Immunodeficiency and the risk of serious clinical endpoints in a well studied cohort of treated HIV-infected patients
Achhra, Amit Ca; Amin, Janakia; Law, Matthew Ga; Emery, Seana; Gerstoft, Janb; Gordin, Fred Mc; Vjecha, Michael Jd; Neaton, James De; Cooper, David Aa; for INSIGHT ESPRIT & SILCAAT study groups
aNational Centre in HIV Epidemiology and Clinical Research, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
bDepartment of Infectious Diseases, Rigshospitalet University of Copenhagen, Copenhagen, Denmark
cVeterans Affairs Medical Center, Washington DC and George Washington University School of Medicine, USA
dVeterans Affairs Medical Center and Institute for Clinical Research, Inc., Washington, District of Columbia, USA
eDivision of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Received 15 February, 2010
Revised 16 April, 2010
Accepted 16 April, 2010
Correspondence to Amit C. Achhra, National Centre in HIV Epidemiology and Clinical Research, University of New South Wales, Cliffbrook Campus, 45 Beach Street, Coogee, Sydney, NSW 2034, Australia. Tel: +612 9385 0900; fax: +612 9385 0920; e-mail: email@example.com
Objective: To investigate the relative predictive value of CD4+ metrics for serious clinical endpoints.
Methods: Patients (3012; 20 317 person-years) from control arms of ESPRIT and SILCAAT were followed prospectively. We used Cox regression to identify CD4+ metrics (latest, baseline and nadir CD4+ cell count, latest CD4+%, time spent with CD4+ count below certain thresholds and CD4+ slopes) independently predictive of all-cause mortality, non-AIDS deaths, non-AIDS (cardiovascular, hepatic, renal and non-AIDS malignancy) and AIDS events. Akaike information criteria (AIC) were calculated for each model. Significant metrics (P < 0.05) were then additionally adjusted for latest CD4+ cell count.
Results: Non-AIDS deaths occurred at a higher rate than AIDS deaths [rate ratio: 6.48, 95% confidence interval (CI) 5.1–8.1], and non-AIDS events likewise (rate ratio: 1.72, 95% CI 1.65–1.79). Latest CD4+ cell count was strongly predictive of lower risk of death (hazard ratio per log2 rise: 0.48, 95% CI 0.43–0.54), with lowest AIC of all metrics. CD4+ slope over seven visits, after additional adjustment for latest CD4+ cell count, was the only metric to be an independent predictor for all-cause (hazard ratio for slope <−10 cells/μl per month vs. 0 ± 10: 3.04, 95% CI 1.98–4.67) and non-AIDS deaths (hazard ratio for slope <−10 cells/μl per month vs. 0 ± 10: 2.62, 95% CI 1.62–4.22). Latest CD4+ cell count (per log2 rise) was the best predictor across all four endpoints and predicted hepatic (hazard ratio 0.46, 95% CI 0.33–0.63) and renal events (hazard ratio 0.39, 95% CI 0.21–0.70), but not cardiovascular events (hazard ratio 1.05, 95% CI 0.77–1.43) or non-AIDS cancers (hazard ratio 0.78, 95% CI 0.59–1.03).
Conclusion: Latest CD4+ cell count is the best predictor of serious endpoints. CD4+ slope independently predicts all-cause and non-AIDS deaths.
The disease burden in HIV-infected population with adequate access to combination antiretroviral therapy (cART) is increasingly due to serious non-AIDS events, with a lesser proportion being contributed by AIDS-related events [1–4]. These non-AIDS events (including cardiovascular, renal, and hepatic events and non-AIDS cancers) [2–9] are most likely to be multifactorial in origin, with aging , high-risk behavior , coinfections  and cART toxicity  being the contributing factors.
Recently, the role of immunodeficiency in the development of non-AIDS events has been investigated. In the SMART study, these events were more common in the arm with less cumulative exposure to cART and, consequently, more time spent with incremental levels of immunodeficiency [13–14]. Subsequently, observational studies have been used to investigate whether any association exists between CD4+ cell count, HIV RNA and non-AIDS events [15–18]. The findings from these studies suggest that higher recent CD4+ cell count is strongly associated with a lesser risk of non-AIDS events as a composite endpoint , although results for specific event categories, such as nonfatal cardiovascular and renal events, have been equivocal . However, traditional latest or recent CD4+ cell count levels do not fully reflect cumulative time spent in immunodeficiency or the rate of changes in CD4+ cell counts and only provide a snapshot of immunological status at a single time point. Recent studies suggest other aspects of immunodeficiency, such as time spent with CD4+ cell count below particular thresholds, could be of predictive value [14,20]. It is not known whether these CD4+ metrics provide any additional predictive value, especially for non-AIDS events, than those provided by latest absolute CD4+ cell count.
