Increased CVD rates are seen among HIV-infected patients, compared with age-matched non–HIV-infected patients [26
]. The increased burden of CVD among HIV-infected patients is likely a consequence of increased traditional risk factors, including dyslipidemia and insulin resistance, and nontraditional risk factors, such as immune activation and inflammation, that may contribute to an accelerated aging process characterized by higher-than-anticipated rates of noninfectious comorbidities. The ability to accurately predict the degree of cardiovascular risk is therefore an essential element of this population’s future care, and its importance is reflected by an American Heart Association–sponsored State-of-the-Science conference that focused on the topic [27
Traditional cardiovascular risk prediction algorithms developed in non–HIV-infected populations may not accurately predict risk for HIV-infected patients because of potential differences in the etiology of CVD in the HIV population. Indeed, risk prediction tools such as the Framingham Risk Score were not designed for use in populations with HIV infection and were developed for patients with different demographic and clinical characteristics [9
]. Studies in non–HIV-infected populations have demonstrated the Framingham Risk Scores to perform differently in subgroups with different demographic [11
] or geographic characteristics [29
]. To be applicable to HIV-infected populations, a recalibration [11
] may be needed to adjust for underprediction or overprediction. Also, factors unique to HIV infection may influence the performance of a standard risk prediction tool. For example, a change in treatment regimens may impact cardiovascular risk, differential impact of traditional cardiovascular risk factors, and potential contributions to cardiovascular risk of inflammatory and immunologic parameters.
Early investigations of CVD risk in HIV-infected patients used the Framingham data and existing Framingham functions directly to evaluate CVD risk. Two case-control studies [31
] in which HIV-infected patients (cases) were matched to Framingham participants (controls) demonstrated that (1) fat redistribution (increased waist circumference and/or reduced hip and extremity circumferences) in HIV was associated with elevated metabolic CVD risk factors; (2) the Framingham Risk Score [9
] was elevated in HIV-infected patients with fat redistribution; (3) controls matched on the basis of age, sex, body mass index, and waist and hip circumferences were at the same risk; and (4) HIV-infected subjects without fat redistribution were not at elevated coronary heart disease risk. The studies provided useful insights into CVD risk among HIV-infected subjects and used the Framingham equations to estimate the risk contributed by the increased prevalence of traditional risk factors for CVD in the HIV-infected population.
Subsequent cross-sectional studies assessed the degree of agreement of predicted risk probabilities or risk predictions (but not the actual 10-year outcomes) for different cardiovascular risk equations among HIV-infected patients. Three studies have assessed the degree of correlation among the Framingham, Systematic Coronary Risk Evaluation (SCORE), and Prospective Cardiovascular Münster (PROCAM) equations. There was significant concordance among the scores in a cross-sectional Spanish HIV-infected cohort, with observed agreement of 84% between Framingham and PROCAM (κ = 0.36), 83% between Framingham and SCORE (κ = 0.32), and 93% between PROCAM and SCORE (κ = 0.46) [33
]. In a similar study from Brazil, there was moderate agreement between the Framingham and PROCAM equations (κ = 0.43) and between the PROCAM and SCORE equations (κ = 0.48), but less agreement between the Framingham and SCORE equations (κ = 0.22) [34
]. A comparison of the Framingham and PROCAM equations at 2 HIV referral centers in Brazil showed good agreement between the scores (κ = 0.64) [35
]. In another cross-sectional study of a Thai HIV-infected cohort, predicted 10-year risk of coronary heart disease was higher for the Framingham risk equation, compared with both the Ramathibodi-Electricity Generating Authority of Thailand (Rama-EGAT) equation and the Data Collection on Adverse Effects of Anti-HIV Drugs (D:A:D) equation (see below), with differences between the 3 scores more pronounced in patients with a higher predicted risk [36
]. These studies compare different risk functions with one another but give no real answer as to how well the functions will do in actual risk prediction. The agreement of the order of magnitude of 80% or so indicates good agreement. However, the low κ values of 0.30–0.40 demonstrated in some of the comparisons highlight the dangers of trying to understand the usefulness of the Framingham Risk Function in such cross-sectional comparisons. Furthermore, given that the comparisons were not of the actual outcomes (events), the usefulness of this approach in general is questionable [36
While there is relatively good concordance among risk prediction tools for HIV-infected patients, it is not clear that these tools accurately predict risk. The D:A:D study applied the Framingham risk equation [10
] to an HIV-positive observational cohort of >23 000 patients to compare observed versus predicted events on the basis of duration of antiretroviral therapy [37
] (). Several methods were employed in order to compare event rates: predictions were extrapolated to provide 10-year risk estimates; missing covariates were imputed; all patients were assumed not to have left ventricular hypertrophy, owing to the absence of data for this variable; and patients with prior CVD were included but considered to be at already increased risk. For patients receiving antiretroviral therapy, observed acute MI events were higher than predicted, yet in patients not receiving antiretroviral therapy, observed acute MI events were lower than predicted (although there were only 3 observed events in this group). For all groups, however, the confidence limits overlapped, suggesting that the Framingham equation did not significantly underpredict events in patients receiving antiretroviral therapy. However, the study was too small, with only 129 myocardial infarctions, to be definitive and did not resolve whether the Framingham equation is useful for predicting CVD events in the HIV-infected population.
