Aging adults with HIV are at increased risk of myocardial infarction (MI) compared with otherwise similar adults without HIV.1,2 This increased risk is likely the result of an amalgam of simultaneously occurring factors, including: (1) increased prevalence of traditional MI risk factors among adults with HIV3–6; (2) HIV-associated immune activation and dysregulation, excess inflammation, and hypercoagulation7,8 that is blunted but not normalized with antiretroviral (ART)-induced virologic suppression and7,9,10; (3) possibly the use of specific ART drugs.4,11,12
There have been conflicting reports linking increased risks of MI and use of nucleoside reverse transcriptase inhibitor agents, with considerable attention focused on abacavir (ABC). Initial reports came from the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D cohort); D:A:D remains the largest cohort in which this relationship has been examined systematically to date. Their analysis included 25,000 adults with HIV followed prospectively for at least 10 years and demonstrated a nearly 2-fold risk of MI incidence associated with recent ABC exposure (with the previous 6 months), although more remote or cumulative ABC exposures were not associated.13
Other observational studies have corroborated these findings.14–17 The French Hospital Database on HIV18 initially reported findings similar to those of D:A:D regarding recent ABC use and MI risk (odds ratio = 2.01, 95% confidence interval: 1.11 to 3.64) but this association did not remain significant after accounting for cocaine or intravenous drug use.18
Other cohorts have failed to identify such an association19 The U.S. Food and Drug Administration undertook a meta-analysis, including participants in randomized controlled trials, and found no increased risk of cardiovascular disease (CVD) associated with ABC use.20 However, of the 26 studies included in this analysis, only 5 reported a mean follow-up time >2 years and none reported a follow-up time of >5 years.20 Reports from cohorts that have not demonstrated an ABC/MI association have differed widely in analytic methodology and clinical definitions used.
Tenofovir disoproxil fumarate (TDF) has been used more commonly in combination ART regimens than ABC since both became available, and ABC use has been more common among persons for whom TDF use was contraindicated, particularly because of renal impairment (calculated GFR < 60 mL/min). Because renal insufficiency is an independent risk of MI, selection of ABC for use among persons with kidney disease may represent a channeling bias enriching the population of ABC users with persons who have higher MI risk compared with nonusers; such bias may have influenced ABC use from 2008 onward13,21,22 in individuals with known CVD risk factors. Furthermore, given the links between protease inhibitor (PI) use and dyslipidemia (which may increase the risk of MI), as well as the link between delayed ART therapy initiation and low CD4 counts (which have also been linked with MIs),23 time-dependent confounding of the ABC and MI relationship is possible.
The objective of the current analysis was to determine the effect of recent ABC use on the risk of MIs overall and stratified as either a type1 MI (T1MI) or type 2 MI (T2MI). We used data from the North American Cohort Collaboration on Research and Design (NA-ACCORD) and analytical methods that attempted to account for potential channeling bias and time-dependent confounding of the ABC and MI relationship.
Study Population: The NA-ACCORD
The NA-ACCORD is the largest consortium of HIV cohorts in the United States and Canada. It serves as the North American region of the International Epidemiology Databases to Evaluate AIDS project, supported by the National Institutes of Health. Details on this collaboration have been published previously.24 Briefly, NA-ACCORD consists of single and multisite clinical and interval cohort studies that accumulate data from adults (≥18 years old) with HIV at ≥200 sites in the United States and Canada. Participating cohorts submit comprehensive data on enrolled participants to the Data Management Core (University of Washington, Seattle, WA), where data are harmonized across cohorts and transmitted to the Epidemiology/Biostatistics Core (Johns Hopkins University, Baltimore, MD), which conducted the analyses presented here. The human subject research activities of the NA-ACCORD and each of the participating cohort studies have been reviewed and approved by their respective local institutional review boards and by the Johns Hopkins University School of Medicine.
