INTRODUCTION
After the introduction of combination antiretroviral therapy (ART) in 1996, Kaposi sarcoma (KS) incidence among HIV-infected persons sharply declined,1 2–4 5 6
KS-associated herpesvirus (KSHV) infection is a necessary but not solely sufficient cause of KS.7,8 + T-cell count (CD4) and KS risk is well established.9 10–18 19,20 21
Although many studies have examined relationships between KS risk and CD4, HIV-1 RNA viral load (VL), and ART use, several questions remain unanswered. First, over the course of HIV infection , when is CD4 most predictive of KS risk? KS risk has been observed to be inversely associated with baseline,22–24 11,25–30 31 32 11,26
Second, is VL associated with KS risk independent of CD4, and if so, which VL measure is the best predictor? A direct independent association between recent/current VL and KS risk has been observed in some studies,11,26,29 25,27 26,32
Third, is the protective effect of ART on KS risk entirely mediated by its effects on CD4 and VL? KS risk is clearly lower in ART users vs. nonusers1,24,26,30,31 27,28,33,34 35,36 11,25–27,30,31
Few studies have comprehensively examined CD4 and VL measures, as well as ART use, to determine independent predictors of KS risk.26
METHODS
Study Sample
The study sample included adults (≥18 years) followed during 1996–2009 from 18 United States and Canadian cohorts participating in the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD; see Table S1, Supplemental Digital Content, https://links.lww.com/QAI/B8 37 6
For our analytic data set, we defined entry date (ie, baseline) for each person as the latest of the following dates: January 1, 1996, cohort-specific start for reporting cancer diagnoses, 18th birthday, or 360 days before the later of the first CD4 or VL laboratory measurement. We defined exit date as the earliest of cohort-specific end for reporting cancer diagnoses, death, KS diagnosis, or the earlier of the last CD4 or VL laboratory measurement plus 540 days. We then excluded persons with <2 CD4 or <2 VL measurements or with <180 days of follow-up.
Estimation of Time-updated CD4 and VL Values
The timing and frequency of observed CD4 and VL laboratory measurements varied by person. To standardize, for each person we estimated time-updated CD4 or log10 (VL) values at 30-day intervals using linear interpolation between consecutive pairs of CD4 or VL laboratory measurements, respectively. Moreover, we carried the first CD4 or VL laboratory measurement backward 360 days or to the entry date, whichever was reached first. Similarly, we carried the last laboratory measurement forward 360 days or to the exit date (see Figure S1, Supplemental Digital Content, https://links.lww.com/QAI/B8
Statistical Methods
We sought to systematically evaluate a variety of CD4 and VL measures (ie, recent, lagged, trajectory, and cumulative) to develop a final model that included the most robust CD4 and/or VL predictors of KS risk. We modeled relationships between KS risk and CD4 or VL measures using proportional hazards regression. Our time metric was months (30 days) from baseline; thus, all time metrics were 30-day multiples.
CD4 and VL measures were updated at the beginning of each 30-day interval from baseline but were lagged by 180 days (defined as “recent”) to reduce the possibility that the presence of KS influenced CD4 or VL (ie, reverse causality). We also considered lag intervals of 540, 900, or 1260 days. These analyses were, by necessity, restricted to persons with at least the amount of follow-up corresponding with the lag. For example, it is impossible to assess VL lagged by 900 days among persons with <900 days of follow-up.
Among persons with at least 1260 days of follow-up, we then examined the following time-updated measures during a 1080-day time window (approximately 3 years) lagged by 180 days: CD4 or log10 (VL) trajectory (ie, linear regression slope); proportion of time CD4 <200 cells/μL or VL >500 copies/mL; and CD4 or VL time-weighted mean (see Figure S2, Supplemental Digital Content, https://links.lww.com/QAI/B8
We modeled measures as categorical variables to allow for the possibility of nonlinear relationships with KS risk. We also modeled measures as continuous variables, calculating P values for trend. We adjusted all models for sex, race/ethnicity, cohort, and baseline age and calendar period.
Analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC).
Model Selection to Develop Final Model
For model selection, we used Akaike's information criterion (AIC), a quantity commonly used to compare models. AIC takes into account both goodness of fit (ie, discrepancy between observed values and modeled values) and model parsimony. A lower AIC indicates the preferred model, with an AIC decrease of >10 between 2 models considered meaningful.38 P values for categorical variables were also taken into account for model selection.
To develop our final model, we first fit a separate Cox model for each CD4 and VL measure (1 measure per model). Second, we selected the best CD4 measures by comparing models that included 2 or more CD4 measures in the same model. Third, we selected the best VL measures in the same manner. Finally, we chose our final model by comparing models that included 2 or more of the best CD4 and VL measures together in the same model.
In the final model, we estimated the percent of the association between cumulative (time-weighted mean) VL and KS risk mediated by recent CD4 by calculating the quantity (βunadj − βadj )/βunadj , where βunadj is the coefficient for cumulative VL unadjusted for recent CD4 and βadj is the coefficient adjusted for recent CD4.39
ART Models
Among persons ART naive at baseline, we examined the relationship between KS risk and time-updated proportion of time on ART during the 1080-day time window lagged by 180 days. We expected to observe confounding by indication because it was the standard clinical practice during the study period to base ART initiation on CD4.40 would be based on if the person was ART naive at the start of the time window or was based on if the person had initiated ART before the start of the time window. In either case, this CD4 variable could not be a mediator because it was measured before or at the start of the time window of the ART measure. To determine whether the ART effect was mediated by the CD4 and VL measures in our final model, we added these measures to the ART model, still adjusting for confounding by CD4.
Sensitivity Analyses
For the primary analyses, we imputed race/ethnicity for the 5.5% of persons with unknown values using cohort-specific probability weights according to sex and baseline age and calendar year. We did not adjust for HIV risk group or smoking because of large numbers of unknowns. However, for sensitivity analyses, we examined the effect of including each of these variables as a covariate either using multiple imputation or by including unknowns as a category.
RESULTS
The 18 NA-ACCORD cohorts included 97,323 persons. We then excluded persons with no follow-up time according to our entry/exit date criteria (N = 6061), with <2 CD4 or <2 VL laboratory measurements (N = 10,678), or with <180 days of follow-up (N = 2888). The remaining 77,696 persons were 85% male, 44% white, and 41% black (Table 1 ). At baseline, most persons were aged 30–49 years (69%) and ART naive (72%), with CD4 ≥200 cells/μL (67%) and VL >500 copies/mL (73%). Persons who eventually developed KS (N = 396) were more likely to be male, white, aged 30–39 years, have a baseline calendar period of 1996–1998, have lower CD4, and higher VL, and to be a man who has sex with men (Table 1 ). Median follow-up was 5.0 years (interquartile range: 2.6–8.8 years). Median number of observed CD4 and VL laboratory measurements per person was the same: 14 (interquartile range: 6–26).
TABLE 1.: Baseline Characteristics of Persons Contributing Observation Time, NA-ACCORD, 1996–2009
Recent and Lagged Measures
We examined the relationship between CD4 or VL, respectively, and KS risk according to number of days lagged (Table 2 ). The strength of the association between CD4 and KS risk declined progressively with increasing lag, with a hazard ratio (HR) for the 180-day lag of 33.1 [95% confidence interval (CI): 22.9 to 47.8] for CD4 <50 vs. ≥500 cells/μL. The AIC comparison among lags confirmed that the model with the 180-day lag was the preferred model.
TABLE 2.: HRs for KS Risk in Relation to CD4 Cell Count or VL With Different Lags (Separate Model for Each Lag, for CD4 Cell Count and VL, Respectively), NA-ACCORD, 1996–2009
The strength of the association between VL and KS risk was similar for the 180- and 540-day lags and then declined with increasing lag, with a HR for the 180-day lag of 16.1 (95% CI: 12.0 to 21.5) for VL ≥100,000 vs. ≤500 copies/mL (Table 2 ). The AIC for the 180- and 540-day lags did not appreciably differ.
