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Keywords:

  • antiretroviral therapy;
  • cardiovascular disease;
  • HAART;
  • protease inhibitors;
  • treatment complications

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Objectives

To study the relationship between exposure to protease inhibitor (PI) therapy and increased risk of cardiovascular events in HIV-infected patients.

Methods

We estimated the risk of cardiovascular disease (CVD) events with PI exposure in a cohort of HIV-infected patients using a time-dependent Cox proportional hazards model adjusting for the major CVD risk factors. Only the first CVD event for each subject was counted.

Results

Of a total of 7542 patients, 77% were exposed to PIs. CVD event rates were 9.8/1000 and 6.5/1000 person–years of follow-up (PYFU) in the PI-exposed and nonexposed groups, respectively (P=0.0008). PI exposure ≥60 days was associated with an increased risk of CVD event [adjusted hazards ratio (HRadj) 1.71; 95% confidence interval (CI) 1.08–2.74; P=0.03]. Results from a subgroup of patients aged between 35 and 65 years were similar (HRadj 1.90; 95% CI 1.13–3.20; P=0.02). Other significant risk factors included smoking status, age, hypertension, diabetes mellitus and pre-existing CVD.

Conclusions

Patients exposed to PI therapy had an increased risk of CVD events. Clinicians should evaluate the risk of CVD when making treatment decisions for HIV-infected patients.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Highly active antiretroviral therapy (HAART) has resulted in dramatic declines in progression to AIDS and AIDS mortality in HIV-infected persons [1–6]. In some reports, death from nonAIDS-related causes exceed those attributed to AIDS in this patient population [2,7–9], and cardiovascular deaths are increasing in these patients [1,2]. Recent reports suggest a shift in the relative cause of death among HIV-infected individuals, with cardiovascular deaths accounting for fewer than 4% of all deaths pre-1997 [1,10], and for 7–10% in more recent years [8,10,11].

Reports from large observational studies demonstrate that considerable controversy exists over the association of HAART, particularly protease inhibitor (PI) therapies, with increased cardiovascular disease (CVD) risk [12–17]. PIs have been associated with alterations in surrogate markers of CVD, including coronary calcium scores and endothelial function [18,19], as well as with metabolic complications such as hyperlipidaemia, fat redistribution, insulin resistance, hypertension and diabetes mellitus [19–24]. Also, HIV-infected patients may have a higher prevalence of traditional CVD risk factors such as smoking than the general population [20,21]. In addition, as the mean age of the HIV-infected patient population has increased as a result of longer life expectancy with the disease, the consequent cardiovascular risk has also increased.

These changes in patient demographics and the increasing prevalence of traditional CVD risk factors such as smoking, along with the increasing prevalence of PI-associated metabolic complications, have clearly increased the risk of CVD in HIV-infected individuals. In this paper, we use a large longitudinal database to address the question: does PI exposure increase the risk of CVD in HIV-infected patients after adjusting for known CVD risk factors?

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

Data source

The study population was derived from HIV Insight™ (Cerner Corporation, Vienna, VA, USA), a prospective observational database of HIV-infected patients, consisting of primary care clinicians' outpatient medical records. Data were collected through chart abstraction and entered into a proprietary software system called Clinical Practice Analyst (CPA; Cerner Corporation). Diagnoses, drug treatments, symptoms and laboratory results documented in each physician encounter were entered into the database, as well as patient demographics, patient risk factors, patient history and reasons for ending drug therapies. Data collection began in 1991, and, as of December 2002, there were over 13 000 patients in the database representing over 500 000 patient-months of observation.

The data source comprises all centres funded by the Centers for Disease Control and Prevention (CDC) HIV Outpatient Study (HOPS) and additional physician offices and clinics funded by the sponsoring agency, Cerner Corporation. There are currently 19 sites (10 from HOPS) participating in ongoing abstraction of patient medical records. On average, 55.5% of patients in the database are from HOPS sites. Abstraction was carried out in a similar manner across all centres by trained staff and data were entered electronically into the database. Each month, the sponsoring agency combined the data from all sites to create the HIV Insight™ database. Also each month, data quality control processing was undertaken to identify erroneous, suspicious, or missing data and error reports were sent to sites for clarification or correction. Quality control processing programs are updated on a regular basis. Once a year, on-site audits were conducted to verify the completeness and accuracy of a sample of patient records. Data for this analysis were derived from an extract of patients with more than two office visits from 1 January 1996 to 30 June 2003. Additional information on the HOPS study has been previously published [6,22].