For this study, 7 US clinical cohorts within the NA-ACCORD with complete access to both inpatient and outpatient electronic medical records validated the occurrence of MI between January 1, 2001, and December 31, 2013. The study population was restricted to persons observed to initiate ART. A flowchart depicting the selection of NA-ACCORD participants for the current study can be found in Supplemental Digital Content Figure 1, http://links.lww.com/QAI/B120.
The primary outcome for this study was incident MI. The protocol for ascertainment, validation, and classification of MIs within the NA-ACCORD has been previously published.25 Briefly, potential MI events were centrally ascertained within the NA-ACCORD data repository using a standard protocol based on primary screening for MI that included the presence of an inpatient or outpatient MI diagnosis (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9] diagnoses including 410 codes, 411.0, 412, and 429.7), or serum cardiac enzyme levels above the laboratory-specific upper limit of normal for troponin-I, troponin-T, or creatine kinase MB. Comprehensive medical records regarding each potential MI identified by this screening were abstracted from electronic medical records, deidentified, and uploaded to the central NA-ACCORD data repository using a secure website. Information regarding specific ART drug use was redacted to avoid possible reviewer bias. Each potential MI event was adjudicated by at least 2 physician reviewers with extensive expertise in MI adjudication. A third review was conducted if discrepancies occurred. Events were classified as either T1MI, which is caused by atherosclerotic plaque rupture or erosion with intraluminal thrombus, or T2MI, which is caused by ischemic imbalance between myocardial oxygen supply and demand often found in the settings of hypotension and sepsis according to the universal definition of MI.26 Reviewers also identified persons who screened positive for an MI and then underwent a cardiac intervention indicating severe underlying coronary artery disease (usually coronary artery bypass graft or percutaneous coronary intervention with stent placement); these events were considered validated MIs and classified as T1MIs.
For this study, only incident MI events were included as an outcome. A patient with an MI identified at or before ART exposure (baseline) was excluded from subsequent analysis.
Exposure: Recent ABC Use
Exposure to ABC was measured from prescription and medical records. Recent ABC use was updated as a time-varying variable in the analysis and defined as any reported use within the previous 6 months.
Sex, race and ethnicity, year of birth, and HIV transmission risk [including injection drug use (IDU)] were self-reported at enrollment. Cigarette smoking was defined as ever having smoked cigarettes based on clinician-recorded diagnoses and/or patient-reported responses to questionnaires administered by individual cohorts. Hepatitis C virus (HCV) infection was defined as having a positive serum HCV antibody test, detectable plasma HCV RNA, or evidence of an HCV genotype test as reported in medical records while under observation in the NA-ACCORD.
Treated hypertension was defined as a clinical diagnosis of hypertension and a prescription of antihypertensive medication. Diabetes mellitus was defined as a diagnosis of diabetes and prescription of diabetes-related medication, or a diabetes-specific medication, or a glycosylated hemoglobin (HbA1c) level ≥6.5%. Statin use was defined as prescription of an HMG-CoA reductase inhibitor medication. Low high-density lipoprotein cholesterol was defined as ≤40 mg/dL for men and ≤50 mg/dL for women. High low-density lipoprotein was defined as ≥130 mg/dL. Elevated total cholesterol was defined as ≥240 mg/dL. These were considered dichotomous variables. Because of the effect of statins, only low-density lipoprotein and total cholesterol measurements before statin initiation were considered. Elevated triglycerides were defined as ≥300 mg/dL. We calculated estimated glomerular filtration rate (eGFR) using the Chronic Kidney Disease Epidemiology Collaboration equation and27 dichotomized eGFR to represent renal function (eGFR ≥60 and <60 mL/min).
History of an AIDS-defining illness was based on clinical diagnoses defined according to the 1993 CDC case definition28; a CD4 count <200 cells/μL was not included in this definition. CD4 cell count was categorized in 3 strata (<200, 200–349, and ≥350 cells/μL). HIV virologic suppression was defined as a plasma HIV-1 RNA level ≤400 copies per milliliter. ART was defined as 3 ARV agents from at least 2 classes or a triple nucleoside reverse transcriptase inhibitor regimen containing ABC or TDF. Because of previous studies29 suggesting a link between use of ART combinations that included a protease inhibitor (PI-based) with MI,29,30 HIV treatment regimens were classified as PI-based and non–PI-based.