However, valid comparison of AICs across the 4 lags required that each AIC be calculated from a model using the same data set (ie, persons with at least 1260 days of follow-up; Table 2 ). Consequently, to further adjudicate the AIC comparison between the 180- and 540-day lags, we derived AICs for these 2 models among persons with at least 540 days of follow-up (which provided a larger N and more KS cases) and found 180 days to be the optimal VL lag (AIC of 6180 for the 180-day lag vs. 6240 for the 540-day lag).
Trajectory and Cumulative Measures
Then we examined the relationship between KS risk and CD4 or VL measures (ie, slope, proportion of time below/above a threshold, or time-weighted mean), respectively, during the 1080-day time window lagged by 180 days and found the relationship to be highly significant (P < 0.0001) for each measure (Table 3 ). The 26,476 persons excluded from these analyses (those with <1260 days of follow-up) had a later baseline calendar period and a higher proportion with unknown smoking status compared with included persons, but other baseline characteristics did not meaningfully differ (see Table S2, Supplemental Digital Content, https://links.lww.com/QAI/B8
TABLE 3.: HRs for KS Risk in Relation to CD4 Cell Count or VL Measures During the 1080-Day Time Window Lagged by 180 Days (Separate Model for Each Measure), NA-ACCORD, 1996–2009 (N = 51,220; KS Cases = 177)
TABLE 3-A.: HRs for KS Risk in Relation to CD4 Cell Count or VL Measures During the 1080-Day Time Window Lagged by 180 Days (Separate Model for Each Measure), NA-ACCORD, 1996–2009 (N = 51,220; KS Cases = 177)
Selection of Best CD4 Predictors
Next, we examined models that included 2 or more CD4 measures (Table 4 ). The best CD4 model according to the AIC included CD4 lagged by 180 days and CD4 slope. Furthermore, in models including these measures and proportion of time CD4 <200 cells/μL or CD4 time-weighted mean, the global P values for the latter measures were no longer significant.
TABLE 4.: Global P values and Akaike's Information Criteria Derived From Cox Models, NA-ACCORD, 1996–2009 (N = 51,220; KS Cases = 177)
TABLE 4-A.: Global P values and Akaike's Information Criteria Derived From Cox Models, NA-ACCORD, 1996–2009 (N = 51,220; KS Cases = 177)
Selection of Best VL Predictors
We also examined models that included 2 or more VL measures (Table 4 ). The 2 best VL models according to the AIC included VL lagged by 180 days and VL time-weighted mean or these measures plus proportion of time VL >500 copies/mL. Because the global P value for the latter measure was not significant (P = 0.11) in the latter model, we chose the more parsimonious model as the best VL model.
Selection of Final Model
To derive a final model, we examined models including 2 or more of the best CD4 and VL predictors determined above: CD4 lagged by 180 days, CD4 slope, VL lagged by 180 days, and VL time-weighted mean (Table 4 ). According to the AIC, the 2 best models included CD4 and VL, each lagged by 180 days, and VL time-weighted mean, or these measures plus CD4 slope. Because the global P value for CD4 slope was only borderline significant (P = 0.055) in the latter model, we chose the more parsimonious model as our final model (Table 5 ). P -trend for each measure in the final model was highly significant (P < 0.0001). In this model, with VL time-weighted mean modeled as a continuous variable, we estimated that CD4 lagged by 180 days mediated 38% of the association between VL time-weighted mean and KS risk.