Patient selection and variable definition

Adult HIV-infected patients aged ≥18 years with at least two medical visits between 1 January 1996 and 30 June 2003 were eligible for this analysis. All demographic and risk factor variables were determined at the beginning of follow-up. The index date was defined as the first date of PI use (PI-exposed group) or the first date of any antiretroviral therapy use (nonPI-exposed group) during the follow-up period. PI exposure was categorized in two ways; first as a cumulative exposure ≥60 days, and secondly as a progressively increasing exposure duration (1 to <180 days, 180 to <365 days, ≥365 days). Exposure over 60 days was chosen to reflect patients likely to be on a stable PI regimen. Patients starting PI therapy during the observation period could only be included in the PI-exposed group.

CVD events were defined as any one of the following: acute myocardial infarction (AMI), angina pectoris, coronary artery disease (CAD), percutaneous transluminal coronary angioplasty (PTCA), coronary artery bypass graft (CABG), cerebrovascular accident (CVA), transient ischaemic attack (TIA) and peripheral vascular disease (PVD). All CVD events for this analysis were as diagnosed by the treating clinician, and each was verified by review of hospital discharge records and clinic charts. Risk factors for CVD events were defined as follows: hyperlipidaemia was defined as a physician diagnosis, use of lipid-lowering therapy or any of the following criteria based on the National Cholesterol Education Program III (NCEP III) guidelines [23]: total cholesterol ≥240 mg/dL, low-density lipoprotein cholesterol (LDLC) ≥130 mg/dL with two other CVD risk factors, LDLC ≥160 mg/dL, or triglyceride (TG) ≥300 mg/dL. The fasting state of all patients for the lipid levels could not be confirmed. As TG levels can increase significantly with meals [24], we selected a cut-off of 300 mg/dL to increase the specificity of the TG level. Hypertension and diabetes mellitus were defined as a diagnosis or treatment with an antihypertensive or diabetic medication within 365 days prior to index date, respectively. Smoking status was categorized as current smoking, past smoking and never smoked. Other variables of interest were intravenous drug use (yes or no), and cocaine use (yes or no). Weight was defined as weight in pounds, and was a continuous variable. Information on family history was not available in this database and therefore not used in this analysis.

Data analysis

CVD event rates were calculated per 1000 person–years of follow-up (PFYU) for both exposure groups. Univariate analyses were performed using χ2 tests (for categorical variables), Student's t-test (for continuous variables with normal distribution), nonparametric tests (for nonnormally distributed continuous variables) and univariate Cox regression models for unadjusted estimates of CVD risk. A time-dependent Cox proportional hazards multivariable model was used to compute the relative hazard of a first CVD event. Patients who did not develop an outcome of interest at the end of follow-up were treated as censored. The PI exposure variables were defined as time-dependent variables that were updated at each event or censoring. Covariates for which data were adjusted were age category, gender, race, smoking status, intravenous drug use (IVDU), cocaine use, hypertension, diabetes mellitus, pre-existing CVD, pre-existing hyperlipidaemia and weight. Weight was imputed for a small minority of patients (<5%) with missing information using the mean weight from the patient cohort with complete weight data. The primary analytic dataset comprised all 7542 patients, and analyses were also repeated in a subset of patients aged between 35 and 65 years (n=5200).

Breslow's approximation method was used in all the analyses to handle ties in the CVD event time. The EXACT method was also tried, and yielded results similar to those obtained with Breslow's method. The proportional hazards assumption was confirmed for each covariate. Covariate selection for the models was based on clinical consensus regarding known risk factors for CVD and potential risk factors in the HIV-infected population.

All statistical analyses were performed using SAS version 8.1 (SAS Institute, Inc., Cary, NC, USA).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

A total of 7542 patients met the enrolment criteria. The median duration of follow-up was 3.5 years (mean 3.5 years; maximum 7.4 years) and 2 years (mean 2.5 years; maximum 7.4 years) for the PI and nonPI groups, respectively. The median PI exposure time was 1.7 years (mean 2 years). Over 95% of the study patients in the PI arm had a minimum of 1 month of PI exposure, and over 75% had greater than 6 months of PI exposure. In the nonPI group, 67% were exposed to nonnucleoside reverse transcriptase inhibitors (NNRTIs), and the others were treated with nucleoside reverse transcriptase inhibitors (NRTIs) only.