In the present study, we first define the relationship between latest CD4+ cell count and various serious clinical endpoints in our cohort. We then examine whether CD4+ metrics other than latest (most recent) CD4+ cell count (namely baseline and nadir CD4+ cell count, CD4+%, CD4+ slopes over short and long term, and time spent with CD4+ cell count below particular thresholds) provide any additional explanatory effect for major event categories than that provided by latest CD4+ cell count. We use data from the control arms of the Evaluation of Subcutaneous Proleukin in a Randomized International Trial (ESPRIT) and the Subcutaneous Recombinant, Human Interleukin-2 in HIV-Infected Patients with Low CD4+ Counts under Active Antiretroviral Therapy (SILCAAT) , which constitute a large heterogeneous group of HIV-infected patients, on cART for nearly a decade, with 4-monthly assessments and low rates of loss to follow-up, and well documented and validated serious clinical endpoints.
We analyzed the pooled follow-up data for patients enrolled in the control arms of ESPRIT and SILCAAT. Details of the study design and primary results of both trials have been published elsewhere [21–22]. Briefly, 4111 HIV-infected adults with CD4+ count 300 cells/μl or more and 1971 with CD4+ cell count 50–299 cells/μl were enrolled in ESPRIT and SILCAAT, respectively, and randomized equally to the control arm or interleukin-2 treatment arm. Both arms of each trial only included patients on cART. All patients were followed-up every 4 months for clinical assessment and the measurement of CD4+ cell count per μl and HIV RNA copies/ml.
The study endpoints were defined as follows: all-cause mortality; non-AIDS deaths; non-AIDS events, which include fatal or nonfatal serious clinical events in one of the four broad categories: cardiovascular disease (CVD) including stroke, myocardial infarction, coronary artery disease (CAD) requiring procedure, other fatal heart/vascular events and sudden death, hepatic, including cirrhosis or liver failure, renal including end-stage renal disease or kidney failure, and non-AIDS malignancy (excluding skin cancers); and AIDS events.
An Endpoint Review Committee validated the underlying cause of death using the Coding of Death in HIV (CoDE) system . Non-AIDS events were validated by the endpoint committee for the ESPRIT and were reported as grade 4 adverse events for the SILCAAT. Grade 4 events were defined as potentially life-threatening events requiring medical intervention according to the toxicity table of the Division of AIDS of the NIAID, and were coded according to the Medical Dictionary for Regulatory Activities (version 12.0).
We used Cox regression with time-updated variables to analyze the relationship between various CD4+ metrics and development of endpoints defined above. CD4+ metrics were defined as follows: latest CD4+ cell count corresponds to the CD4+ cell count measured closest to the event. This metric was time-updated and analyzed as both categorical (as >500, 350–500, 200–350, 50–200 and <50 cells/μl) and continuous variable log2 transformed (i.e. doubling) and per 100 cell rise. Time-updated CD4+% was the categorical variable (as >25, 14–25 and <14). Time-updated CD4+ slope over three consecutive visits as change in CD4+ cell count per month was determined by linear regression. The regression slope was determined from three consecutive CD4+ cell counts [current (at time t) and past two CD4+ counts (at time t − 1 and t − 2)]. A slope less than zero was interpreted as a decline in CD4+ cell count and vice versa. The median time between two visits was 3.5 months [interquartile range (IQR) 2.4–4.2]. Time-updated CD4+ slope for every seven consecutive visits (averaging a time span of approximately 2 years) was determined using linear regression. We defined CD4+ cell counts to be at plateau if CD4+ slope lay within the bounds of ±10 cells/μl change per month. It indicates the stability of CD4+ cell count over a prolonged period, as opposed to increasing (>10 cell rise per month) or decreasing CD4+ cell counts (>10 cell decrease per month) over that period. Time spent (per year) with CD4+ cell count below 200 cells/μl, below 100 cells/μl and below 50 cells/μl was a time-updated variable. Nadir CD4+ cell count as known at randomization. Baseline CD4+ cell count as measured at randomization.