We used marginal structural models (MSMs)30 to determine the risk of MI with ABC use as a time-varying exposure in the presence of time-varying confounders that are affected by previous treatment. This approach accounts for channeling bias that may have existed among individuals with renal function impairment (and potentially higher prevalence of other known MI risk factors) receiving ABC because of the contraindication for TDF use by such persons.31 The use of MSM also accounts for time-dependent confounding of the ABC and MI relationship occurring after ART initiation. Individuals began contributing person-time to the analysis from the time of ART initiation until the earliest date of: (1) incident MI; (2) death; (3) 6 months after discontinuation of ABC use (because our measure of interest was current or recent ABC use, within the previous 6 months); (4) loss to follow-up (defined as 1 year after last CD4 or HIV RNA measurement); or (5) administrative censoring at the last date of the cohort full MI observation, or December 31, 2013. As noted above, participants with a validated MI at, or before, study entry were excluded as they were no longer at risk for their first MI event.
Individuals who initiated a non–ABC-containing ART regimen but subsequently received ABC were included and contributed person-time to the appropriate group. For the MSM analysis, we constructed the necessary stabilized weights as the product of probabilities from 3 models: initiating ABC, discontinuing ABC once initiated, and censoring (see Supplemental Digital Content Fig. 2, http://links.lww.com/QAI/B120 for box plots of the stabilized weights). Our primary analysis estimated the relationship of recent ABC use with the combined T1MI and T2MI outcome. Two subgroup analyses were performed. First, the outcome was redefined as T1MI only; individuals who had a T2MI were excluded altogether from the T1MI analysis. For the second subgroup analysis, the outcome was redefined as T2MI; persons with a T1MI were altogether excluded from the T2MI analysis. Because of the decreased number of events when stratified by MI type, we used a bootstrapped approach using the bootstrap-t approach (1000 first iterations and 25 second iterations) to estimate 95% confidence intervals for the subgroup analyses.
Two sensitivity analyses were performed. First, to test the a priori hypothesis that Framingham risk score (FRS) modified the effect of ABC use on MI occurrence, we used a global test of heterogeneity with a nested-models approach to determine whether the MSM model with an interaction term for ABC and FRS was a better fit, providing statistical evidence of interaction. Second, we estimated a naive model that did not account for the channeling bias or time-dependent confounding by removing the weights. Although all the variables in the MSM were retained in the naive model, we measured all confounders at study entry; only recent ABC use and age were time-varying.
Finally, we replicated the analysis published by the original D:A:D report of the relationship of ABC and MI.22 By replicating the D:A:D approach, we sought to evaluate whether similar findings could be ascertained among these 2 distinct study populations using similar methods. See Supplemental Digital Content Figure 1, Table 1, and Table 4, http://links.lww.com/QAI/B120 for details on the D:A:D replication analysis.
All analyses were performed using SAS version 9.3 (SAS Institute) and a P-value <0.05 guided statistical interpretations.
A total of 8265 adults contributed 29,077 person-years and 123 events (65 T1MI and 58 T2MI). Median follow-up time was 2.9 (interquartile range 1.4–5.1) years. Median follow-up time was similar in ABC initiators [3.0 (interquartile range 1.4–5.5) years] compared with persons who initiated other ART regimens [median = 2.9 (1.5–5.1), P = 0.27]. Compared with persons who did not initiate ABC, ABC initiators were more likely to be older, female, Black, have a history of IDU and heterosexual HIV transmission risk, have HCV infection, treated hypertension, diabetes, impaired kidney function, elevated total cholesterol, elevated triglycerides, low (<200 cells/mm3) CD4 count, and a history of clinical AIDS diagnosis at study entry (Table 1). There was a decrease in ABC use (defined as ≥1 month of ABC use) from a peak of 39% of participants in 2002 to a low of 9% in 2013 (Supplemental Digital Content Fig. 3, http://links.lww.com/QAI/B120).
