Role of the CD4 count in HIV management

Financial & competing interests disclosure

CME Author: Charles P Vega, MD,Associate Professor; Residency Director, Department of Family Medicine, University of California, Irvine, USA.Disclosure:Charles P Vega, has disclosed no relevant financial relationships.

Editor: Elisa Manzotti,Editorial Director, Future Science Group.Disclosure:Elisa Manzotti has disclosed no relevant financial relationships.

Authors and Credentials: Jennifer Hoffman, MD,Duke University Medical Center, Durham, NC, USA.Disclosure:Jennifer Hoffman has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

Johan van Griensven, MD, PhD,Prince Leopold Institute for Tropical Medicine, Antwerp, Belgium.Disclosure:Johan van Griensven received support from the Inbev-Baillet Latour Fund (private donation not related to a pharmaceutical company). The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Robert Colebunders, MD, PhD,Prince Leopold Institute for Tropical Medicine, Antwerp, Belgium.Disclosure:Robert Colebunders received grants from Abbott, Tibotec Therapeutics, Pfizer, Bristol-Meyers Squibb, Roche, GlaxoSmithKline, Gilead and Merck. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Mehri McKellar, MD,Duke University Medical Center, Durham, NC, USA.Disclosure:Mehri McKellar received a research grant from Tibotec Therapeutics. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Morbidity and mortality from HIV/AIDS has declined both in the USA, as well as other developed countries, and in developing nations since the advent of HAART [1,2,101]. In clinical trials, different HAART regimens have been found to produce viral suppression in up to 80–90% of subjects [3,4]. However, the long-term durability of potent HAART in clinical practice is not entirely clear. Success rates of clinical trials are not easily translated into clinical practice, and treatment outcomes in a clinic setting have been shown to be worse than those in research trials [3,5]. Treatment failure, whether attributable to virologic failure, stopping HAART or loss to follow-up, leads to increases in morbidity and mortality [6,7]. Having a reliable marker to evaluate disease progression and predict treatment outcomes would be useful for the practitioner and patient alike.

Since the introduction of HAART, much has been studied regarding which factors best predict a patient’s success on HAART. Previously described predictors of treatment failure include poor adherence to medications, one or more missed visits in the previous year, prior virologic failure, a regimen consisting only of nucleosides, higher baseline viral loads and lower baseline CD4 cell counts [3,5,8–10]. Scoring systems have been designed and validated to assess the incidence of clinical disease progression among patients receiving HAART. In a model designed by the EuroSIDA study group, the most recent CD4 cell count, viral load and hemoglobin level were independently related to the risk of disease progression, as was a late presentation of persons with advanced disease, before the start of HAART [11].

Several cohort studies and clinical trials have shown that the CD4 count is the strongest predictor of subsequent disease progression and survival [12,13]. The use of the CD4 count as an independent and reliable marker for treatment outcome is attractive from various aspects. First, CD4 counts are already the most important factor in deciding whether to initiate antiretroviral therapy and opportunistic prophylaxis – all HIV-positive patients in high-income countries, and an increasing number of patients in low-income countries have a baseline CD4 count at entry into care [102]. Second, the CD4 count is a relatively objective and simple marker to follow. Finally, the cost of CD4 counts has become more affordable, including in developing countries [14,15]. This article further evaluates the use of the CD4 count in assessing the clinical status of HIV-infected individuals, in making informed decisions regarding the initiation of antiretroviral therapy and in monitoring the success of such therapy.

What is an adequate CD4 response on HAART?

An adequate CD4 response for most patients on therapy is defined as an increase in the range of 50–150 cells/mm³ per year with an accelerated response in the first 3 months of treatment [102]. In general, CD4 counts should be checked every 3–4 months to determine when to start antiretroviral therapy, to assess immunologic response to therapy and to evaluate the need for initiation or discontinuation of prophylaxis for opportunistic infections (OIs). Patients with good virologic control average approximately 50–100 cells/mm³ per year until a steady-state level is reached [102]. A clinically significant change between CD4 counts approximates a 30% change in the absolute count or an increase or decrease in CD4 percentage by 3% [102]. For those patients who adhere to therapy with sustained viral suppression and are clinically stable for more than 2–3 years, the frequency of CD4 count monitoring may be extended to every 6 months. In cases of discordant CD4 and viral-load results, the clinician should first exclude a laboratory error and consider retesting the patient.