We analyzed each of the above CD4+ metrics as predictors of each of the above-defined endpoints in adjusted Cox models, stratified by trial type (ESPRIT or SILCAAT). Models were fitted for each CD4+ metric, a priori adjusted for variables available for both the trials that are known to be associated with non-AIDS endpoints or death. These were sex, age, prior AIDS at baseline, ART duration at baseline, current ART class, region, race and time-updated HIV RNA load (categorized as ≤500, 500–10 000 and >10 000 copies/ml). For all time-updated variables, missing data were imputed by carrying forward (but not backward) the last observation till the last follow-up date. Akaike information criteria (AIC) were then calculated to assess the fit of the each adjusted model (lower AIC indicates better fit) .
Following this, those CD4+ metrics significant in the adjusted models, two-sided α less than 0.05, were additionally adjusted for latest CD4+ cell count (as log2 transformed) to see whether they provide any additional explanatory effect as to that provided by latest CD4+ cell count. Sensitivity analysis was performed by lagging CD4+ cell count and HIV RNA by 6 months for analyzing mortality-related endpoints. We also tested for any interaction between HIV RNA category (<500 or >500 copies/ml) and latest CD4+ cell count.
Follow-up data were censored at the first of lost to follow-up, date of death or the closing date of study (15 November 2008). Patients who met multiple endpoints were counted for each endpoint when they were considered separately. Findings are summarized as hazard ratios and 95% confidence intervals (CIs). All analyses were performed using STATA (StataCorp, College Station, Texas, USA) version 10.
There were a total of 3024 patients randomized to the control arms of ESPRIT and SILCAAT (2040 and 984, respectively), of which 3012 patients were included in the analysis. No follow-up data were available for 12 patients. The population at baseline was characterized as 2488 (82.3%) men, median age of 41 years, predominantly of white race (n = 2315; 76. 8%), mean CD4+ cell count 400 cells/μl, mean nadir CD4+ cell count 167 cells/μl, mean CD4+% 21.5, 2438 (80.8%) with HIV RNA below the detection limit of 500 copies/ml, 847 (28%) with a history of AIDS and the mean cumulative duration of ART of 57.4 months (Table 1). Except for baseline CD4+ cell count, nadir CD4+ cell count and baseline CD4+%, which were higher in the ESPRIT patients, there were no meaningful differences in baseline characteristics between the two trials (see Table 1). Hepatitis B and hepatitis C coinfection status and likely mode of HIV infection were not collected for SILCAAT patients and, therefore, these covariates were not used in any further analysis. The median follow-up time was 7 years (IQR 6–7.76), providing 20 317 person-years of follow-up.
The rate per 1000 person-years for each endpoint was 9.89 for all-cause mortality, 1.32 for AIDS-related deaths, 8.56 for non-AIDS deaths, 6.70 for AIDS events and 11.53 for non-AIDS events. The rates were higher for non-AIDS deaths (as compared to AIDS deaths; overall rate ratio 6.48, 95% CI 5.1–8.1) and for non-AIDS events (as compared to AIDS events; overall rate ratio 1.72, 95% CI 1.65–1.79) in both the trials and overall.
Association of CD4+ metrics with all-cause mortality
There were a total of 201 deaths overall. Adjusted for key covariates, latest CD4+ cell count was significantly predictive, with the risk halving per doubling of latest CD4+ cell count (hazard ratio per log2 rise: 0.48, 95% CI 0.43–0.54) (Table 2). Baseline CD4+ cell count (hazard ratio per 100 cells/μl rise: 0.77, 95% CI 0.68–0.89), nadir CD4+ cell count (hazard ratio per 100 cells/μl rise: 0.84, 95% CI 0.73–0.97), latest CD4+% less than 14 (hazard ratio 2.42, 95% CI 1.55–3.78 vs. CD4+% >25%), time (per year) spent with CD4+ cell count below 200 cells/μl (hazard ratio 1.29, 95% CI 1.18–1.41), below 100 (hazard ratio 1.35, 95% CI 1.20–1.51) and below 50 cells/μl (hazard ratio 1.45, 95% CI 1.12–1.87) were all significant individual predictors (adjusted for other covariates) (Table 2). Also, time-updated CD4+ slope over seven consecutive visits was significantly associated with risk of death (hazard ratio for slope <−10 cells/μl per month: 3.32, 95% CI 2.14–5.15 vs. CD4+ slope = 0 ± 10 cells/μl, i.e. plateau).
When CD4+ metrics significant in the adjusted models were additionally adjusted for latest CD4+ cell count, only CD4+ slope over seven consecutive visits remained statistically significant (hazard ratio for slope <−10 cells/μl per month vs. plateau: 3.04, 95% CI 1.98–4.67) (models 1–7 in Table 2). The results did not change appreciably when latest CD4+ cell count and HIV RNA were lagged by 6 months (not shown).
