discrepant results from previous studies, we aimed to evaluate both the short- and long-term effects of HAART on TB risk while appropriately controlling for time-varying HAART use and time-updated laboratory biomarkers (CD4+ counts and HIV viral loads). We sought to overcome the limitations of previous studies by evaluating the effect of HAART initiation on incident TB risk in a large, well-characterized cohort of HIV-infected persons, by incorporating both 3- and 6-month durations of HAART exposure into the adjusted model, and by assessing HAART use in both the ITT and AT formats.

METHODS

Patient Population

We conducted an observational cohort study among HIV-infected adults enrolled in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) between 1998 and 2011.25 Data for this study were from 13 cohorts: AIDS Linked to the IntraVenous Experience, Fenway Community Health Center, Case Western Reserve University Immunology Unit Patient Care and Research Database, Adult AIDS Clinical Trials Group Longitudinal Linked Randomized Trials (AACTG) Longitudinal Linked Randomized Trials, University of Alabama at Birmingham 1917 Clinic Cohort, Kaiser Permanente Northern California, University of North Carolina Chapel Hill HIV Clinic, Montreal Chest Institute Immunodeficiency Service Cohort, Johns Hopkins HIV Clinical Cohort, HAART Observational Medical Evaluation and Research, Vanderbilt Comprehensive Care Clinic HIV Cohort, Southern Alberta Clinic Cohort, and University of Washington HIV Cohort. All sites were located in the United States or Canada, both resource-rich countries with low TB incidence. Patients were included if they were HIV-1 seropositive, had ≥2 visits within 12 months of the initial visit date, and were HAART naive. Procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Persons with TB before enrollment were included and followed for additional TB episodes if there was a gap of at least 240 days between the TB episodes. Additionally, persons with TB diagnosed within 30 days of the initial visit were excluded to remove the prevalent cases of TB from the analysis.

Study Definitions

TB diagnoses were classified in accordance with US Centers for Disease Control and Prevention guidelines as either culture-confirmed or culture-negative and were validated by local investigators via standardized abstraction forms.26 Culture-negative disease was established by (1) signs, symptoms, and chest radiography consistent with TB; (2) pathologic findings including necrotizing granulomas and acid-fast bacilli; or (3) a positive nucleic acid amplification test and a clinical response to anti-TB therapy. The NA-ACCORD sites participating in this study included all sites able to validate TB diagnoses based on this definition. The date of TB diagnosis was defined as the date of initiation of anti-TB therapy. Demographic and laboratory data were obtained. Study baseline was defined as the initial visit date. Baseline CD4+ lymphocyte count and HIV-1 RNA values were defined as the first available values within 120 days before or up to 7 days after the initial visit date.

Person-time was contributed from the time of the initial visit date until first TB diagnosis, loss to follow-up, death, or administrative censoring. The date of administrative censoring varied by cohort and ranged from December 2009 to December 2011. Loss to follow-up was defined as a gap of >12 months between available laboratory results; these patients were censored at the time of 12 months after their last available laboratory results.

HAART was defined as a regimen that contained at least 3 antiretroviral drugs including a protease inhibitor (with or without ritonavir boosting), a nonnucleoside reverse transcriptase inhibitor, a nucleoside reverse transcriptase inhibitor, an integrase inhibitor, or an entry inhibitor. Antiretroviral therapy not meeting the definition of HAART was defined as not being on HAART. A person was considered on HAART after at least 7 days of exposure. HAART status was assessed in 2 ways: (1) ITT, in which once a person initiated HAART any subsequent gaps in HAART exposure were ignored and (2) AT, in which gaps in HAART exposure were accounted for and the clock for duration of HAART exposure was restarted at each HAART initiation or reinitiation. The ITT analysis was performed to mimic a clinical trial investigating the impact of HAART initiation on incident TB. The AT analysis was performed to use the available real-world data accounting for patients stopping and restarting HAART.