As shown in Table 1, significant demographic differences were observed between the exposure groups. Significant differences were also found for the following risk factors: current smoking (more common among the nonPI group); hypertension (more common among the nonPI group); pre-existing hyperlipidaemia (more common in the PI group).

Table 1.  Demographics and risk factor distribution
CharacteristicPI group (n=5787)NonPI group (n=1755)P-value
  1. IQR, interquartile range; SD, standard deviation; IVDU, intravenous drug use; CVD, cardiovascular disease; NS, not significant.

Median duration of follow–up (years) (IQR)3.5 (1.67–5.46)2.0 (0.75–3.89)<0.0001
Age (years) [mean (SD)]39.4 (8.30)38.7 (8.90)0.0012
Age (years) [n (%)]
 18–341679 (29%)604 (34%)<0.0001
 35–493461 (60%)951 (54%)<0.0001
 50–64600 (10%)188 (11%)NS
 65+47 (1%)12 (1%)NS
Gender [n male (%)]5078 (88%)1402 (80%)<0.0001
Race [n (%)]
 White3501 (61%)883 (50%)<0.0001
 African American1430 (25%)587 (33%)<0.0001
Smoking status [n (%)]
 Current1953 (34%)678 (39%)0.0002
 Past787 (14%)230 (13%)NS
Weight (kg) [mean (SD)]169.1 (32.8)170.1 (33.1)NS
IVDU [n (%)]42 (1%)17 (1%)NS
Cocaine use [n (%)]110 (2%)39 (2%)NS
Hypertension [n (%)]271 (5%)109 (6%)0.01
Diabetes mellitus [n (%)]65 (1%)16 (1%)NS
Pre-existing CVD [n (%)]7 (<1%)1 (<1%)NS
Pre-existing hyperlipidaemia [n (%)]553 (10%)83 (5%)<0.0001

A total of 127 CVD events were observed, with 112 in the PI group for an adjusted event rate of 9.8/1000 PYFU, and 15 in the nonPI group for an adjusted event rate of 6.5/1000 PYFU (P=0.0008). Among patients in the 35 to 65-year-old subset, CVD event rates were also higher in the PI group (11.5/1000 PYFU vs. 7.9/1000 PYFU; P=0.01).

In univariate analyses, cumulative PI therapy for ≥60 days, current or past smoking, age 35–49, 50–64 or ≥65 years, and prior history of hypertension, diabetes, CVD or hyperlipidaemia were significantly associated with higher CVD event risk (Table 2). In the multivariate regression model adjusting for all risk factors, cumulative PI therapy for ≥60 days was associated with an increased risk of CVD events (P=0.03) (Table 2). Other independent risk factors for CVD events were: current smoking (P<0.0001), past smoking (P=0.03), age 35–49 years (P=0.004), age 50–64 years (P=<0.0001), age ≥65 years (P<0.0001) (reference group: age 18–34 years), hypertension (P=0.03), diabetes mellitus (P=0.0006) and pre-existing CVD (P<0.0001). Gender, pre-existing hyperlipidaemia, race, cocaine use, IVDU and weight were not significantly associated with CVD events.

Table 2.  Cox proportional hazards regression model: significant predictors of time to first cardiovascular disease event for all patients (n=7542)
Risk factorCVD events [n (%)]Univariate analysis HR (95% CI)Multivariate analysis HR (95% CI)
Yes (n=127)No (n=7415)
  1. Other variables that were adjusted for but were not significant predictors included gender, race, weight, cocaine use and intravenous drug use.