Association of CD4+ metrics with non-AIDS deaths
There were a total of 174 deaths due to non-AIDS causes (30 due to cardiovascular causes, 19 due to hepatic causes, 39 due to non-AIDS cancers, 14 due to non-AIDS infections, 16 due to suicides or accidents and 56 due to other/unknown causes) and 27 deaths due to AIDS. In addition to latest CD4+ cell count (hazard ratio per log2 rise: 0.53, 95% CI 0.46–0.60), baseline CD4+ cell count, latest CD4%, CD4+ slope over seven consecutive visits and time spent (per year) with CD4+ cell count below 200 and 100 cells/μl were other significant predictors, with lowest AIC for latest CD4+ cell count (data not shown). However, only CD4+ slope over seven consecutive visits retained significance (hazard ratio for slope <−10 cells/μl per month vs. plateau: 2.62, 95% CI 1.62–4.22) when they were additionally adjusted for latest CD4+ cell count. When rates of AIDS and non-AIDS deaths were compared across different CD4+ categories, non-AIDS deaths occurred at higher rates in all CD4+ categories, with the difference more marked in higher CD4+ categories (Fig. 1).
Association of CD4+ metrics with non-AIDS events
There were a total of 226 non-AIDS events (95 cardiovascular events, 95 non-AIDS cancers, 28 hepatic and eight renal events). In the adjusted models, latest CD4+ cell count was strongly associated with non-AIDS events as an endpoint (hazard ratio per log2 rise: 0.73, 95% CI 0.62–0.86). Time (per year) spent with CD4+ cell count below 200 cells/μl (hazard ratio 1.14, 95% CI 1.02–1.27), below 100 cells/μl (hazard ratio 1.30, 95% CI 1.10–1.53) and below 50 cells/μl (hazard ratio 1.80, 95% CI 1.05–3.08) were other significant predictors in the adjusted model (Table 3). However, they were no longer significant when the models were additionally adjusted for latest CD4+ cell count (models 1–3 in Table 3). Other CD4+ metrics were not found to be significant predictors of non-AIDS events (Table 3).
In comparison, for AIDS events (n = 132), latest CD4+ cell count, baseline CD4+ cell count, nadir CD4+ cell count, CD4+%, CD4+ slope over three visits and time (per year) spent with CD4+ cell count below 200 cells/μl, below 100 cells/μl and below 50 cells/μl were all found to be significant predictors in the adjusted model, with lowest AIC for latest CD4+ count (data not shown). Only CD4+% less than 14 retained significance when additionally adjusted for latest CD4+ cell count. When rates of AIDS and non-AIDS events were compared across different CD4+ categories, non-AIDS events and AIDS events occurred nearly at equal rates in higher CD4+ categories (Fig. 1).
Association of latest CD4+ count with specific non-AIDS subcategories
The risk of fatal or nonfatal cardiovascular events was not significantly associated with latest CD4+ cell count (adjusted hazard ratio per log2 rise: 1.05, 95% CI 0.77–1.43) (Fig. 2a). When only fatal cardiovascular events were considered, risk was 37% lower (adjusted hazard ratio per log2 rise in CD4+: 0.63, 95% CI 0.43–0.92). However, the association was no longer significant when CD4+ cell count and HIV RNA were lagged by 6 months (Fig. 2a).
There were a total of 95 non-AIDS cancers. These included respiratory tract (n = 13), anal (n = 10), other gastrointestinal (n = 9), prostatic (n = 8), lip and oropharyngeal (n = 7), laryngeal (n = 5), hepatic (n = 4) and breast (n = 4) neoplasms and 13 were of unknown/unspecified type. The risk of fatal or nonfatal non-AIDS cancers tended to be lower per log2 rise in CD4+ count, but was not significant (adjusted hazard ratio per log2 rise: 0.78, 95% CI 0.59–1.03). Also, association between CD4+ cell count and fatal non-AIDS cancers lost its significance when CD4+ count and HIV RNA were lagged by 6 months (Fig. 2b).
Risk of serious hepatic events was significantly associated with latest CD4+ cell count (adjusted hazard ratio per log2 rise: 0.46, 95% CI 0.33–0.63) and this significance did not diminish when CD4+ count and HIV RNA were lagged by 6 months while analyzing fatal hepatic events (Fig. 2c). Although there were only eight renal events, their risk was significantly associated with latest CD4+ cell count (adjusted hazard ratio per log2 rise: 0.39, 95% CI 0.21–0.70) (Fig. 2d).