Statistical Analysis

The χ2 and Wilcoxon rank sum tests were used to compare categorical and continuous variables, respectively. A marginal structural model was constructed to appropriately adjust for CD4+ lymphocyte count, a time-dependent confounder affected by HAART exposure. The marginal structural model had 3 components used to create inverse probability weights (IPWs) incorporated into the primary model. The first was a logistic regression model for predicting the probability of HAART exposure in each month. The second component of the marginal structural model was a logistic regression model predicting the probability of loss to follow-up in each month. These models included time since the initial visit date in months (including a quadratic), age at the initial visit date (years), sex, race (black, nonblack), foreign-born status (US or Canadian born, not US or Canadian born), the NA-ACCORD site, the year of enrollment, injection drug use as HIV risk factor, the baseline CD4+ lymphocyte count, the baseline HIV-1 RNA, and the current CD4+ count (including the square root); current HIV viral load (including log transformed) was also included in the model predicting the probability of loss to follow-up in each month. If a patient had >1 one CD4+ or HIV viral load measurement during a given month, the average of these measurements was used; if a patient did not have a measurement during a given month, the value from the previous month was carried forward. The third component of the marginal structural model was a logistic regression model predicting the probability of missing baseline laboratory values. This model included the same variables as the first 2 with the exception of time since study entry and CD4+ or HIV-1 RNA laboratory values.

A weighted pooled logistic regression model including robust standard errors was used to estimate the odds ratio for the risk of TB. The weights from the 3 components described above were multiplied to obtain the single weight incorporated into this model. This model included current HAART status (not on HAART, ≤180 days of HAART, or >180 days of HAART), time since the initial visit date in months (including a quadratic), age at the initial visit date (years), sex, race (black, nonblack), foreign-born status (US or Canadian born, not US or Canadian born), the year of enrollment, injection drug use as HIV risk factor, the baseline CD4+ lymphocyte count, and the baseline HIV-1 RNA. The probability of starting (ITT and AT analyses) or stopping (AT analysis) HAART was estimated and incorporated into the weighted pool logistic regression model with IPWs throughout the entire follow-up, including post-HAART initiation. The 180 day cutoff was chosen for the main analysis based on our findings of an elevated crude TB risk during this time period (Table 2). Additionally, a second set of analyses included HAART status with 90 days duration (not on HAART, ≤90 days of HAART, or >90 days of HAART) to be consistent with previous studies.22,23 Sensitivity analyses including cases of TB and follow-up during the first 30 days after enrollment were performed as done in a previous study.22

TABLE 1.
TABLE 1.:
Clinical and Demographic Characteristics of the Study Population
TABLE 2.
TABLE 2.:
TB Incidence According to Time from HAART Initiation

Results are reported with stabilized weights truncated at the first and 99th percentiles. Findings using stabilized weights truncated at the fifth and 95th as well as at the 10th and 90th percentiles were also assessed. The median of the untruncated stabilized weights was 0.98 [interquartile range (IQR): 0.58–1.34]. All statistical tests were 2-sided. P values <0.05 were considered statistically significant. Statistical analyses were performed using STATA version 13 (College Station, TX).

RESULTS

There were 26,342 patients included in this study; these patients were followed for a total of 147,557 person-years (p-y) (median 5.4 years). The median age at initial visit date was 39 years, 80% were men, 29% were black, 5% were not US or Canadian born, and 17% reported injection drug use as an HIV risk factor. The median baseline CD4+ lymphocyte count was 301 cells per cubic milliliter (IQR: 142–490 cells per cubic milliliter), and the median HIV-1 viral load was 23,751 copies per milliliter (IQR: 1710–100,000 copies per milliliter). The baseline CD4+ lymphocyte count was missing for 777 (2.9%) and the baseline HIV-1 viral load was missing for 1104 (4.2%). There were 21,342 (81.0%) patients who initiated HAART during the study period, and the median CD4+ count at HAART initiation was 262 cells per cubic milliliter (IQR: 154–370).

There were 124 patients who were diagnosed with TB after enrollment in the NA-ACCORD. There were 10 patients with TB diagnoses both before and after enrollment in the NA-ACCORD; 3 were excluded because there were <240 days between the date of TB diagnosed before enrollment and after enrollment. Of the 121 remaining cases of TB, 27 were excluded due to occurrence within the first 30 days after enrollment. There were no patients with >1 case of TB, both occurring after enrollment. Therefore, 94 patients who developed TB during the study period were included in the primary analysis (64 per 100,000 p-y). The median time from enrollment to TB diagnosis was 390 days (IQR: 95–1038 days).