Absolute CD4 counts may fluctuate among individuals or be influenced by factors that affect the total white blood cell count and lymphocyte percentages (Box 1). Bone marrow-suppressive medications and IFN-α may reduce the absolute CD4 count [16,103]. Acute infection, sepsis, malaria and TB can decrease both absolute CD4 counts and percentages [17–19,103]. In turn, a splenectomy or co-infection with human T-cell leukemia virus type 1 may cause misleadingly elevated absolute CD4 counts [20,21]. Although it does not appear to yield clinical effects, administration of IL-2 has been shown to increase CD4 counts [22–24]; and steroids can both increase and decrease CD4 counts [25,103]. Sex, race and psychological and physical stress typically have a minimal effect on CD4 counts [103]. Pregnancy can lead to hemodilution with a small decline in CD4 count but no decline in percentage [103]. In many of these cases, the CD4 percentage remains stable and may be a more appropriate parameter to assess the patient’s immune function.

Baseline CD4 count as a predictor of disease progression & treatment outcome

Numerous studies have demonstrated that the baseline CD4 count serves as a significant prognostic indicator for treatment outcome [5,12,26–29]. In one study, patients starting therapy with a CD4 count below 200 cells/mm³ were almost twice as likely (HR: 1.90) to fail treatment, compared with those starting with a CD4 count higher than 200 cells/mm³[5]. Another study showed an inverse relationship between the CD4 count at baseline and a risk of progression to AIDS or death [12]. This effect was quite dramatic: the adjusted HR for progression to AIDS or death was 0.24 (95% CI: 0.20–0.30) for patients starting HAART with a baseline CD4 count of 200–350 cells/mm³, compared with patients with a CD4 count below 50?cells/mm³.

Recent data support the prognostic value at higher CD4 cell-count levels. In a large cohort study, patients initiating HAART with CD4 counts of 350–500?cells/mm³ had a 94% increased risk of death, relative to those with baseline CD4 counts above 500 cells/mm³[30]. Equally, this and another study documented an increased risk of death when HAART was deferred until the CD4 count fell below 350 cells/mm³[30,31]. These studies and others have renewed and validated the impetus for earlier treatment of HIV.

A similarly strong association has been observed between the baseline CD4 count and the subsequent CD4 response on HAART therapy [26–28,32]. These studies demonstrate that individuals with the highest baseline CD4 count at HAART initiation have the best chance for full immune reconstitution and restoration of a near-normal CD4 count and support a higher CD4 threshold for HAART initiation. In the ACTG 384 study, immune reconstitution was evaluated for five different baseline CD4 strata (<50 to >500 cells/mm³) [32]. Although absolute CD4 count increases were similar in all strata, only patients in the higher baseline strata achieved close to normal CD4 values following 3 years of HAART. In addition, abnormalities in CD4 naive-memory cell-count ratios and T-cell activation markers were clearly more pronounced in the lower CD4 strata, although immune imbalances remained among all patients. The ACTG 384 study suggests that T-cell subsets and ratios may give more detailed prognostic information on immune recovery on HAART than absolute CD4 counts [32]. Importantly, some of these activation markers have already been strongly linked with disease progression [33]. Several recent studies have focused on the prognostic value of inflammatory (e.g., high-sensitivity C-reactive protein and IL-6) and coagulation markers (e.g., d-dimer). Increasingly, it is becoming clear that elevated levels of these markers are predictive of overall mortality and AIDS- and non-AIDS-related events, independent of CD4 counts or viral-load values [34–38].