Latest CD4+ cell count was significantly associated with risk of all remaining causes of non-AIDS death, even when CD4+ count and HIV RNA were lagged by 6 months (Fig. 2e).
Among the other covariates included in the model adjusted for latest CD4+ cell count, age was an independent predictor of all-cause mortality, non-AIDS deaths and non-AIDS events and HIV RNA was an independent predictor of AIDS events (data not shown). We did not find significant interaction between HIV RNA and latest CD4+ cell count for all the endpoints (P = 0.41 for all-cause mortality), due possibly to the fact that more than 80% patients had undetectable HIV RNA at baseline and for most of the follow-up time. Results were therefore not stratified by HIV RNA category.
In assessing the relationship between various CD4+ metrics and the risk of serious clinical endpoints, including all-cause mortality, non-AIDS deaths, fatal or nonfatal non-AIDS and AIDS events, we found that latest absolute CD4+ cell count was the best predictor across all the endpoints. Negative CD4+ slope over approximately 2 years was also independently associated with higher risk of all-cause and non-AIDS deaths, even after additional adjustment for latest CD4+ count.
Our findings confirm that in the cART era, non-AIDS events dominate the disease burden in HIV-infected population and their risk is related to the degree of immunodeficiency, even in the cohort that had undetectable HIV RNA levels for most of the follow-up time. In our cohort, lower latest CD4+ cell count was significantly associated with higher risk of non-AIDS events or deaths. Also, the risk of non-AIDS events tended to increase with more time spent in lower CD4+ categories. These findings are consistent with similar studies conducted in other cohorts [15,18–19]. A recent analysis that pooled the ESPRIT and SMART cohorts and focused on risk of death after AIDS and non-AIDS events did not find a significant association between latest CD4+ cell count and non-AIDS events, due possibly to the greater number of CVD events and non-AIDS malignancies in those studies, as well as the higher CD4+ counts (>300 cells/μl) at baseline . In our study, patients with latest CD4+ cell count 350–500 cells/μl were not found to be at higher risk of non-AIDS events, as compared with those with latest CD4+ cell count higher than 500 cells/μl.
Nadir CD4+ cell count was not associated with the risk of non-AIDS events or deaths. This finding is consistent with that of the D:A:D study, which reported no association between nadir CD4+ cell count and the risk of non-AIDS malignancies , and with that of a study that pooled the ESPRIT and SMART cohorts, which reported only borderline association with non-AIDS events . This suggests that the risk of these events may not be related to severity of past immunodeficiency. CD4+ plateau was considered, as some patients are known to attain stability in their CD4+ cell count after approximately 3.5 years of cART . It was found that negative CD4+ slope (an overall ongoing decline in CD4+ cell count) over approximately 2 years was independently associated with higher risk of all-cause and non-AIDS deaths, as compared to CD4+ plateau. This finding further suggests that relatively recent immunodeficiency may have a role in non-AIDS deaths. However, CD4+ slope was not found to be a better predictor of endpoints than were latest CD4+ cell counts. This is consistent with a recent CASCADE analysis of therapy-naive individuals . Other CD4+ metrics did not provide any additional explanatory effect than that provided by latest CD4+ cell count.
Latest CD4+ cell count was significantly associated with the risk of hepatic and renal events, and this finding is consistent with that of other observational studies [18,19,29,30]. This association remained even after we lagged the CD4+ cell count and HIV RNA by 6 months for fatal events, thereby confirming the role of immunodeficiency in these events, apart from other factors such as coinfections. The risk of fatal or nonfatal non-AIDS cancers was not found to be associated with latest CD4+ cell count level. Further, any significant association between fatal non-AIDS cancers and CD4+ cell count was lost when CD4+ cell count and HIV RNA levels were lagged by 6 months. This is in contrast to findings from other observational studies, which reported a significant association between recent CD4+ cell count and non-AIDS malignancies [15,18,26]. This discrepancy could be due to lack of sufficient number of events (and therefore the power) in our analysis. Alternatively, our cohort may have experienced those non-AIDS cancers that are less associated with immunodeficiency. Non-AIDS cancers related to the infectious cause are most likely related to immunodeficiency . However, we did not have enough events in specific subtypes to formally evaluate this hypothesis.