Of the 94 patients with TB, 66 (70.2%) had pulmonary disease and 23 (24.5%) had extrapulmonary disease; the site of disease was unknown for 5 (5.3%). TB was laboratory confirmed by smear or culture for 60 (63.8%); 41 (43.6%) were culture-positive and 38 (40.4%) were smear-positive. Patients with TB were more likely to be black, foreign born, and report injection drug use as an HIV risk factor. They were also more likely to have a lower baseline CD4+ lymphocyte count and a higher baseline viral load. There were no differences in the median frequency of CD4+ measurements during the study period (Table 1).

ITT Analysis

In the ITT analysis, 31 cases of TB occurred among persons before HAART initiation {93 per 100,000 p-y [95% confidence interval (CI): 63 to 132]; 33,371 p-y of follow-up}. Sixty-three cases of TB occurred among persons after HAART initiation [55 per 100,000 p-y (95% CI: 42 to 71); 114,186 p-y of follow-up]. Of these 63 cases, 21 occurred in the first 6 months after HAART initiation [203 per 100,000 p-y (95% CI: 126 to 311); 10,329 p-y of follow-up] and 42 occurred after >6 months of HAART [40 per 100,000 p-y (95% CI: 29 to 55); 103,857 p-y of follow-up] (Table 2).

In the adjusted marginal structural model, the adjusted odds ratio (aOR) for the risk of TB among persons on HAART of any duration was 0.39 (95% CI: 0.22 to 0.70) compared with those not on HAART. The aOR for TB in the first 6 months after HAART initiation was 0.65 (95% CI: 0.28 to 1.51) and 0.29 (95% CI: 0.16 to 0.53) after >6 months on HAART, compared with persons not on HAART (Table 3). The results were similar when HAART use was categorized as not on HAART, ≤3 months since HAART initiation, and >3 months on HAART (Table 4). Results were also similar when stabilized IPW were truncated at the fifth and 95th percentiles or were truncated at the 10th and 90th percentiles (see Table S1, Supplemental Digital Content, http://links.lww.com/QAI/A813).

TABLE 3.
TABLE 3.:
Tuberculosis Risk Using 6-Month HAART Cutoff Marginal Structural Model
TABLE 4.
TABLE 4.:
Tuberculosis Risk Using 3-Month HAART Cutoff Marginal Structural Model

There were 27 cases of TB that occurred within the first 30 days after enrollment in the NA-ACCORD. In a sensitivity analysis in which cases diagnosed within 30 days of the initial visit were included, the aOR for the risk of TB among persons in the first 6 months after HAART initiation was 0.36 (95% CI: 0.21 to 0.64) compared with persons not on HAART. The aOR for the risk of TB among persons after >6 months on HAART was 0.16 (95% CI: 0.10 to 0.26) (Table 5).

TABLE 5.
TABLE 5.:
Tuberculosis Risk Using 6-Month HAART Cutoff (Including Cases of TB Diagnosed in the First 30 Days After Enrollment) Marginal Structural Model

AT Analysis

In the AT analysis, 48 cases of TB occurred among persons not on HAART [70 per 100,000 p-y (95% CI: 51 to 92); 69,046 p-y of follow-up]. Forty-six cases of TB occurred among persons on HAART [59 per 100,000 p-y (95% CI: 43 to 78); 76,511 p-y of follow-up]. Of these 46 cases, 22 occurred in the first 6 months of HAART [152 per 100,000 p-y (95% CI: 95 to 229); 14,510 p-y of follow-up] and 24 occurred after >6 months of HAART [37 per 100,000 p-y (95% CI: 24 to 56); 64,001 p-y of follow-up] (Table 2).

Using the adjusted marginal structural model, the aOR for the risk of TB among persons on HAART of any duration was 0.35 (95% CI: 0.20 to 0.61) compared with those not on HAART. The aOR for TB in the first 6 months after HAART initiation was 0.82 (95% CI: 0.38 to 1.78) and 0.12 (95% CI: 0.06 to 0.24) after >6 months on HAART, compared with persons not on HAART (Table 3). Results were similar when 3 months of HAART use was assessed (Table 4) and when varying the truncation of the stabilized weights (see Table S2, Supplemental Digital Content, http://links.lww.com/QAI/A813).