The primary analysis of the combined MI outcome showed an increased risk among persons who had used ABC in the previous 6 months (P < 0.0001); this difference persisted when stratifying the outcome by MI type (T1MI P < 0.0001 and T2MI P < 0.0001, Fig. 1). The proportion of MI events that were type 1 or type 2 did not vary significantly by calendar period.
After accounting for potential channeling biases and time-varying confounding in the MSM approach, persons with recent ABC exposure had an 84% increase in MI risk compared with those without recent ABC exposure [adjusted hazard ratio (aHR) = 1.84 (1.17–2.91), Table 2]. Stratifying by MI type (T1MI or T2MI),22 there was a 62% increase in the risk of T1MI with recent ABC [aHR = 1.62 (1.01–2.94)]; the risk of T2MI was 2-fold with recent ABC use [aHR = 2.11 (1.08–4.29)].
To address the a priori hypothesis that CVD risk modified the effect of ABC on MI occurrence, FRSs at study entry were estimated for participants. Cumulative incidence of combined MI was highest among persons with the highest FRS (Fig. 2). Stratification by MI type demonstrated that the ABC use/MI relationship persisted with T1MI, as expected, and also with T2MI. There was no statistical evidence that FRS modified the effect of ABC on the risk of combined MI (P-value of interaction = 0.14). To further adjust for any differences by CVD risk, we included FRS in the MSM approach after excluding those variables already included in the FRS; the effect of recent ABC use on combined MI remained similar [aHR = 1.88 (1.20–2.95), Supplemental Digital Content Table 2, http://links.lww.com/QAI/B120].
In the analysis restricted to ART naïve persons who were observed to initiate ART, channeling biases that may have existed among persons with greater risk factors for T1MI were not taken into account; in this analysis, there was a 75% increase in the risk of combined MI with recent ABC use [aHR = 1.76 (1.15–2.68), Supplemental Digital Content Table 3, http://links.lww.com/QAI/B120].
The results of the replicated D:A:D approach in the NA-ACCORD [aHR = 1.63 (1.21–2.18)] were remarkably similar to the D:A:D's findings [aHR = 1.70 (1.17–2.47), Supplemental Digital Content Table 4, http://links.lww.com/QAI/B120].
The point estimates for the relationship of ABC and MI from the main analysis, subgroup analyses, sensitivity analyses, and the D:A:D replication and original studies can be visually compared in Figure 3.
In this large, demographically diverse cohort of adults with HIV in care, we identified an increased risk of MI associated with recent use of ABC (vs. use of ART not including ABC). This association was apparent regardless of MI type, both T1MI and T2MI. Although ABC recipients were enriched for traditional risk factors for T1MI (compared with non-ABC recipients) including kidney disease, our methods accounting for channeling biases and time-dependent confounding adjusted for this potential bias. In models that included a priori stratification for FRS or specific adjustment for FRS, we observed no attenuation of the magnitude of the association between recent ABC use with MI occurrence.