We did not find significant association between CD4+ cell count and cardiovascular events, which is consistent with findings of other studies [18,19]. Although some association existed for fatal cardiovascular events, this was lost when we lagged the CD4+ cell count and HIV RNA by 6 months, thereby suggesting that CD4+ cell count may have decreased due to the illness, rather than vice versa. Increased rate of cardiovascular and possibly other non-AIDS events in HIV-infected population could be due to subtle ongoing inflammatory process stimulated by residual viral replication [32,33] or the treatment . The subclinical inflammation may not be best reflected by latest CD4+ cell count. More specific inflammation and coagulation biomarkers, such as IL-6, D-dimer and C-reactive protein, may prove to be better predictors of these events [33,34]. Further research in this area should focus on elucidating the role of these and possibly newer biomarkers in predicting various non-AIDS events in HIV-infected population. This would not only provide new clinical tools for predicting these events but also provide better insight into their pathogenesis.
The strengths of our analysis include a large heterogeneous group of patients with wide range of baseline CD4+ cell counts, long-term follow-up (7 years), low rates of loss to follow-up , prospective validation of even nonfatal non-AIDS events (especially for ESPRIT trial, which was in majority) and a significant number of nonfatal non-AIDS events. The three main limitations to this analysis include the following. First, some risk factors for non-AIDS events, including some behavioral risk factors, were not collected on all patients and hepatitis B and C status, and likely mode of HIV infection were not documented for the SILCAAT patients. Our inability to adjust for coinfection status is especially important, as coinfection is known to have a detrimental effect on CD4+ cell count  and also, may in part, be responsible for liver-related events. Second, we could not compare the predictive value of various CD4+ metrics for specific non-AIDS subcategories, due mainly to the smaller number of events and, therefore, the lack of power. Lastly, these findings are based on control arms of two clinical trials and, therefore, may not be as representative of participants in some observational cohorts.
In summary, we confirm the association between latest CD4+ cell count and non-AIDS events in HIV-infected population on stable cART. We also showed that among various CD4+ metrics, latest CD4+ cell count is the best predictor of non-AIDS deaths and events. Inducing CD4+ proliferation by immune-based therapies, such as IL-2, has not provided any clinical benefit . Whether or not other strategies that focus on CD4+ T cells separately from impacting viral load would be of benefit remains to be determined.
Also, there is currently considerable debate regarding the best time to initiate antiretroviral therapy for HIV infection. As the differences in absolute risk of non-AIDS events between higher CD4 strata are rather low, even a small risk from antiretroviral treatment (especially for CVD events) could offset the absolute gain. The only definitive way to assess this is via randomized trial. The results from the START trial, which is an ongoing randomized study investigating whether starting cART at CD4+ cell count above 500 cells/μl provides any additional benefit as compared to starting cART at CD4+ cell count less than or equal to 350 cells/μl, are likely to provide important answers to these questions.
The writing group acknowledges the efforts of thousands of patients, the many ESPRIT and SILCAAT investigators who collected these data, the International Network for Strategic Initiatives in Global HIV Trials (INSIGHT) Executive Committee (JD Neaton, D Abrams, A Babiker, J Baxter, DA Cooper, CJ Cohen, D Cohn, JH Darbyshire, W El-Sadr, S Emery, F Gordin, HC Lane, G Larson, MH Losso, JD Lundgren, J Nadler, AN Phillips) for their oversight of the ESPRIT study, and the SILCAAT Scientific Committee (Y Lévy, D Abrams, A Babiker, P Cahn, B Clotet, N Clumeck, DA Cooper, JH Darbyshire, S Emery, U Hengge, HC Lane, J Lange, G Levi, JD Lundgren, R Mitsuyasu, JD Neaton, JP Routy, G Tambussi) for their oversight of the SILCAAT study.
ESPRIT was supported by grants U01 AI46957 and U01 AI068641 from the National Institute of Allergy and Infectious Diseases (NIAID). SILCAAT was supported by Chiron and Novartis.
Author contribution: A.C.A. performed statistical analysis and drafted the first manuscript; J.A., M.G.L., S.E. and D.A.C. contributed to design of the study, review of the results and critical review of the manuscript; and J.D.N., F.M.G., M.J.V. and J.G. each contributed review of the results and provided critical revision to the manuscript.
1. Mocroft A, Vella S, Benfield TL, Chiesi A, Miller V, Gargalianos P, et al
. Changing patterns of mortality across Europe in patients infected with HIV-1. EuroSIDA Study Group. Lancet 1998; 352:1725–1730.
2. Pacheco AG, Tuboi SH, Faulhaber JC, Harrison LH, Schechter M. Increase in non-AIDS related conditions as causes of death among HIV-infected individuals in the HAART era in Brazil. PLoS One 2008; 3:e1531.