In the sensitivity analysis including TB diagnosed within 30 days of the initial study visit, the aOR for the risk of TB among persons in the first 6 months after HAART initiation was 0.42 (95% CI: 0.24 to 0.72) compared with persons not on HAART. The aOR for the risk of TB among persons after >6 months on HAART was 0.07 (95% CI: 0.04 to 0.14) (Table 5).

DISCUSSION

In our study, the crude TB rate appeared to be the highest in the first 6 months after HAART initiation [203 per 100,000 p-y (95% CI: 126 to 311)] compared with the rate among those not on HAART [93 per 100,000 p-y (95% CI: 63 to 132)] and those on HAART for >6 months [40 per 100,000 p-y (95% CI: 29 to 55)] (Table 2). However, after incorporating time-updated HAART exposure and controlling for time-updated CD4+ lymphocyte counts and HIV viral loads, HAART reduced the long-term odds of incident TB by 61%, which is consistent with the findings of previous studies.2–5 Moreover, HAART did not seem to increase the risk of TB during the first 6 months after HAART initiation (Table 3). The apparent increased crude TB risk during the first 6 months was confounded by low CD4+ lymphocyte counts and high HIV viral loads at the time of and for the first 6 months after HAART initiation.

It can be difficult in an observational study to determine if the cases of TB diagnosed shortly after entry into care are incident or prevalent cases. A previous study in the HIV-CAUSAL Collaboration excluded cases of TB diagnosed in the first 30 days after enrollment in an attempt to exclude potentially prevalent cases.22 Similar to HIV-Cohorts Analyzed Using Structural Approaches to Longitudinal data (CAUSAL), we found that the inclusion of these cases in a sensitivity analysis led to an overestimation of the beneficial effect of HAART initiation on TB risk. Regardless, we found no evidence that HAART increased the risk of incident TB shortly after its initiation.

This study sought to overcome the limitations of previous studies in 2 important ways. First, we assessed HAART exposure in both the ITT and AT formats. We hypothesized that the ITT analysis might underestimate the protective effect of HAART on TB risk. When comparing the results of our ITT and AT analyses using both the 6- and the 3-month cutoffs for categorization of HAART, the confidence intervals consistently overlapped (Tables 3 and 4). Therefore, we found no differences in the results based on the treatment of HAART exposure in an ITT or AT format.

Second, we categorized early HAART exposure using both 3- and 6-month cutoffs. We hypothesized that the 3-month cutoff might underestimate the effect of early HAART initiation on TB risk. When comparing the results using the 3- and 6-month cutoffs, the confidence intervals were again consistently overlapping in both the ITT and AT analyses (Tables 3 and 4). Therefore, we also found no differences in the results based on the categorization of early HAART exposure using 3- or 6-month cutoffs.

These findings suggest that the “HIV-test-and-treat” strategy should not lead to an increased TB risk early after HAART initiation.27 This theoretical strategy would require universal HIV testing and initiation of HAART immediately after diagnosis, regardless of the CD4+ lymphocyte counts. Using this strategy, TB risk could potentially be decreased both by reducing the amount of time at which a patient's CD4+ lymphocyte count remains low and by decreasing HIV transmission. The impact of this strategy has been modeled using data from sub-Saharan Africa, and it has been shown that TB incidence rates in 2050 would be reduced by 66%–97.7% depending on how quickly a patient initiated HAART after HIV diagnosis.28

One limitation of this study is the low number of TB events that limit the power to identify statistically significant differences. It is possible that some cases of TB were missed if they were not documented in the medical record. However, this is unlikely given that TB is a reportable disease in the United States and Canada and that these patients were receiving care for HIV infection. A second limitation is that the clinical data required to determine if a case of TB was due to IRIS, particularly the short-term change in CD4+ counts and viral loads after HAART initiation, were not available. Further study is needed to determine if HAART would decrease the risk of TB-associated IRIS after adjustment for time-updated laboratory biomarkers. Third, it is possible that the association between HAART exposure and TB risk could be confounded by other factors not included in our adjusted analyses. An additional limitation pertains to the generalizability of these findings to resource-limited and high-TB-incidence settings.