The importance of the baseline CD4 count also holds true in low-income countries; the Antiretroviral Treatment in Lower-Income Countries (ART-LINC) collaborative has shown that the most important predictor of a patient’s CD4 response on HAART is the baseline CD4 count at the time treatment is initiated [39]. However, it should be noted that substantial improvements in CD4 counts often occur even in patients who are profoundly immunosuppressed at the time of HAART initiation. A study in Cambodia, involving 416 patients with a median CD4 count of 11 cells/mm³ at the time of HAART initiation, showed that an impressive 74% had achieved CD4 counts higher than 200 cells/mm³ 24 months later [40]. Although very low baseline CD4 counts are a risk factor for inadequate CD4 reconstitution on HAART, many patients will still manage to achieve a CD4 count above the critical threshold of 200 cells/mm³ after a period of time on HAART with successful virologic suppression.

Recovery of CD4 cells as a prognostic marker for patients on HAART

The extent of recovery of CD4 cells, once the patient has been placed on HAART, appears to be another important predictor of treatment success; patients who achieve close to normal values could potentially have a normal lifespan [41]. Most studies focus on reducing AIDS-related mortality with progressive increases in CD4 counts while on treatment. Among previously HAART-naive patients, those who obtain CD4 counts between 500–649?cells/mm³ had a 55% higher risk of AIDS or death compared with those with values of at least 650 cells/mm³[42]. However, several studies have clearly illustrated that at higher CD4 counts non-AIDS-related diseases, specifically malignancies, cardiovascular, liver and kidney disease, account for the majority of deaths [43].

In a recent study by Marin et al., the risk of non-AIDS-defining deaths was reduced by approximately 30% for non-AIDS infections, end-stage liver disease and non-AIDS malignancies for each 100 cell/mm³ increment in the latest CD4 count [44]. Similar data has been reported in other studies [45–47]. A fascinating study by Gutierrez et al. demonstrated that among patients starting HAART with sustained virologic suppression, immunologic nonresponders (defined in this study as a <50?cells/mm³ increase in CD4 counts after 1 year on HAART) had more than a four-times greater rate of non-AIDS-related death, compared with those with satisfactory immunologic responses [48]. All together, these findings suggest that subclinical immunodeficiency may be related with long-term risks, both AIDS and non-AIDS related. In line with this, the recent US Department of Health and Human Services guidelines recommend defining immunological failure as the lack of an increase in CD4 counts to more than 350–500?cells/mm³ after 4–7 years of effective HAART [102].

Absolute CD4 count versus CD4 percentage

Several studies listed in Table 1 have shown the utility of the CD4 percentage in providing additional information about prognosis and when to initiate antiretroviral therapy. The CD4 percent appears to be most useful in patients with CD4 counts above 200?cells/mm³. One cohort study by Moore et al. demonstrated that in patients starting HAART with an absolute CD4 count of 200–350?cells/mm³, a CD4 percentage of less than 15% was associated with a markedly increased risk of mortality (relative hazard [RH]: 2.71), in comparison to those subjects with a similar baseline CD4 count but a CD4 percentage above 15% [49]. Another retrospective cohort study by Pirzada et al. demonstrated that CD4 percentage is superior to absolute CD4 counts in predicting time to an AIDS-related event, including for patients not yet on HAART with CD4 counts between 200–350?cells/mm³[50]. A third study by Guiguet et al. demonstrated that CD4 percentage has additional prognostic values in terms of progression to an AIDS-defining event or death in patients with a CD4 count of 350–500?cells/mm³; patients with an absolute CD4 count in this range but a CD4 percentage below 15%, were found to be at risk for an AIDS-defining event or death similar to that of patients with an absolute CD4 count of 200–350?cells/mm³ but a CD4 percentage of over 15% [51]. A study by Hulgan et al. demonstrated that patients with an absolute CD4 count of over 350 cells/mm³ and a CD4 percent below 17% had earlier disease progression, compared with those with a CD4 percent above 17% [52]. It is somewhat surprising that CD4 percentage adds such predictive value even in patients with absolute CD4 counts above 350 cells/mm³. Some of the additional value of the CD4 percentage may be due to the fact that absolute CD4 counts vary depending on the time of day and other factors (e.g., in acute infection) – CD4 percentage is less subject to this variability [50]. However, absolute CD4 count continues to be the superior prognostic indicator for patients with CD4 counts lower than 200 cells/mm³[50].