3. Sackoff JE, Hanna DB, Pfeiffer MR, Torian LV. Causes of death among persons with AIDS in the era of highly active antiretroviral therapy: New York City. Ann Intern Med 2006; 145:397–406.
4. Smit C, Geskus R, Walker S, Sabin C, Coutinho R, Porter K, et al
. Effective therapy has altered the spectrum of cause-specific mortality following HIV seroconversion. AIDS 2006; 20:741–749.
5. Burgi A, Brodine S, Wegner S, Milazzo M, Wallace MR, Spooner K, et al
. Incidence and risk factors for the occurrence of non-AIDS-defining cancers among human immunodeficiency virus-infected individuals. Cancer 2005; 104:1505–1511.
6. Chaturvedi AK, Pfeiffer RM, Chang L, Goedert JJ, Biggar RJ, Engels EA. Elevated risk of lung cancer among people with AIDS. AIDS 2007; 21:207–213.
7. Friis-Moller N, Sabin CA, Weber R, d'Arminio Monforte A, El-Sadr WM, Reiss P, et al
. Combination antiretroviral therapy and the risk of myocardial infarction. N Engl J Med 2003; 349:1993–2003.
8. Frisch M, Biggar RJ, Engels EA, Goedert JJ. Association of cancer with AIDS-related immunosuppression in adults. JAMA 2001; 285:1736–1745.
9. Schwartz EJ, Szczech LA, Ross MJ, Klotman ME, Winston JA, Klotman PE. Highly active antiretroviral therapy and the epidemic of HIV+ end-stage renal disease. J Am Soc Nephrol 2005; 16:2412–2420.
10. Deeks SG, Phillips AN. HIV infection, antiretroviral treatment, ageing, and non-AIDS related morbidity. BMJ 2009; 338:a3172.
11. Nahvi S, Cooperman NA. Review: the need for smoking cessation among HIV-positive smokers. AIDS Educ Prev 2009; 21:14–27.
12. Torti C, Lapadula G, Uccelli MC, Quiros-Roldan E, Regazzi M, Ladisa N, et al
. Influence of viral chronic hepatitis co-infection on plasma drug concentrations and liver transaminase elevations upon therapy switch in HIV-positive patients. Int J Antimicrob Agents 2007; 29:185–190.
13. El-Sadr WM, Lundgren JD, Neaton JD, Gordin F, Abrams D, Arduino RC, et al
. CD4+ count-guided interruption of antiretroviral treatment. N Engl J Med 2006; 355:2283–2296.
14. Strategies for Management of Antiretroviral Therapy (SMART) Study Group, Lundgren JD, Babiker A, El-Sadr W, Emery S, Grund B, Neaton JD, et al. Inferior Clinical Outcome of the CD4þ Cell Count-Guided Antiretroviral Treatment Interruption Strategy in the SMART Study: role of CD4þ cell counts and HIV RNA levels during follow-up. J Infect Dis
15. Baker JV, Peng G, Rapkin J, Abrams DI, Silverberg MJ, MacArthur RD, et al
. CD4+ count and risk of non-AIDS diseases following initial treatment for HIV infection. AIDS 2008; 22:841–848.
16. Bruyand M, Thiebaut R, Lawson-Ayayi S, Joly P, Sasco AJ, Mercie P, et al
. Role of uncontrolled HIV RNA level and immunodeficiency in the occurrence of malignancy in HIV-infected patients during the combination antiretroviral therapy era: Agence Nationale de Recherche sur le Sida (ANRS) CO3 Aquitaine Cohort. Clin Infect Dis 2009; 49:1109–1116.
17. Kaplan RC, Kingsley LA, Gange SJ, Benning L, Jacobson LP, Lazar J, et al
. Low CD4+ T-cell count as a major atherosclerosis risk factor in HIV-infected women and men. AIDS 2008; 22:1615–1624.
18. Smith C, and the D:A:D Study Group. Association between modifiable and nonmodifiable risk factors and specific causes of death in the HAART era: the data collection on adverse events of anti-HIV drugs study. 16th Conference on retroviruses and opportunistic infections
; Montreal; 2009; abstract 145.
19. Phillips AN, Neaton J, Lundgren JD. The role of HIV in serious diseases other than AIDS. AIDS 2008; 22:2409–2418.
20. Kesselring A, Gras L, Smit C, Wolf Fd, Reiss P, Wit F. Longer duration of exposure to immunodeficiency and detectable viremia both are risk factors for non-AIDS defining malignancies in HIV-1 infected patients on combination antiretroviral therapy. International AIDS Society (IAS) conference
; Cape Town, South Africa; 2009; abstract WEAB104.