  2. CVD, cardiovascular disease; PI, protease inhibitor; HR, hazards ratio.

PI exposure≥60 days
 Yes105 (82.7)4707 (63.5)1.69 (1.07–2.68)1.71 (1.07–2.74)
 No22 (17.3)2708 (36.5)ReferenceReference
Smoking
 Current56 (44.1)2575 (34.7)1.62 (1.14–2.30)2.40 (1.59–3.64)
 Past29 (22.8)988 (13.301.80 (1.18–2.69)1.74 (1.06–2.84)
 Never42 (33.1)3852 (52)ReferenceReference
Age (years)
 35–4965 (51.2)4347 (58.6)2.90 (1.53–5.49)2.57 (1.35–4.88)
 50–6442 (33.1)746 (10.1)10.93 (5.63–21.23)8.09 (4.04–16.19)
 ≥659 (7.1)50 (0.7)38.97 (16.14–94.07)32.04 (12.94–79.34)
 < 3511 (8.7)2272 (30.6)ReferenceReference
Hypertension
 Yes19 (15)361 (4.9)3.68 (2.26–5.99)1.80 (1.07–3.03)
 No108 (85)7054 (95.1)ReferenceReference
Diabetes mellitus
 Yes5 (3.9)76 (1)5.25 (2.15–12.86)3.59 (1.44–8.95)
 No122 (96.1)7339 (99)ReferenceReference
Evidence of pre-existing CVD
 Yes8 (6.3)0 (0)69.17 (33.7–141.94)19.88 (8.68–45.55)
 No119 (93.7)7415 (100)ReferenceReference
Evidence of hyperlipidaemia
 Yes21 (16.5)615 (8.3)1.63 (1.02–2.60)1.11 (0.69–1.80)
 No106 (83.5)6800 (91.7)ReferenceReference

In a model defining age as a continuous variable, increasing age was associated with a HRadj of 1.09 (95% CI 1.08–1.11) for CVD events. PI exposure was still associated with risk of CVD event with a HRadj of 1.75 (95% CI 1.09–2.78). When all nonsignificant variables were removed from the model, there was no change in the hazards ratio.

Sensitivity analyses

Several sensitivity analyses were conducted to test the robustness of the findings (Fig. 1). In a multivariate regression model for the subset of patients aged 35–65 years (n=5200), adjusting for all risk factors, cumulative PI exposure ≥60 days was also associated with an increased risk of CVD (HRadj 1.90; 95% CI 1.13–3.20). Follow-up time for the PI group was truncated in two different analyses to achieve the same median and mean follow-up times as in the nonPI group. As shown in Fig. 1, PI exposure ≥60 days was still associated with increased risk of CVD events [(analysis with similar median follow-up times HRadj 1.53; 95% CI 0.79–2.95) and (analysis with similar mean follow-up times HRadj 2.07; 95% CI 1.18–3.66)].

image

Figure 1. Cox proportional hazards regression models sensitivity analyses of protease inhibitor (PI) exposure ≥60 days and adjusted risk of cardiovascular disease (CVD) events. Reference groups for all analyses were the non-PI-exposed groups. All models were adjusted for age, gender, ethnicity, smoking status, weight, cocaine use, intravenous drug use, hypertension, diabetes mellitus, pre-existing CVD and hyperlipidaemia. f/u, follow up.

Download figure to PowerPoint

The PI exposure variable was redefined to determine whether increasing exposure duration had an impact on CVD risk (Fig. 2). Three exposure categories were defined as follows: PI exposure of 1 to <180 days (group A); 180 to <365 days (group B); ≥365 days (group C). In the multivariate regression model adjusting for all risk factors, a dose effect was observed, with only group C patients having an increased risk of CVD events (HRadj 1.51; 95% CI 0.98–2.32). Applying this model to the 35 to 65-year-old subset resulted in similar results, with group C patients having an increased risk of CVD events (HRadj 1.63; 95% CI 1.01–2.63).

image

Figure 2. Cox proportional hazards regression models sensitivity analyses of protease inhibitor (PI) exposure for duration 1 to <180 days (group A), 180 to <365 days (group B) and ≥365 days (group C), and adjusted risk of cardiovascular disease (CVD) events. Reference groups for all analyses were the nonPI-exposed groups. All models were adjusted for age, gender, ethnicity, smoking status, weight, cocaine use, intravenous drug use, hypertension, diabetes mellitus, pre-existing CVD and hyperlipidaemia.

Download figure to PowerPoint

With all nonsignificant variables removed and age defined as a continuous variable, additional analyses were run with the outcome variably defined as (1) only AMI (31 events) or (2) a composite endpoint of CHD (including AMI, PTCA, CABG, angina and CAD) (93 events). In these analyses, the HRadj (95% CI) for PI exposure was 1.76 (0.66–4.64) for AMI and 1.88 (1.07–3.31) for CHD. The distribution of all CVD events is shown in Table 3.

