The factors associated with HIV-1 disease progression and survival in men have been described in several large cohort studies. These factors include quantitative HIV-1 RNA level[1-3], CD4 cell count[3-5], age, clinical manifestations of disease[7-9], and receipt of antiretroviral therapies[10,11]. Prospective studies have also demonstrated that in seroprevalent cohorts higher HIV-1 RNA levels, at baseline or during antiretroviral therapy, are associated with more rapid disease progression and shorter survival[2,10,12-16]. With a few recent exceptions[17,18], published data demonstrating the strong predictive value of HIV-1 RNA levels (both at baseline and during therapy) in HIV-related disease progression and mortality, have been derived from cohorts which are predominately white and male[1,2,19]. Even in this setting, the relative utility of HIV-1 RNA level and CD4 cell count in predicting clinical outcomes has not been fully delineated. Some authors have indicated that with more advanced HIV disease, CD4 cell count and quantitative HIV-1 RNA values may be equally strong predictors of clinical outcomes[20,21].
Although there exist little data regarding the value of either HIV-1 RNA levels or of CD4 cell count in predicting survival among HIV-infected women, recently published data have suggested that the quantitative HIV-1 RNA values in women may differ from those in men with similar CD4 cell count or similar duration of HIV-1 infection[13,22-27]. Other authors have not demonstrated such differences[28,29]. It has also been consistently demonstrated that lymphocyte subsets differ by sex in individuals who are not infected with HIV-1, with higher CD4 cell percentage and count in women[30,31]. These findings suggest that prognostic markers of disease progression and death in HIV-1 infection may differ by sex, and need to be assessed in populations of HIV-1-infected women.
The objectives of this study were to delineate the factors associated with survival in the first 2.5 years of follow-up in the Women‚s Interagency HIV Study (WIHS) and to examine the relative prognostic strength of CD4 cell count and quantitative HIV-1 RNA in predicting survival.
The WIHS is a multicenter prospective study of the natural history of HIV-1 infection in women conducted in five cities in the USA: New York City (two sites), Washington D.C., Chicago, Los Angeles and San Francisco. The WIHS methods and baseline cohort characteristics have been described previously. From October 1994 through November 1995, 2619 women with confirmed HIV-1 serostatus (2050 seropositive and 569 seronegative) were enrolled in WIHS. An additional six women had positive HIV-1 serostatus confirmed at the 6 month follow-up visit. For purposes of this analysis their 6 month visit was considered baseline. Women were recruited through HIV primary care sites, drug treatment facilities and community based outreach.
Every 6 months, WIHS participants are interviewed using a structured questionnaire and receive a physical examination. Multiple gynecologic and blood specimens are collected at each visit. Notification of participant death is obtained continuously from passive surveillance: participant friends, relatives, and medical providers, and, in some sites, through local death registries. Death certificates are requested for all women who are known to have died and date of death is ascertained from the following sources in descending order of priority: death certificate, medical records, medical provider, and family/friends. Follow-up visits through October 31, 1997 were analyzed. Data were censored at date of last interview for those not known to have died, or on October 31, 1997 for those who died after this date. In analyses of highly active antiretroviral therapy (HAART), included regimens were: (i) one or more protease inhibitors (PI) plus two nucleoside analogue reverse transcriptase inhibitors (NRTI); or (ii) one or more PI plus one NRTI plus one non-NRTI; or (iii) two NRTI plus one non-NRTI.
Whole peripheral blood was collected in sodium citrate cell preparation tubes (CPT) (VACUTAINER brand tubes, Becton-Dickinson, Franklin Lakes, NJ, USA) at baseline and every 6 months thereafter. Tubes were centrifuged locally if plasma could not be separated within 6 h and shipped to a central laboratory where final separation was performed. Plasma was aliquoted and stored at -80°C. Quantification of HIV-1 RNA in plasma was performed using the isothermal nucleic acid sequence based amplification (NASBA) method (Organon Teknika Corp., Durham, North Carolina, USA). The HIV-1 RNA determinations were performed in laboratories that participated in the National Institutes of Health, Virology Quality Assurance Laboratory proficiency testing program. The lower limit of quantification was 4000 copies/ml. T-cell subsets were determined using standard flow cytometry performed in laboratories certified by the AIDS Clinical Trials Groups.