In conclusion, we found that although the crude TB risk was highest in the first 6 months after HAART initiation, after controlling for CD4+ count and HIV viral load during this period, HAART was not associated with an increased the risk of TB. These findings highlight the need for continued vigilance for TB and close clinical follow-up shortly after HAART initiation given the elevated crude TB rates in the first 6 months after the initiation of HAART. They also support the theory that the “test-and-treat” strategy for HIV infection should not lead to an increased TB risk in the early period after HAART initiation.

ACKNOWLEDGMENTS

NA-ACCORD Collaborating Cohorts and Representatives: AIDS Link to the IntraVenous Experience: Gregory D. Kirk; Adult AIDS Clinical Trials Group Longitudinal Linked Randomized Trials: Constance A. Benson and Ronald J. Bosch; Fenway Health HIV Cohort: Stephen Boswell, Kenneth H. Mayer, and Chris Grasso; HAART Observational Medical Evaluation and Research: Robert S. Hogg, P. Richard Harrigan, Julio SG Montaner, Angela Cescon, and Hasina Samji; HIV Outpatient Study: John T. Brooks and Kate Buchacz; HIV Research Network: Kelly A. Gebo and Richard D. Moore; Johns Hopkins HIV Clinical Cohort: R.D.M.; John T. Carey Special Immunology Unit Patient Care and Research Database, Case Western Reserve University: Benigno Rodriguez; Kaiser Permanente Mid-Atlantic States: Michael A. Horberg; Kaiser Permanente Northern California: Michael J. Silverberg; Longitudinal Study of Ocular Complications of AIDS: Jennifer E. Thorne; Multicenter Hemophilia Cohort Study–II: James J. Goedert; Multicenter AIDS Cohort Study: Lisa P. Jacobsoncand Gypsyamber D'Souza; Montreal Chest Institute Immunodeficiency Service Cohort: Marina B. Klein; Ontario HIV Treatment Network Cohort Study: Sean B. Rourke, Ann N. Burchell, and A.R.R.; Retrovirus Research Center, Bayamon Puerto Rico: Robert F. Hunter-Mellado and Angel M. Mayor; Southern Alberta Clinic Cohort: M. John Gill; Studies of the Consequences of the Protease Inhibitor Era: Steven G. Deeks and Jeffrey N. Martin; Study to Understand the Natural History of HIV/AIDS in the Era of Effective Therapy: Pragna Patel and John T. Brooks; University of Alabama at Birmingham 1917 Clinic Cohort: Michael S. Saag, Michael J. Mugavero, and James Willig; University of North Carolina at Chapel Hill HIV Clinic Cohort: Joseph J. Eron and S.N.; University of Washington HIV Cohort: Mari M. Kitahata, Heidi M. Crane, and Daniel R. Drozd; Vanderbilt Comprehensive Care Clinic HIV Cohort: T.R.S., David Haas, Sally Bebawy, and Megan Turner; Veterans Aging Cohort Study: Amy C. Justice, Robert Dubrow, and David Fiellin; Women's Interagency HIV Study: Stephen J. Gange and Kathryn Anastos. NA-ACCORD Study Administration: Executive Committee: R.D.M., Michael S. Saag, Stephen J. Gange, Mari M. Kitahata, Keri N. Althoff, Rosemary G. McKaig, Amy C. Justice and Aimee M. Freeman; Administrative Core: R.D.M., A.M.F., and Carol Lent; Data Management Core: Mari M. Kitahata, Stephen E. Van Rompaey, Heidi M. Crane, Daniel R. Drozd, Liz Morton, Justin McReynolds, and William B. Lober; Epidemiology and Biostatistics Core: Stephen J. Gange, Keri N. Althoff, Alison G. Abraham, Bryan Lau, Jinbing Zhang, Jerry Jing, Elizabeth Golub, Shari Modur, Cherise Wong, Brenna Hogan, Weiqun Tong, and Bin Liu.

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Keywords:

antiretroviral therapy; immune reconstitution inflammatory syndrome; marginal structural model; tuberculosis

Supplemental Digital Content

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