Furthermore, we were able to evaluate whether differential associations between ABC use by MI type existed. This is particularly relevant because recent work from our group has demonstrated that 45% of MIs occurring among adults with HIV are T2MI.1 Although we found that recent ABC use was associated with MI occurrence regardless of type, the magnitude of the association may be higher in T2MI. In several studies, increased MI risk has been associated with recent ABC use (within approximately 6 months of last use), implying that the pro-MI effects diminish within 6 months of ABC discontinuation. Our study purposely focused on recent and early (part of first ART regimen received) ABC exposure, with the advantage of using adjudicated MIs, a design that mimicked a clinical trial of ART initiation and included both T1MI and T2MI. However, some studies have also suggested that cumulative exposure to ABC beyond 6 months may further increase MI risk,32,33 possibly out to 24–36 months of exposure. Precise mechanisms by which ABC exposure increases the risk of MI are not currently clear. Our current understanding of potential pathophysiologic mechanisms by which ABC use may contribute to MI risk include promotion of enhanced platelet activation, aggregation, and increased adhesion to vascular endothelial cells (and consequent promotion of thrombus formation) through interference with processing of purinergic mediators, resulting in an overall ABC effect that seems to involve endothelial activation.34 Although prothrombotic states probably exist for both T1MI and T2MI, ABC's effect feasibly could create an enhanced vulnerability to MI that is distinct from, but possibly additive to, MI risk from preexisting coronary atherosclerosis or systemic inflammation or immune dysregulation. Such an effect could manifest early during ABC exposure but feasibly could also be an ongoing effect that is cumulative in nature.
Although exact mechanism(s) by which ABC exerts pro-MI effects remain speculative, our results suggest that clinical screening practices for MI risk among adults with HIV focused only on more traditional T1MI risks may be insufficient. As many as 50% of the T2MIs in NA-ACCORD are related to sepsis, cocaine use, or “other illicit drug-induced vasospasm,26,31 and in the current analysis, we found an adjusted HR of 1.76 among IDU for T2MI. Such findings are consistent with observations from the French Hospital Database on HIV, which reported that, after adjustment for cocaine or recreational intravenous drug use, an association between recent ABC use and MI was not apparent.18 In addition, recent work35 demonstrated a prominent association of cocaine use with the development of MIs among adults with HIV, prompting the suggestion that treating cocaine addiction may comprise an important adjunctive measure in MI prevention for this patient group.
Our analysis identified other risk factors for T2MI, including Black race, hypertension, and diabetes, traditional risk factors for T1MI, as well as a CD4 <200 cells/mm3. It is important to note that our analysis was not focused on describing the mechanism of any ART-related causations of MI, but rather isolating the effect of recent ABC use on MI risk in the context of channeling bias and time-dependent confounding. After adjusting for other factors in the model, these variables continued to be independently associated with T2MI. The identification of the prominence of T2MIs among persons living with HIV, as described by Drozd et al1, and our current finding of an independent association between recent ABC use and MI (both T2MI and T1MI) broaden our understanding of the spectrum of potential etiologies of MIs in this population. Potential interactions between lifestyle-related factors, genetics, inflammation, various causes of hypoxemia, and exposures to specific ARV drugs may help to expand our understanding of the diverse etiologic factors contributing to MI among persons living with HIV.
The NA-ACCORD differs from randomized interventional ART trials from which some ABC and MI data have been analyzed previously (such as those included in the Food and Drug Administration meta-analysis)20 because the NA-ACCORD analysis reflects data involving ABC utilization in clinical practice, including among adults at diverse stages of HIV disease with various types of MI risk factor profiles, and involved much longer follow-up. Our present study also represents an important improvement in verification of clinical MI events, Including designation into T1MI or T2MI categories. No MIs were included that were based only on participant self-report or on clinical International Classification of Diseases codes alone. Uniquely, there were a sufficient number of adults with available data from the time of ART initiation while observed in the NA-ACCORD, analysis of whom not only avoided potential analytic biases associated with prevalent ART use36 but also enabled the use of MSM approaches that seek to overcome analytic challenges involving time-dependent confounding. The D:A:D investigators were conscientious of excluding potential mediators from the analysis,22but in doing so, questions were raised as to whether the findings would be corroborated with alternative approaches. We note that an earlier analysis from the NA-ACCORD showed a weaker, although qualitatively similar, adjusted association between ABC exposure and MI.37 Unlike the previous analysis, the current analysis limited the study sample to patients who were observed to initiate ART to better mimic a clinical trial. We were also able to add more recently adjudicated MIs to the sample, although the selection criteria necessary for the our approach restricted the sample and associated number of MI events in comparison with another previous NA-ACCORD study (Supplemental Digital Content Fig. 1, http://links.lww.com/QAI/B120).1