Except where noted, analysis included all women who had at least one follow-up visit or were known to have died after the baseline visit, and who had baseline HIV-1 plasma RNA determination and CD4 cell count. Survival time was measured from date of baseline WIHS visit, and all values for plasma HIV-1 RNA and CD4 cell count in this analysis are baseline determinations. All-cause mortality within subgroups was calculated by the Kaplan-Meier method  and summarized as the probability of dying within 18 months. Comparisons of survival across subgroups were performed using the log rank test and the Cox proportional hazards model. All proportional hazards models were stratified by site of enrollment to adjust for differences in ascertainment of deaths as well as any other sources of site heterogeneity.
Natural cubic spline functions of CD4 cell count and HIV-1 RNA  were used as covariates in proportional hazards models to explore the association between the hazard of death and HIV-1 RNA level and CD4 cell count. Natural cubic splines are piecewise cubic polynomials joined together at ‚knots‚, with suitable restrictions to ensure that the pieces join together smoothly and to dampen large fluctuations at the lower and upper edges. Spline functions can model a wide variety of nonlinear curves. After regions of linearity between log hazard and the covariates were identified, a single linear term in the proportional hazards model was used to estimate the slope over that region. These analyses adjusted for both CD4 cell count and HIV-1 RNA so that each estimated relative hazard was adjusted for the other covariate.
To assess the relative prognostic value of CD4 cell count and quantitative HIV-1 RNA measurements, multiple analyses were performed. First, categories of both HIV-1 RNA and CD4 cell count were created with arbitrary cut-off points corresponding to common clinically useful categories (<50, 50-199, 200-349, ≥350×106 cells/l for CD4 cell count; <4000, 4000-20000, 20000-100000, 100000-500000, >500000 copies/ml for HIV-1 RNA values). In additional analyses categories for each variable were constructed such that the distribution of each factor was the same. Thus, CD4 cell count categories were created with the previously delineated arbitrary cut-off points corresponding to common clinically useful categories, and HIV-1 RNA categories were developed such that the sizes of the subgroups matched the sizes of the CD4 cell count subgroups. Similarly, HIV-1 RNA categories were created with the previously delineated arbitrary cut-off points, and CD4 cell count categories were developed such that the sizes of the subgroups matched the sizes of the HIV-1 RNA subgroups. The conventional cut-off point at CD4 cell count ≥500×106 cells/l was not used because there were few deaths in the group with CD4 cell count ≥500×106 cells/l and survival was similar for the groups with CD4 cell count of 350-499 and ≥500×106 cells/l. The associations among these subgroups were then examined with Kaplan-Meier plots and proportional hazards models, including partial likelihood ratio χ2 statistics. All statistical tests are two-sided.
Of the 2056 HIV-1-infected women, 138 had no follow-up data and an additional 149 had no baseline HIV-1 RNA and/or CD4 cell count data, leaving 1769 for analysis. Demographic characteristics of the 1769 women analyzed and the 287 excluded are shown in Table 1. Included and excluded women were similar in age, race/ethnicity, educational attainment, and remote or recent (past 6 months) injection drug use. Among women with CD4 cell count or HIV-1 RNA data, the included and excluded women had similar distributions. Among women with follow-up data, the survival distributions of those included and excluded were similar. Median CD4 cell count at study entry was 326×106 cells/l, with 30% having fewer than 200×106 cells/l. Median HIV-1 RNA was 22230 copies/ml, and median age was 36 years. The median HIV-1 RNA values for CD4 cell count strata <50, 50-199, 200-349, and ≥350×106 cells/l were 204320, 89107, 22764, and <4000 copies/ml, respectively. The Spearman correlation between CD4 cell count and HIV-1 RNA level was -0.54. At enrollment 36% of participants reported no history of antiretroviral therapy and <1% reported use of HAART.
There was a total of 252 deaths in 29 months median follow-up. Kaplan-Meier estimates of death within 6, 12, 18, 24, and 30 months of follow-up were 2.3%, 6.4%, 9.8%, 12.8% and 15.8%, respectively. Among 498 HIV-1-uninfected women with follow-up data, the corresponding probabilities were 0.0%, 0.4%, 0.6%, 1.1% and 1.1%, respectively.
Lower CD4 cell count, higher quantitative HIV-1 RNA levels, self-report of a clinical AIDS-defining condition, older age and receipt of any antiretroviral therapy prior to the baseline visit were associated with shorter survival in univariate analysis (Table 2). Survival was not significantly associated with race/ethnicity, route of exposure to HIV-1, annual household income, educational attainment, smoking of tobacco or use of injection drugs in the last 6 months (data not shown). Approximately 49% of women with CD4 cell count <50×106 cells/l or HIV-1 RNA >500000 copies/ml, had died within 18 months, compared with 2% of women with CD4 cell count ≥350×106 cells/l or HIV-1 RNA <4000 copies/ml.