Discordant CD4 count & HIV-1 viral load

Even though definitions of immunologic success vary between studies, individuals with discordant responses on HAART (‘virological only’, without an appropriate immunological response, or ‘immunologic only’ without viral suppression) consistently do worse than individuals with complete responses (both virological and immunological), yet generally do better than those with no response. One observational, multicenter study found that after 4 years of follow-up, the rate of clinical disease progression was six-times greater in nonresponders, 1.9-times greater in virologic-only responders and 2.3-times greater in immunologic-only responders. However, patients with virologic-only response or with immunologic-only response had a significantly reduced risk for clinical progression than nonresponders [53]. Other studies have demonstrated similar outcomes, with discordant responses being significantly associated with an earlier development of an OI or death [54].

An interesting study by Tan et al. examined a group of treatment-naive patients starting on HAART between 1995 and 2004 [54]. Among these patients, 70% experienced a complete response, 16% experienced an immunologic-only response, 9% had a virologic-only response and 5% had a concordant unfavorable response (neither viral suppression nor an increase of >50 cells/mm³ in CD4 count). Patients who experienced discordant virologic and immunologic responses had a RH of 2.28 for the development of an OI or death, compared with those with a complete response; those in the nonresponse group had an RH of 4.83 for the development of OI or death, compared with the complete response group. Similarly, a study by Moore et al. demonstrated that a discordant immunologic and virologic response was an independent risk factor for mortality (RH: 1.87) [55].

▪ Adequate immunological response despite virological failure (immunologic only)

Risk factors for immunologic-only response include younger age, a lower baseline CD4 count, higher baseline viral load, poor adherence to therapy and antiretroviral drug resistance (Box 2)[53,55–58]. Although the underlying mechanism is not entirely understood, less doubt exists on the management of this type of discordant response. Since current guidelines strongly emphasize the need to achieve undetectable viral loads, treatment change is usually recommended unless treatment options are limited or other underlying causes can be identified.

▪ Poor immunological response despite effective virological response (virologic only)

Most studies have indicated that patients starting on HAART with a lower baseline CD4 count are more likely to develop a virologic-only response, probably related to disturbances in regulatory functions over T-cell homeostasis [59,60]. In addition, older age also seems to be associated with a lower degree of immune reconstitution, even with successful viral suppression (Box 2)[28,56]. Some data have demonstrated that this is related to a decreased thymic activity in older individuals [53,55,56]. Other risk factors for an inadequate immunologic response may include the use of didanosine/tenofovir-containing regimens [57,58].

The pathophysiologic reason for failure to reconstitute a normal CD4 T-cell population despite sustained virologic suppression is an area of ongoing investigation. Several studies have found that genetic polymorphisms, including the Fas receptor (CD95) gene, the Fas ligand (CD178), the IL-6 gene, and the MHC genes are involved in T-cell immunity and affect whether an individual experiences an immunologic response to HAART therapy or not [61,62]. Some investigators have asserted that the lack of reconstitution may be related to increased chronic T-cell activation, higher levels of T-cell apoptosis and a lower production of naive T cells [63]. A recent study by Marziali et al. showed that immunologic nonresponders to HAART (defined as those who experienced <20% increase in CD4 count, or an absolute count <200?cells/mm³ following at least 1 year of therapy) differed from immunologic responders in several ways: reduced numbers of naive and thymic T cells, higher levels of IL-7 indicating persistent immune activation, lower numbers of regulatory T cells and a reduced expression of the IL-7 receptor (IL-7Ra) on CD4 and CD8 T cells [64]. A recent, intriguing case series by Nies-Kraske et al. suggests that fibrosis of the T-cell zone of lymphoid tissue may also be an important factor in the failure to reconstitute T cells [65]. Together, these findings demonstrate that the immune systems of immunologic nonresponders may differ from those of individuals with a successful immune response to HAART.