TABLE 5.: Final Mutually Adjusted Model,* NA-ACCORD, 1996–2009 (N = 51,220; KS Cases = 177)
ART Models
Among persons who were ART naive at baseline (N = 35,804; KS cases = 119), the HR per 20% of time on ART during the 1080-day time window lagged by 180 days was 0.77 (95% CI: 0.70 to 0.85) with adjustment for baseline covariates; 0.71 (95% CI: 0.64 to 0.79) with further adjustment for confounding by CD4; and 0.96 (95% CI: 0.85 to 1.09) with further adjustment for the CD4 and VL measures in the final model (see Table S3, Supplemental Digital Content, https://links.lww.com/QAI/B8
Sensitivity Analyses
Our sensitivity analyses revealed our final model to be highly robust, with no confounding by HIV risk group or smoking (data not shown).
DISCUSSION
Using data from a large North American HIV-cohort consortium, we found each of a wide variety of CD4 and VL measures (recent, lagged, trajectory, and cumulative) to be highly significantly associated with KS risk. However, we found recent CD4, recent VL, and cumulative VL to be the most robust independent predictors. Furthermore, the protective effect of cumulative ART exposure seemed to be completely explained by these factors, meaning we did not detect an independent anti-KS effect of ART itself.
Our results suggested a multifactorial etiology for KS, with early and late phases of development. Although the recent VL and CD4 effects suggested that HIV and immunosuppression are involved in late phases of KS development, the cumulative VL effect suggested that ongoing HIV exposure may promote earlier phases of development. Of course, KSHV acquisition represents an additional requirement for KS development that we were unable to evaluate.
The cumulative VL effect, in particular, suggested that to prevent KS it is important to control HIV replication promptly after HIV diagnosis, no matter how high the CD4 count, consistent with current recommendations41 16
The first of 3 questions we posed in the Introduction was when is CD4 most predictive of KS risk over the course of HIV infection . An inverse association between recent/current CD4 and KS risk has been consistently observed by others. However, most previous studies measured current CD4,11,25–28,30
Our second question was whether VL is associated with KS risk independent of CD4, and if so, which VL measure is the best predictor. We found a slower decline in HRs with increasing lag observed for VL than for CD4 (Table 2 ), suggesting a role for past, and possibly cumulative, VL exposure. We then found that both VL lagged by 180 days and VL time-weighted mean were independent predictors of KS risk. Moreover, only 38% of the association between VL time-weighted mean and KS risk was mediated by recent CD4, suggesting that most of the cumulative VL effect acted through an independent mechanism.
The independent effect of recent VL was consistent with some,11,26,29 25,27 25,27 26,32,42 43
We are aware of only 2 studies that examined the relationship between cumulative VL exposure and KS risk. One study with only 39 KS cases found that cumulative time with VL >500 copies/mL was not associated with KS risk after adjusting for cumulative time with CD4 <200 cells/μL.32 26 32,44,45
An effect of recent VL independent of recent CD4 suggests that recent VL provides independent information about recent immune dysfunction,43 8,46–48 49–56 57
Our final question was whether the protective effect of ART on KS risk is entirely mediated by its effects on CD4 and VL. Our observed association between higher proportion of time on ART and reduced KS risk was expected.1,24,26,30,31 40 25,30 31 26,27 11
An important study limitation was lack of information on KSHV infection status. Not only is KSHV infection necessary for KS development, but among persons infected with both KSHV and HIV, KSHV viremia is strongly associated with subsequent development of KS, independent of CD4 and VL.58 37 1,59
Strengths of our study included its large size, validated KS diagnoses,6
In summary, we found that recent CD4, recent VL, and cumulative VL were robust independent predictors of KS risk, consistent with a multifactorial etiology for KS, with early and late phases of development. The novel finding of an independent association between cumulative VL and KS risk provided a possible explanation for the elevated KS risk among HIV-infected persons on ART and suggested the importance of controlling HIV replication promptly after HIV diagnosis to prevent KS. However, this finding requires confirmation and further characterization, including how long the cumulative VL effect lasts after viral suppression by ART. Finally, we found no evidence for direct anti-KS activity of ART, independent of CD4 and VL.
ACKNOWLEDGMENTS
The authors would like to thank all individuals involved with the NA-ACCORD collaboration, including staff, investigators, and patients, for their invaluable contributions to this work.
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