Both CD4 cell count and quantitative HIV-1 RNA remained statistically significant independent predictors of survival in multivariate proportional hazards analysis (Table 3). The relative hazard (RH) of dying was over eight times higher for women in the lowest (<50×106 cells/l) compared with the highest (≥350×106 cells/l) CD4 cell count category, and more than seven times higher (RH, 7.25) for women in the highest (>500000×106 cells/l) compared with the lowest (<4000×106 cells/l) category of HIV-1 RNA. After adjustment for CD4 cell count and HIV-1 RNA, a self-reported history of a CDC Class C AIDS-defining condition carried a RH of 2.22 [95% confidence interval (CI), 1.69-2.92]. Age was not significantly associated with survival after adjusting for CD4 cell count, HIV-1 RNA level and clinical stage (P=0.10). After adjustment for CD4 cell count, prior receipt of antiretroviral therapy was not associated with survival (P=0.50).
HIV-1 RNA and CD4 cell count were considered as continuous covariates using spline functions in order to explore their relationships with the hazard of death, after adjustment for one another and for clinical stage. For HIV-1 RNA the relationship was fairly uniform over the measured range (>4000 copies/ml). For each 1.0 log10 increase in HIV-1 RNA value, the RH of death was 2.22 (95% CI, 1.85-2.67). For CD4 cell count, only values <300×106 cells/l were associated with increased hazard, with a constant RH in that range of 2.51 (95% CI, 2.07-3.06) for each decrement of 100×106 cells/l.
When both CD4 cell count and HIV-1 RNA values were categorized by commonly used clinical cut-off points, multivariate analysis of our data suggests a larger RH associated with declining CD4 cell count than with increasing viral load (Table 4). Thus, compared with the reference category of women with CD4 cell count ≥200×106 cells/l and HIV RNA <20000 copies/ml, women incurred a greater risk of dying if they had a CD4 cell count <50×106 cells/l (RH, 21.62) than if they had a measurement of HIV RNA >500000 copies/ml (RH, 10.69). Within the group of women with CD4 cell count <50×106 cells/l, there was an approximate twofold increase in RH when viral burden increased from <20000 to >500000 copies/ml. Within the group of women with HIV RNA >500000 copies/ml, there was more than a fourfold increase in RH with CD4 cell count <50×106 cells/l compared with CD4 cell count ≥200×106 cells/l.
To assess further the prognostic strength of CD4 cell count compared to that of quantitative HIV-1 RNA, we created categories of HIV-1 RNA that had the same distribution. Categories for HIV-1 RNA (<17000, 17000-75650, 75650-285500, >285 500 copies/ml) were selected such that the sizes of the subgroups matched the sizes of the CD4 cell count categories. The percentage of women in the low, middle, higher and highest CD4 cell count groups were 12%, 18%, 23% and 46%, respectively, as were the percentages of women in the high, middle, lower and lowest RNA groups.
Both analyses performed suggested that CD4 cell count at baseline was as strong a predictor of survival as the baseline HIV-1 RNA level. Proportional hazards analysis demonstrated a larger RH for CD4 cell count: women in the lowest CD4 cell count cell group were 11 times more likely to die compared with those in the highest CD4 cell count cell group (RH, 11.19) whereas women in the highest HIV-1 RNA group were approximately five times more likely to die than those in the lowest HIV-1 RNA group (RH, 5.08) (Table 5). Likelihood ratio χ2 square statistics (three degrees of freedom) from this proportional hazards model yielded a χ2 value of 174.51 for CD4 cell count, and 69.55 for HIV RNA (P<10-9 for both). Conclusions were the same if the HIV-1 cut-off points were fixed at commonly used clinical values (as delineated in Table 4) and CD4 cell count cut-off points were selected to match the sizes of the RNA categories. For example, adjusted likelihood ratio χ2 statistics were 186.45 for CD4 cell count and 71.01 for HIV-1 RNA. Kaplan-Meier plots demonstrated greater spread among the curves for CD4 cell count than for HIV-1 RNA, again suggesting that CD4 cell count carries as strong a prognostic value (Fig. 1). However, the progression of RH for HIV-1 RNA was more linear than for CD4 cell count, and the marked increase in RH associated with CD4 cell count occurred in the stratum of CD4 cell count <50×106 cells/l.