CD4 count & immune reconstitution inflammatory syndrome

The development of immune reconstitution inflammatory syndrome (IRIS) can occur in the first weeks to months following HAART initiation, particularly among patients with a co-existing OI [66]. IRIS occurs as the immune system is reconstituted during the early period on HAART and is caused by a pathologic immune response to a latent or active infection, which leads to increased inflammatory symptoms. This can occur in response to any infection; however, some of the most common ones include Mycobacterium tuberculosis, other mycobacterial infections, herpes zoster, cytomegalovirus, Pneumocystis (carinii) jiroveci pneumonia and Cryptococcus neoformans[66,67].

CD4 count and CD4 percentage before starting HAART have been shown to be important risk factors in determining which patients develop IRIS after the initiation of HAART. One cohort study of an ethnically diverse population starting on HAART demonstrated that a CD4 percentage of less than 15% was associated with a greater risk of development of IRIS by nearly three-times compared with a CD4 percentage over 15% (OR: 2.97 for a CD4 percentage >10%, and 2.59 for a CD4 percentage of 10–15%) [68]. A recent case-control study at Johns Hopkins University Hospital (MD, USA) revealed that a CD4 count of under 100 cells/mm³ was a strong independent risk factor for the development of IRIS (OR: 6.2) [67]. Other studies have shown similar associations between a lower nadir CD4 count and a higher risk of developing IRIS [69]. The rate of the increase of CD4 count after initiation of HAART may also be a risk factor in the development of IRIS, with patients who have a more rapid rise in CD4 count being at a higher risk for developing IRIS [66]; however, not all studies have confirmed this.

CD4 count monitoring in resource-limited settings

One of the many challenges in deciding when to start and how to monitor patients on HAART in resource-limited settings (RLS) is the inaccessibility and expense of laboratory tests, including CD4 counts and viral load, which are standard of care in the developed world. Since viral-load testing is not widely available, the WHO has developed clinical and immunologic criteria that can be used to define treatment failure in RLS. Clinical failure is defined as the development of a new or recurrent WHO stage 4 condition. Immunologic failure is defined as a persistent CD4 count of under 100?cells/mm³, a fall in CD4 count of more than 50% from the on-treatment peak value or a fall in CD4 count to below the pretreatment value [104]. Since in some locations the availability of CD4 testing is limited, numerous researchers have looked at whether the total lymphocyte count (TLC) can be used as a surrogate marker for CD4 counts with variable results. The correlation between TLC and CD4 appears to be poor in children [70]. The correlation in adults may be slightly better; one study of HIV-infected patients in Nairobi, Kenya found that a TLC cut-off of 1900?cells/mm³ was 81% sensitive and 90% specific in detecting patients with a CD4 count below 200 cells/mm³[71]. Another study in Ethiopia found that a TLC cutoff of 1780 cells/mm³ was 61% sensitive and 62% specific in identifying adults with a CD4 count of less than 200 cells/mm³[72]. The TLC generally increases while on HAART but is not a reliable test to monitor the efficacy of such treatment.

Recently, there has been a great deal of effort towards finding simpler and more cost-effective means of measuring CD4 counts. Novel techniques have been devised, utilizing more affordable flow cytometry methods as well as technologies other than flow cytometry, which requires significant operator expertise in addition to the expense [73–75]. One promising technology is the modified Dynabeads^® method for measuring CD4 counts; the cost for this test is less than US$3/sample, compared with $17/sample for the older Capcellia technology [73,74].