Table 3.  Distribution of cardiovascular disease events
CVD event [n (%)]PI group (n=112)NonPI group (n=15)
  1. CVD, cardiovascular disease; AMI, acute myocardial infarction; CAD, coronary artery disease; CVA, cerebrovascular accident; TIA, transient ischaemic attack; PVD, peripheral vascular disease; PTCA, percutaneous transluminal coronary angioplasty; CABG, coronary artery bypass graft.

AMI25 (22)5 (33)
Angina pectoris32 (29)3 (20)
CAD23 (21)3 (20)
CVA17 (15)2 (13)
TIA9 (8)1 (7)
PVD4 (4)1 (7)
PTCA0 (0)0 (0)
CABG2 (<1)0 (0)

Additional sensitivity analyses were performed to test whether measures of HIV-infection duration and disease history, such as nadir CD4 T-cell count, duration of known positive HIV test and cumulative NRTI exposure, could account for the observed effect. Adding all three variables to the base model, the estimated HRadj (95% CI) for PI exposure was 2.7 (1.4–5.1). Additionally, we tested whether calendar year of index might modify the observed effect, and this did not change the results.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The results of this analysis suggest an increased CVD event rate in HIV-infected patients exposed to PI therapies. In multivariable analyses adjusting for most major CVD risk factors, there was an increased risk of CVD events in patients exposed to PI therapies. The observed increased risk was statistically significant in the primary and subgroup analyses.

The sensitivity analyses conducted showed similar increased risk estimates for the PI-exposed group, even after follow-up was truncated in the PI group to achieve similar median and mean follow-up times. However, the 95% CI of the risk estimate from the model truncating follow-up time to the same median included 1. Also, when measures of disease duration and disease history (such as nadir CD4 T-cell count, duration of known HIV-positive test and cumulative NRTI exposure) were tested in the model, there was no change in the direction and significance of the PI effect.

As expected, the major CVD risk factors were significant independent risk factors for CVD in this population of patients. The strongest risk was associated with increasing age category and pre-existing CVD events. The observed increased risk of CVD events associated with PI exposure is probably clinically meaningful as most major CVD risk factors, with the exception of family history, were adjusted for in this analysis.

We cannot fully explain the absence of a gender effect in this analysis. Besides the relatively low proportion of females in this analysis, an explanation for this might be the more pronounced metabolic derangement associated with HAART therapy seen in female patients when compared to male patients [25]. Also, in the United States, HIV-infected male patients with hyperlipidaemia may be more likely to receive lipid-lowering agents than infected female patients [26]. These factors might combine to alter the relative risk of a CVD outcome in women compared to men; hence the CVD risk differences between the genders might not be as evident in treated HIV-infected persons as is seen in the general population.

We have not accounted for the subsequent development of diabetes and hyperlipidaemia after the index date, as the development of these abnormalities has been linked to the use of PI agents, and is expected to be in the causal pathway for eventual CVD disease. Not adjusting for these abnormalities is not therefore likely to introduce any bias in the analyses performed.

Compared to some other recent analyses [14,16], a greater number of major CVD risk factors were accounted for. We also defined the outcome events as would be done in a clinical practice setting, with all abstracted events verified by independent medical abstractors from the sponsoring agency for the database using discharge records and clinic records. Although other studies have shown a relationship between antiretroviral drugs and MI, to our knowledge there are very few studies on other cardiovascular outcomes in this patient population. We feel that this is a more clinically relevant analysis, as events such as CVA, TIA and PVD confer considerable morbidity upon the patient. As shown in the sensitivity analyses, it is likely that models based on aggregate endpoints will provide different estimates of risk from models based upon myocardial infarction alone. Removal of all noncoronary events (CVA, TIA and PVD) from the analyses yielded similar risk estimates to the primary analysis. When only MI was modelled, there were too few events to allow any conclusions to be drawn from this study.

We sought to expand the analytical database beyond the earlier published data from the HOPS investigators [15]. This earlier publication showed an association between PI exposure and MIs, but results from the adjusted Cox model had a wide 95% confidence interval and were not statistically significant. By expanding the dataset and including nonMI events, we increased the observation time and statistical power of this study. We were also able to conduct additional analyses, such as the dose–response analyses, not performed in the earlier publication.