The observation period of this study largely antedated the widespread use of HAART. Less than 1% of the women included reported use of HAART prior to the baseline visit, and only 13% of the measured follow-up time could have been influenced by the receipt of such therapy. The findings were not changed by analysis which included only those women who had not received HAART, by censoring follow-up time when HAART was initiated, or by modeling the initiation of HAART as a time-varying covariate in the proportional hazards model.
In this large cohort of HIV-infected North American women, CD4 cell count and HIV-1 RNA were independent predictors of short-term (2.5 year) survival. In contrast with some previous studies, but consistent with others, all of which differ from WIHS in several parameters including sex[1,2,12,18-21], CD4 cell count had greater prognostic value than HIV-1 RNA level, particularly among participants with more advanced immunodeficiency. When the analysis was adjusted to eliminate the distortion created by having disproportionately sized strata of the categorized variables, the RH associated with CD4 cell count became even larger in comparison with that for HIV-1 RNA. Eliminating from the analysis all follow-up time during which participants could have received HAART did not change these findings.
Sex or ethnicity may contribute to these findings. Several reports have demonstrated lower viral burden in women compared with men as stratified by CD4 cell count[13,22-27], and a higher mortality in women with similar HIV-1 RNA values. Two previous studies reporting no sex difference in HIV-1 RNA levels [28,29] may have been limited by small sample size or by a failure to match fully for CD4 cell count. Other authors [13,36] have described findings of median HIV-1 RNA values in both women and African- American individuals that were lower (0.29-0.35 log10 copies/ml HIV-1 RNA) than in white men with similar CD4 cell count. As with our data, these differences may be due to differences other than sex or race in the populations studied. However, higher CD4 cell count and percent in women have been reported consistently among HIV-seronegative individuals, with CD4 cell count higher by approximately 100×106 cells/l in both premenopausal and postmenopausal women compared with age-matched men[27,28,37]. This difference has been reported to persist for at least 5 years after HIV-1 infection, and suggests that stratification by CD4 cell count may not be a valid adjustment for duration of HIV infection. Thus, women may be at a more advanced stage of disease than men when matched by CD4 cell count. If quantitative HIV-1 RNA levels in women are lower than in men in some but not all CD4 cell count strata, or if CD4 cell count is not an appropriate adjustment for duration of infection, then CD4 cell count might become as strong a predictor of survival. These findings suggest that prognostic markers of HIV disease in different demographic groups must be examined before assuming that findings in one group are applicable to all.
Differences in human cellular genes, some yet to be identified and fully understood, may influence disease progression and survival. It is thus possible that sex or ethnicity contributes, either directly or as a surrogate for other unmeasured influences, to the differences demonstrated between our cohort and previously reported cohorts of men. Both the ethnic and sex composition of the WIHS cohort differs from that of other cohorts: the WIHS cohort is comprised entirely of women, of whom 56% are African-American and 24% are Latina. Forty-two percent of the cohort reported exposure to HIV-1 via heterosexual activity, 34% by injection drug use, and 20% did not have a known route of exposure.
However, the differences demonstrated between our data and that of previous studies may be due to differences in study design or to other characteristics of the study populations, particularly duration of HIV-1 infection, disease stage at enrollment, and duration of follow-up. Our cohort had more advanced HIV-1 disease at entry than either the Multicenter AIDS Cohort Study (MACS) [2,12,19]or the AIDS Link to Intravenous Experience (ALIVE) cohort[16,24], as demonstrated by lower median CD4 cell count, higher viral burden, and higher prevalence of prior AIDS-defining clinical events or symptomatic disease. In addition, WIHS was initiated more than a decade after the beginning of the epidemic, and thus includes women who may have been HIV-1 infected for as long as 15 years at time of study enrollment. In MACS the participants had probably been HIV-1 infected for less than 5 years. Thus, there may be selection bias in WIHS favoring long-term survivors, for whom prognostic markers have not been well defined.