We acknowledge that as an observational study lacking a randomized assignment of ABC, confounding is still possible and many variables exist that could potentially be predictive not only of MI occurrence but also of the prescribing of ABC. Our methods adjusted for kidney function, which is associated with ABC and TDF prescription, and also for T1MI risk factors such as diabetes, proatherogenic hyperlipidemia, hypertension, and smoking, which may also impact the prescribing of ABC. To date, we are not aware of evidence that risk factors for T2MI (sepsis, cardiac arrhythmia, recreational drug use, anemia, an impaired functional level, and female sex) are associated with ABC and TDF prescribing and there is no apparent reason to suspect that ABC recipients would be enriched in T2MI risks compared with non-ABC (largely TDF) users. It should also be noted that it is possible that reverse channeling bias (ie, the avoidance of ABC use among persons with traditional CAD risk factors) may have existed in the prescription of ABC after initial reports of its association with increased MI risk. If such a phenomenon existed, it may or may not have attenuated apparent associations between recent ABC use and MI occurrence.1 We further acknowledge that even with the large overall sample size, the number of MIs was limited and power to detect statistical evidence that FRS modified the effect of ABC on MI was limited.
In summary, in this large, diverse, North American cohort of adults with HIV in clinical care, we found that recent (within 6 months) ABC use significantly increased the risk of MI, both for T1MI and T2MI events. These associations persisted after adjusting for known MI risks, including kidney disease, and for FRS. Our MI ascertainment and adjudication procedures were detailed, precise, and involved central standardized adjudication by experts. Despite limitations, we believe that we have undertaken the most rigorous analysis possible within the limitations of our data set, using a study population and analytic methods that better address concerns raised from previous reports evaluating ABC use and MI risk. Our findings imply that analyses seeking to ascertain factors associated with MI among adults with HIV should include routine stratification by MI type. Clinicians may struggle to identify appropriate patients for ABC therapy, integrating known risk factors for T1MI and the less well-defined risks of T2MI. Our evolving understanding of the prominence of T2MI among adults with HIV makes patient selection for ABC use more challenging. Future work may focus on identifying the best subset of patients who can benefit from ABC with the least amount of risk.
NA-ACCORD Collaborating Cohorts and Representatives:
AIDS Clinical Trials Group Longitudinal Linked Randomized Trials: Constance A. Benson and Ronald J. Bosch AIDS Link to the IntraVenous Experience: Gregory D. Kirk. 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, Benita Yip, Julia Zhu, Kate Salters and Karyn Gabler. HIV Outpatient Study: Kate Buchacz and John T. Brooks. HIV Research Network: Kelly A. Gebo and Richard D. Moore. Johns Hopkins HIV Clinical Cohort: Richard D. Moore. 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: Charles Rabkin. Multicenter AIDS Cohort Study: Joseph B. Margolick, Lisa P. Jacobson and Gypsyamber D'Souza. Montreal Chest Institute Immunodeficiency Service Cohort: Marina B. Klein. Ontario HIV Treatment Network Cohort Study: Abigail Kroch, Ann Burchell, Beth Rachlis, Anita Rachlis, Patrick Cupido and Joanne Lindsay. Retrovirus Research Center, Bayamon Puerto Rico: Robert F. Hunter-Mellado and Angel M. Mayor. Southern Alberta Clinic Cohort: M. John Gill. Study 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 California at San Diego: William C. Mathews. University of North Carolina at Chapel Hill HIV Clinic Cohort: Joseph J. Eron and Sonia Napravnik. University of Washington HIV Cohort: Mari M. Kitahata, Heidi M. Crane and Daniel R. Drozd. Vanderbilt Comprehensive Care Clinic HIV Cohort: Timothy R. Sterling, David Haas, Peter Rebeiro, Megan Turner, Sally Bebawy and Ben Rogers. 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: Richard D. Moore, Michael S. Saag, Stephen J. Gange, Mari M. Kitahata, Keri N. Althoff, Michael A. Horberg, Marina B. Klein, Rosemary G. McKaig and Aimee M. Freeman. Administrative Core: Richard D. Moore, Aimee M. Freeman 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, Jennifer S. Lee, Bin You, Brenna Hogan, Jinbing Zhang, Jerry Jing, Bin Liu, Fidel Desir, Mark Riffon, and Sally Coburn
1. Drozd DR, Kitahata MM, Althoff KN, et al. Increased risk of myocardial infarction
-infected individuals in North America compared with the general population. J Acquir Immune Defic Syndr. 2017;75:568–576.