Figure 1. Observed and predicted rates of myocardial infarction by duration of combination antiretroviral treatment (CART). Observed rates are observed number of myocardial infarctions (MIs) divided by person-years of follow-up. Predicted rates are the sum of estimated (more ...)
Additionally, several studies have explored the association between the Framingham risk equation and measures of subclinical atherosclerosis among HIV-infected patients [36
]. It should be noted that the Framingham functions do not attempt to predict subclinical disease. Therefore, assessment of the usefulness of the Framingham risk equation for predicting subclinical atherosclerotic disease in HIV-infected persons goes beyond the intended purpose of the Framingham equation and does not reflect appropriate use of the equation.
In light of the uncertain data, illustrated above, concerning the usefulness of traditional cardiovascular risk prediction tools to accurately predict risk for HIV-infected patients, several groups have developed cardiovascular risk prediction models tailored to HIV-infected populations. On the basis of 5 cohorts of non–HIV-infected men, investigators developed a prognostic model for the outcome of coronary heart disease (defined by International Classification of Diseases code) tailored to changes in risk factors typically observed in patients starting antiretroviral therapy [38
]. The model included variables such as body mass index and fasting blood glucose level, traditional cardiovascular risk factors that are commonly seen among HIV-infected patients. Hazard ratios for the model, however, were derived from non–HIV-infected men and extrapolated to HIV-infected patients. Moreover, the prognostic model was limited to traditional cardiovascular risk factors and did not include factors specific to HIV infection, including indices of inflammation and fat redistribution, that are increasingly recognized to play a pivotal role in HIV-associated CVD.
More recently, the D:A:D group took an important step toward addressing the issue of HIV-specific risk prediction by developing a cardiovascular risk equation on the basis of covariates derived from a large HIV-infected cohort [39
]. By use of data from >20
000 patients in a prospective observational cohort, most of whom were in developed countries, models that included exposure to HIV medications (indinavir, lopinavir/ritonavir, and abacavir), as well as traditional cardiovascular risk factors, were developed to predict several cardiovascular outcomes. CD4 cell count and HIV RNA load were considered as covariates but did not achieve statistical significance. The performance of the models was assessed by discrimination (the area under the receiver operating curve was 0.78, 0.78, and 0.77 for acute myocardial infarction, coronary heart disease, and CVD end points, respectively) and calibration (the ratio of predicted to observed events was 0.97, 0.96, and 0.95 for acute MI, coronary heart disease, and CVD end points, respectively) and was found to be similar to the Framingham equation [10
] in terms of ordering patient risk (discrimination) but superior to the Framingham equation in terms of accurately predicting risk (calibration). Importantly, the risk equation was validated in the same data set from which it was derived rather than in an independent data set, although the investigators employed an internal-external cross-validation (ie, “test and hold”) technique, in which models were developed from one subcohort and validated in another, to mimic external validation. Further limitations of the analysis comparing the D:A:D-derived and Framingham equations include shorter follow-up times for D:A:D patients, necessitating extrapolation to 5-year predicted risks; differing end point definitions; exclusion of patients without a complete risk factor profile; and inclusion of a limited number of outcomes in women, precluding the development of sex-specific equations. Also, the Framingham equation used for comparison to the D:A:D was not ideal for the comparison. For example, it did not contain a variable for hypertension medication, an important predictor of outcomes, and its outcome did not match the outcomes used in the D:A:D analysis. The latter had cardiac procedures in the outcome, whereas the Framingham function did not, and the Framingham function had silent MIs as one of its outcomes, whereas the D:A:D did not.