21. Abrams D, Levy Y, Losso MH, Babiker A, Collins G, Cooper DA, et al
. Interleukin-2 therapy in patients with HIV infection. N Engl J Med 2009; 361:1548–1559.
22. Emery S, Abrams DI, Cooper DA, Darbyshire JH, Lane HC, Lundgren JD, et al
. The evaluation of subcutaneous proleukin (interleukin-2) in a randomized international trial: rationale, design, and methods of ESPRIT. Control Clin Trials 2002; 23:198–220.
23. Lifson AR, Belloso WH, Carey C, Davey RT, Duprez D, El-Sadr WM, et al
. Determination of the underlying cause of death in three multicenter international HIV clinical trials. HIV Clin Trials 2008; 9:177–185.
24. Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control 1974; 19:716–723.
25. Neuhaus J, Angus B, Kowalska JD, La Rosa A, Sampson J, Wentworth D, et al
. Risk of all-cause mortality associated with nonfatal AIDS and serious non-AIDS events among adults infected with HIV. AIDS 2010; 24:697–706.
26. Monforte A, Abrams D, Pradier C, Weber R, Reiss P, Bonnet F, et al
. HIV-induced immunodeficiency and mortality from AIDS-defining and non-AIDS-defining malignancies. AIDS 2008; 22:2143–2153.
27. Tarwater PM, Margolick JB, Jin J, Phair JP, Detels R, Rinaldo C, et al
. Increase and plateau of CD4 T-cell counts in the 3(1/2) years after initiation of potent antiretroviral therapy. J Acquir Immune Defic Syndr 2001; 27:168–175.
28. Wolbers M, Babiker A, Sabin C, Young J, Dorrucci M, Chene G, et al
. Pretreatment CD4 cell slope and progression to AIDS or death in HIV-infected patients initiating antiretroviral therapy: the CASCADE collaboration – a collaboration of 23 cohort studies. PLoS Med 2010; 7:e1000239.
29. Weber R, Sabin CA, Friis-Moller N, Reiss P, El-Sadr WM, Kirk O, et al
. Liver-related deaths in persons infected with the human immunodeficiency virus: the D:A:D study. Arch Intern Med 2006; 166:1632–1641.
30. O Kirk AM, d'Arminio Monforte A, Eg Hansen A-B, Gatell JM, Caplinskas S, Fätkenheuer G, et al
., and the EuroSIDA Study Group. Deterioration of renal function associated with current level of immunodeficiency. 15th Conference on Retroviruses and Opportunistic Infections
; Boston, USA; 2008; abstract 971.
31. Grulich AE, van Leeuwen MT, Falster MO, Vajdic CM. Incidence of cancers in people with HIV/AIDS compared with immunosuppressed transplant recipients: a meta-analysis. Lancet 2007; 370:59–67.
32. Aberg JA. Cardiovascular complications in HIV management: past, present, and future. J Acquir Immune Defic Syndr 2009; 50:54–64.
33. Kuller LH, Tracy R, Belloso W, De Wit S, Drummond F, Lane HC, et al
. Inflammatory and coagulation biomarkers and mortality in patients with HIV infection. PLoS Med 2008; 5:e203.
34. Lundgren JD. Use of nucleoside reverse transcriptase inhibitors and risk of myocardial infarction in HIV-infected patients. AIDS 2008; 22:7.
35. Miller MF, Haley C, Koziel MJ, Rowley CF. Impact of hepatitis C virus on immune restoration in HIV-infected patients who start highly active antiretroviral therapy: a meta-analysis. Clin Infect Dis 2005; 41:713–720.
AIDS; CD4+; CD4+ cell counts; immunodeficiency; serious non-AIDS events
© 2010 Lippincott Williams & Wilkins, Inc.
What does "Remember me" mean?
By checking this box, you'll stay logged in until you logout. You'll get easier access to your articles, collections,
media, and all your other content, even if you close your browser or shut down your
To protect your most sensitive data and activities (like changing your password),
we'll ask you to re-enter your password when you access these services.
What if I'm on a computer that I share with others?
If you're using a public computer or you share this computer with others, we recommend
that you uncheck the "Remember me" box.
Highlight selected keywords in the article text.
Data is temporarily unavailable. Please try again soon.
Readers Of this Article Also Read