2. Freiberg MS, Chang CC, Kuller LH, et al. HIV
infection and the risk of acute myocardial infarction
. JAMA Intern Med. 2013;173:614–622.
3. Mdodo R, Frazier EL, Dube SR, et al. Cigarette smoking prevalence among adults with HIV
compared with the general adult population in the United States: cross-sectional surveys. Ann Intern Med. 2015;162:335–344.
4. Friis-Moller N, Weber R, Reiss P, et al. Cardiovascular disease risk factors in HIV
patients–association with antiretroviral therapy. Results from the DAD study. AIDS. 2003;17:1179–1193.
5. Butt AA, McGinnis K, Rodriguez-Barradas MC, et al. HIV
infection and the risk of diabetes mellitus. AIDS. 2009;23:1227–1234.
6. Kaplan RC, Kingsley LA, Sharrett AR, et al. Ten-year predicted coronary heart disease risk in HIV
-infected men and women. Clin Infect Dis. 2007;45:1074–1081.
7. Kuller LH, Tracy R, Belloso W, et al. Inflammatory and coagulation biomarkers and mortality in patients with HIV
infection. Plos Med. 2008;5:e203.
8. Deeks SG, Tracy R, Douek DC. Systemic effects of inflammation on health during chronic HIV
infection. Immunity. 2013;39:633–645.
9. Wada NI, Jacobson LP, Margolick JB, et al. The effect of HAART-induced HIV
suppression on circulating markers of inflammation and immune activation. AIDS. 2015;29:463–471.
10. Massanella M, Ouchi D, Marfil S, et al. Different plasma markers of inflammation are influenced by immune recovery and cART composition or intensification in treated HIV
infected individuals. PLoS One. 2014;9:e114142.
11. Carr A, Cooper DA. HIV
and AIDS. Gap between biology and reality in AIDS. Lancet. 1998;352:SIV16.
12. Holmberg SD, Moorman AC, Williamson JM, et al. Protease inhibitors and cardiovascular outcomes in patients with HIV
-1. Lancet. 2002;360:1747–1748.
13. Group DADS, Sabin CA, Worm SW, et al. Use of nucleoside reverse transcriptase inhibitors and risk of myocardial infarction
-infected patients enrolled in the D: A:D study: a multi-cohort collaboration. Lancet. 2008;371:1417–1426.
14. Marcus JL, Neugebauer RS, Leyden WA, et al. Use of abacavir
and risk of cardiovascular disease among HIV
-infected individuals. J Acquir Immune Defic Syndr. 2016;71:413–419.
15. Durand M, Sheehy O, Baril JG, et al. Association between HIV
infection, antiretroviral therapy, and risk of acute myocardial infarction
: a cohort and nested case-control study using Quebec's public health insurance database. J Acquir Immune Defic Syndr. 2011;57:245–253.
16. Desai M, Joyce V, Bendavid E, et al. Risk of cardiovascular events associated with current exposure to HIV
antiretroviral therapies in a US veteran population. Clin Infect Dis. 2015;61:445–452.
17. Sabin CA, Reiss P, Ryom L, et al. Is there continued evidence for an association between abacavir
usage and myocardial infarction
risk in individuals with HIV
? A cohort collaboration. BMC Med. 2016;14:61.