Our findings differ from those recently reported from a large study using data from the US Veteran's Administration (VA) [16]. This might be a result of methodological differences as well as differences in the populations studied. In that study [16], CVD admission trends were analysed over time, with only 42% of the patients ever taking PIs compared to 77% in our study. The data source for that study was also different; in our study, CVD events were derived from abstracted medical records, whereas in the VA study CVD events were based on hospital diagnosis codes from administrative databases.

Our findings are consistent with those of some recently published studies [12,14,15,17,27]. Four of these studies reported specifically on MI as the outcome of interest. A French hospital database study of HIV-infected men found that men exposed to PI therapy had an increased risk of MI (HRadj 2.56; 95% CI 1.03–6.34) [14]. The duration of PI exposure was associated with significantly increased MI incidence in a dose-dependent relationship. Patients exposed for more than 30 months had the highest MI incidence rate. In the HOPS study discussed above, MI incidence increased annually following the introduction of PIs in 1996 [15]. PI exposure was associated with an adjusted hazards ratio of 6.51 (95% CI 0.89–47.8) for MI. Published data from the Data Collection on Adverse Events of Anti-HIV Drugs (DAD) study group showed an increased risk of MI for each year on combination antiretroviral therapy (RRadj 1.26; 95% CI 1.12–1.41) [12]. The Kaiser hospitalization study has recently reported an association between PI exposure and risk of hospitalization for MI (HRadj 4.1; P<0.0001) and CHD (HRadj 3.37; P<0.0001) [17]. The most recent data from the DAD study were for outcomes of cardiovascular and cerebrovascular events, and showed an association with increasing combination antiretroviral therapy use (RRadj per year of exposure: 1.26, 95% CI 1.15–1.38) [27].

There are limitations to the current analysis. First, we observed a difference in the follow-up times between exposure groups. Longer follow-up in the PI group could lead to a biased estimate of effect. We tested this bias by truncating the follow-up in the PI group to obtain similar follow-up for the exposure groups and repeated the primary analysis. As already discussed, the HR for this analysis was similar to that from the primary analysis, indicating that the adjusted incidence rate and time-dependent analysis accounted for the differential follow-up. Secondly, to the extent that clinicians are influenced by the association of PI therapies with metabolic disturbances, an ascertainment bias could explain the findings in this analysis. Conversely, clinicians may also fail to initiate or continue PI therapy in patients at high risk of CVD events, thereby introducing a bias towards the null.

Detailed historical information on exposure to antiretroviral therapy prior to enrolment in the database is not available for all the patients. This has the potential to bias the result towards the null, as patients with unknown prior PI exposure could be classified as never exposed, resulting in a misclassification. Incomplete baseline risk factor information is also of concern. However, any missing information is likely to be randomly distributed among all patients, which should not bias the relative risk estimates. Use of lipid-lowering therapy was not accounted for in this analysis. Lipid-lowering therapy is, however, more likely to be used in PI-treated patients [28,29], which is expected to exert a bias towards the null. Patients on PI drugs who develop metabolic complications may not only be prescribed a lipid-lowering drug, but be switched to a PI-sparing regimen, thus diminishing the risk for CVD events. Our analysis does not account for such switches in therapy, and these patients would remain classified as PI-exposed in our analysis, which would also bias the results towards the null. Lastly, the period of our analyses (1 January 1996 to 30 June 2003) limits the interpretation of our results to the antiretroviral medications available during that period.

In conclusion, our results suggest an association between PI exposure and increased risk of CVD. This association was shown in the overall population as well as in a subset of patients aged between 35 and 65 years. Although the effect of PI treatment on CVD risk was evident in this analysis, the demonstrated benefits of HAART therapy still outweigh the risk of subsequent cardiovascular events [30]. However, a combination of several factors, such as longer life expectancy, higher smoking rates among HIV-infected persons and prolonged exposure to PI-based HAART regimens, may lead to greater CVD event rates, especially as this population continues to age. Further studies are needed to confirm this association as well as to identify the causal pathway. Finally, we believe that the evaluation of CVD risk profiles of patients initiating or currently on HAART should be part of clinical practice. Nonpharmacological and pharmacological steps should be taken to address modifiable risk factors such as smoking, hyperlipidaemia, hypertension and diabetes in patients at risk of future CVD events.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References

The authors wish to thank Rose Baker from Cerner Corporation (Vienna, VA, USA) for substantive consultations regarding the HIV InsightTM database.

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  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
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