Our cohort has been followed for a relatively short period of time compared with cohorts of men. Some studies have indicated that viral burden is a better predictor of long-term outcome in a population which is relatively free of HIV-related disease at study entry, but that CD4 cell count better predicts short-term outcomes in persons with lower CD4 cell count and/or more advanced disease[20,21]. The marked increase in mortality conferred by low CD4 cell count in the WIHS cohort occurs with CD4 cell count <50×106 cells/l, and this degree of CD4 cell count depletion is associated with high mortality even in the presence of low viral burden. WIHS has a larger proportion of participants with CD4 cell count <50×106 cells/l (12.5% of the subjects in this analysis) than did some studies reported previously. The high mortality in this group, and its larger contribution to the WIHS deaths compared with those in the MACS or ALIVE studies, could contribute to the findings of CD4 cell count as a more powerful predictor of survival in WIHS. However, in the MACS, examination of the Kaplan-Meier curves at both 2 and 3 years demonstrates that within each CD4 cell count category, viral load predicts AIDS-free survival: the Kaplan-Meier curves diverge by 2 years of follow-up in all CD4 cell count categories.
Interpretation of the negative prognostic value of higher HIV-1 RNA values in our study suggests that a prognosis comparable to that associated with CD4 cell count cell count <50 ×106 cells/l was not reached until the HIV-1 RNA burden was >500000 copies copies/ml (Tables 2, 3 and 5).
Possible confounding by indication for treatment with HAART must be considered when interpreting our findings. However, during the observation period there was little use of HAART, and analysis to assess this indicates that HAART did not influence the results, which thus apply primarily in the absence of such therapy. Additionally, analysis of WIHS data indicates that in the time period under study, initiation of HAART was independently associated with CD4 cell count but not with HIV-1 RNA level (L. Ahdieh, S. Gange, R. Greenblatt, H. Minkoff, K. Anastos, M. Young, M. Nowicki, A. Kovacs, M. Cohen and A. Muñoz, unpublished data).
The NASBA method was used to quantify HIV-1 RNA levels utilizing CPT. Lower levels of degradation of HIV-1 RNA occur with CPT tubes than with heparinized samples, which were used in both the MACS and ALIVE studies. In addition, other methods of measuring viral burden may yield results somewhat different than the NASBA technique. Vandamme et al.  provide a conversion factor of: log10 reverse transcription (RT)-PCR=1.853+(0.613log10 NASBA copies) in which RT-PCR is performed with Amplicor HIV-1 Monitor (Roche Diagnostic Systems, Branchburg, New Jersey, USA). This results in variation of the magnitude and the direction of the difference between the two assays. The NASBA value of 4000 copies/ml is equivalent to a value of 11550 copies/ml by RT-PCR with this conversion equation. Other authors  provide a different conversion factor: log10 RT-PCR=0.38+(1.01log10 NASBA copies). These differences should be considered when correlating our study results to clinical practice.
Potential limitations in our study include the facts that analyses were performed with all-cause mortality as the primary measured outcome and that some but not all sites were able to match to their local death registries. Including deaths from causes other than HIV-1 related disease, and failure to ascertain deaths among the women lost to follow-up, would result in attenuated associations among viral burden, CD4 cell count and mortality, and thus would result in underestimating the strength of the associations. If the much lower rate of death in the HIV-1-negative women in WIHS (1.1% at 24 months of follow-up) represents the ‚background‚ rate, the dilution effect will be small. This interpretation is supported further by the findings in a WIHS substudy. Among 388 HIV-1-infected women, 34 died during the first 18 months of follow-up. Of the 32 women in whom the cause of death was known, 31 died of medical illnesses, most of which were clearly related to HIV-1 infection. Only one participant died of a drug overdose, and none of the deaths was ascribed directly to violence or trauma.
Our data demonstrate that in this cohort of HIV-1-infected women, CD4 cell count was as strong a predictor of short-term survival as quantitative HIV-1 RNA. This finding differs from some other cohort studies of men, in which HIV-1 RNA levels are a stronger predictor of survival than CD4 cell count. It is unclear whether this difference represents a direct effect of sex on HIV-1 disease manifestations, including viral burden as well as clinical disease, or represents other differences, including more advanced disease stage at study entry, a shorter period of follow-up, cellular genetic factors associated with race/ethnicity or sex. Our analysis indicated that introduction of HAART did not explain this finding. Our data suggest that it is critically important to delineate the prognostic value of different levels of HIV-1 RNA in women both in the presence and in the absence of antiretroviral therapy. Perhaps most important to clinical practice is the delineation of the prognostic value of CD4 cell count and quantitative HIV-1 RNA levels in individuals receiving potent antiretroviral therapy over a longer period of time. This will allow the continued development of treatment recommendations for HIV-1-infected individuals, both women and men. Further analysis of outcomes in the WIHS and other cohorts during this treatment era will help to elucidate these relationships.
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