18. Lang S, Mary-Krause M, Cotte L, et al. Impact of individual antiretroviral drugs on the risk of myocardial infarction
in human immunodeficiency virus-infected patients: a case-control study nested within the French Hospital Database on HIV
ANRS cohort CO4. Arch Intern Med. 2010;170:1228–1238.
19. Bedimo RJ, Westfall AO, Drechsler H, et al. Abacavir
use and risk of acute myocardial infarction
and cerebrovascular events in the highly active antiretroviral therapy era. Clin Infect Dis. 2011;53:84–91.
20. Ding X, Andraca-Carrera E, Cooper C, et al. No association of abacavir
use with myocardial infarction
: findings of an FDA meta-analysis. J Acquir Immune Defic Syndr. 2012;61:441–447.
21. Obel N, Farkas DK, Kronborg G, et al. Abacavir
and risk of myocardial infarction
-infected patients on highly active antiretroviral therapy: a population-based nationwide cohort study. HIV
22. Worm SW, Sabin C, Weber R, et al. Risk of myocardial infarction
in patients with HIV
infection exposed to specific individual antiretroviral drugs from the 3 major drug classes: the data collection on adverse events of anti-HIV
drugs (D: A:D) study. J Infect Dis. 2010;201:318–330.
23. Justice AC, Modur SP, Tate JP, et al. Predictive accuracy of the Veterans Aging Cohort Study index for mortality with HIV
infection: a North American cross cohort analysis. J Acquir Immune Defic Syndr. 2013;62:149–163.
24. Gange SJ, Kitahata MM, Saag MS, et al. Cohort profile: the North American AIDS cohort collaboration on research and design (NA-ACCORD). Int J Epidemiol. 2007;36:294–301.
25. Crane HM, Heckbert SR, Drozd DR, et al. Lessons learned from the design and implementation of myocardial infarction
adjudication tailored for HIV
clinical cohorts. Am J Epidemiol. 2014;179:996–1005.
26. Thygesen K, Alpert JS, Jaffe AS, et al. Third universal definition of myocardial infarction
. J Am Coll Cardiol. 2012;60:1581–1598.
27. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604–612.
28. 1993 revised classification system for HIV
infection and expanded surveillance case definition for AIDS among adolescents and adults. MMWR Recomm Rep. 1992;41:1–19.
29. Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for Use of Antiretroviral Agents in Adults and Adolescents Living with HIV
. Department of Health and Human Services. Available at: http://www.aidsinfo.nif.gov/ContentFiles/AdultandAdolescentGL.pdf.Accessed
January 3, 2018.
30. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference
in epidemiology. Epidemiology. 2000;11:550–560.
31. Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV
-positive men. Epidemiology. 2000;11:561–570.
32. Dorjee K, Baxi SM, Reingold AL, et al. Risk of cardiovascular events from current, recent, and cumulative exposure to abacavir
among persons living with HIV
who were receiving antiretroviral therapy in the United States: a cohort study. BMC Infect Dis. 2017;17:708.
33. Young J, Xiao Y, Moodie EEM, et al. Effect of cumulating exposure to abacavir
on the risk of cardiovascular disease events in patients from the swiss HIV
cohort study. J Acquir Immun Def Syndr. 2015;69:413–421.
34. Hsue PY, Hunt PW, Wu Y, et al. Association of abacavir
and impaired endothelial function in treated and suppressed HIV
-infected patients. AIDS. 2009;23:2021–2027.
35. Lai H, Stitzer M, Treisman G, et al. Cocaine Abstinence and reduced use associated with lowered marker of endothelial dysfunction in African Americans: a preliminary study. J Addict Med. 2015;9:331–339.
36. Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am J Epidemiol. 2003;158:915–920.
37. Palella FJ, Althoff KN, Moore R, et al. Abacavir
Use and Risk for Myocardial Infarction
in the NA-ACCORD [abstract 749LB]. Seattle, WA: Conference on Retroviruses and Opportunistic Infections (CROI); 2015.