Owing to these developments, CD4 counts are increasingly being measured in many RLS at baseline and for monitoring purposes. However, viral loads remain largely inaccessible and/or cost prohibitive – this can lead to the late identification of virologic failure and development of resistance. This is particularly problematic in RLS, where there are often limited treatment options; therefore, the prevention of resistance remains of the utmost importance. Unfortunately, several studies in RLS have shown that changes in CD4 count cannot be used alone to predictably identify patients with virologic failure [76–79]. A prospective study in South Africa investigated the relationship between CD4 count and virologic failure and found that a negative CD4 count slope was only 53% sensitive and 64% specific for identifying patients with virologic failure [76]. A study in Botswana found that CD4 count increases have a low predictive value for identifying patients with suppressed viral loads; an increase in the absolute CD4 count of more than 50 cells/mm³ after starting HAART was 93% sensitive in identifying patients who had achieved virologic suppression but only 62% specific [78].

Another study by Keiser et al., analyzing outcomes from 10 antiretroviral treatment programs in Africa and South America, demonstrated that the sensitivity of the WHO criteria for immunologic failure in detecting virologic failure among patients on HAART was only 12.6–17.1% in these settings, although it was approximately 97% specific [80]. This raises concerns as presumably the use of immunologic monitoring without concurrent viral-load monitoring would lead to a later detection of treatment failure and delayed switching to a second-line regimen, thus facilitating the development of viral resistance. The converse hazard – premature switching to a second-line regimen based on immunologic criteria in individuals who actually have viral suppression – exists as well. A study of a Namibian population on first-line ART, who met immunologic or clinical criteria for treatment failure, demonstrated that 79% of patients actually had a suppressed viral load when viral-load testing was performed [81]. If the decision to switch to second-line therapy was based on clinical and immunologic criteria alone, many patients would be switched to more expensive and possibly less well-tolerated second-line regimens unnecessarily.

As a result, there has been a considerable interest as to whether medication adherence can be used in addition to the CD4 count for patient monitoring. Assessment of medication adherence may add value to the CD4 count in terms of detecting virologic failure or may even be a better predictor of virologic failure than the CD4 count alone. A study by Bisson et al. found that medication adherence, as measured by pharmacy refills, was independently associated with virologic failure, although the median level of adherence among patients with virologic failure was greater than 90% [77].

Although identifying virologic failure early is important in preventing resistance, it may not have as much of an effect on clinical outcomes in the developing world as might be expected. Resources expended on viral-load monitoring may be better spent on providing more patients with HAART. The recently released results from the Development of Antiretroviral Therapy in Africa (DART) study in Uganda and Zimbabwe demonstrated minimal differences in outcomes between a group of patients on HAART who were clinically monitored versus those receiving both laboratory and clinical monitoring (including CD4 count, complete blood count and chemistries every 12 weeks, but excluding viral loads) [82]. Among the clinical monitoring group, the 5-year survival rate was 87%, compared with 90% in the laboratory and clinical monitoring. This high 5-year overall survival is somewhat encouraging, albeit surprising, as the study group had advanced disease at the time of initiation on HAART. The difference in outcomes only became apparent after the second year of treatment in the 5 year study, indicating that clinical monitoring alone may be feasible during the first 2 years of treatment and that CD4 counts could be reserved for monitoring treatment beyond this point. Similarly, a computer simulation model developed by Phillips et al. showed minimal differences in outcomes between patients monitored by clinical status alone and those monitored with viral load and CD4 count or CD4 count alone [83].

Future perspective

In the next 5–10 years absolute CD4 counts and CD4 percentages will most likely remain the cornerstone for initiating and monitoring patients on therapy. Other markers, such as T-cell subsets as well as activation, inflammatory and/or coagulation biomarkers, may become increasingly important for evaluating disease progression, although their anticipated cost for RLS will pose a formidable challenge. Serious non-AIDS-related diseases, such as liver, cardiovascular, renal and non-AIDS malignancies will contribute to the majority of morbidity and mortality among HIV-infected patients who are stable on HAART. Higher CD4 counts have already been shown to reduce these rates. As antiretroviral treatment continues to improve with fewer side effects and less frequent dosing, the CD4 count threshold for starting therapy is likely to increase. With recent data supporting the cost–effectiveness of starting therapy earlier in South Africa [84], future guidelines on starting antiretroviral therapy at higher CD4 counts should include RLS.

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