Despite the fact that the WHO’s “3 by 5” target—ie,
getting 3 million people in low-income and middle-
income countries on antiretroviral therapy by the end of
2005—was not reached, a lot of progress has been
made.1Today it is evident that to scale up the roll out of
antiretrovirals even more, we will need to further
decentralise antiretroviral services, and this will require
new and less expensive ways to monitor the virological
efficacy of treatment.2,3
In developed countries, the decision to change
antiretroviral therapy is based on a rise in viral load, a fall
in CD4+ lymphocyte counts, and the presence of drug
resistance.4Monitoring the efficacy of treatment in
countries with limited resources is more difficult because
of inadequate laboratory facilities, a shortage of trained
staff, unreliable electricity supply, and costly reagents.
Even where such tests can be done, most HIV patients in
developing countries do not have access to them.5
Here, we review existing methods to monitor the
effectiveness of antiretroviral treatment, and propose a
new model that could be used in resource-limited
settings. The purpose of the model is to diagnose
virological treatment failure early enough to allow a
switch to second-line regimens. Changing treatment
early will prevent resistance from developing further,
both in the individual and in the population.
The model is largely a result of the experience of
physicians working at the Infectious Diseases Institute
(IDI) of the Makerere Medical School in Kampala,
Uganda. The institute’s clinics in Mulago hospital offer
medical treatment free of charge to more then
12 000 adults and 3000 children with HIV, of whom
about 3000 are being treated with antiretrovirals. The
IDI laboratory can measure CD4+ lymphocyte cell
counts and test viral load, but does not have enough
funding to offer these tests routinely to all patients.
Weekly meetings are held to decide which patients are
failing antiretroviral treatment, and whether a switch to
second-line antiretroviral therapy is justified.
Existing methods for monitoring the efficacy
of antiretroviral therapy
The effectiveness of antiretroviral treatment can be
monitored virologically, immunologically, or clinically.
Virological monitoring has been considered the ideal
method for assessing the efficacy of an antiretroviral
regimen. However, HIV RNA viral load testing is costly
($15–150 per test), and needs both adequate laboratory
infrastructure and well-trained personnel. Alternative
technologies are being developed for viral load
testing—eg, heat-denatured quantitative p24 antigen
measurement, the Cavidi real-time reverse tran-
scriptase assay, and real time PCR assays.6-12But these
tests are expensive too (about $3–15 per test). The p24
antigen test might be useful in diagnosing HIV
infection in infants,13but this test and others need
further field validation in resource-poor settings before
they are recommended for monitoring antiretroviral
Ideally, drug resistance should be tested for in any case
of virological treatment failure. Complete genotypic
resistance testing, however, costs about $300 per test. A
way to reduce this cost could be to develop tests that
identify only key genetic mutations such as the Met184Val
mutation, indicating resistance to lamivudine, and the
Lys103Asn and Tyr181Cys/Ile mutation, indicating
resistance to nevirapine or efavirenz.14In developing
countries, the generic fixed drug combination of
lamivudine, stavudine (or zidovudine), and nevirapine is
most widely used.15–17Therefore, knowledge about the
presence or absence of these key mutations could help
decide whether an antiretroviral regimen containing these
drugs should be stopped.
Choosing the best second-line therapy needs
additional information about the presence or absence
of thymidine analogue mutations, since thymidine
analogues—eg, zidovudine or stavudine—are often
used in resource-poor countries.
Lancet Infect Dis2006; 6: 53–59
RC is at the Infectious Disease
Institute, Faculty of Medicine,
Makerere University, Kampala,
Uganda, and the Prince Leopold
Institute of Tropical Medicine
and University of Antwerp,
Antwerp, Belgium; KRM, JL, HMS,
FS, FL, SB-K, and HM-K are at the
Infectious Disease Institute,
Faculty of Medicine, Makerere
University, Kampala; LL is at
University of Antwerp, Antwerp;
LS, SJR, and TCQ are at The
National Institute of Allergy and
Infectious Diseases, National
Institutes of Health, Bethesda,
Maryland and Johns Hopkins
University, Baltimore, USA; and
BV is at the Boston University
School of Medicine, Boston, USA.
Dr Robert Colebunders, Prince
Leopold Institute of Tropical
Medicine, 2000 Antwerp,
http://infection.thelancet.com Vol 6 January 200653
Robert Colebunders, Kamya R Moses, John Laurence, Hasan M Shihab, Fred Semitala, Fred Lutwama, Sabrina Bakeera-Kitaka, Lut Lynen,
Lisa Spacek, Steven J Reynolds, Thomas C Quinn, Brant Viner, Harriet Mayanja-Kizza
Monitoring the efficacy of antiretroviral treatment in developing countries is difficult because these countries
have few laboratory facilities to test viral load and drug resistance. Those that exist are faced with a shortage of
trained staff, unreliable electricity supply, and costly reagents. Not only that, but most HIV patients in resource-
poor countries do not have access to such testing. We propose a new model for monitoring antiretroviral
treatment in resource-limited settings that uses patients’ clinical and treatment history, adherence to treatment,
and laboratory indices such as haemoglobin level and total lymphocyte count to identify virological treatment
failure, and offers patients future treatment options. We believe that this model can make an accurate diagnosis
of treatment failure in most patients. However, operational research is needed to assess whether this strategy
works in practice.
A new model to monitor the virological efficacy of
antiretroviral treatment in resource-poor countries
CD4+ lymphocyte counts are useful in detecting
asymptomatic patients or patients with minor symptoms
(WHO stage II disease) who need antiretroviral therapy,
or determining when to start or stop prophylaxis for
opportunistic infections. But it is of less value as an
indirect measure of antiretroviral efficacy. Indeed, in
most instances once a patient has failed immuno-
logically, viral resistance has already evolved.
Moreover, the change in CD4+ count might vary from
one patient to another regardless of the virological
efficacy of a treatment regimen. A patient with only a
slight increase in CD4+ levels (?50 cells/µL after one
year of highly active antiretroviral therapy [HAART]) has
not necessarily failed treatment.18,19Conversely, a patient
on an ineffective regimen, and in whom the HIV strain
is resistant to only one or two drugs in the treatment,
may have a continued rise in CD4+ count.20,21WHO
criteria for immunological treatment failure are a CD4+
count that falls below the baseline level or one that falls
by more than 50% after an initial increase.1
Ideally, CD4+ levels should be measured when the
patient does not have an active opportunistic infection
since intercurrent infections can cause these levels to
fall.22But in resource-poor settings, many opportunistic
infections are difficult to diagnose. Therefore, in a patient
who is not doing well clinically, with a falling CD4+
count, it is often unclear whether this is because of an
intercurrent illness, HIV disease progression, or both.
In Africa, measuring CD4+ levels with flow cytometry
can cost $4–25 per test. Because of this high cost,
alternative tests are under investigation, including
microscopic, bead-based manual methods (Dynabeads,
cytospheres) and more affordable modified flow
cytometry (Guava, Cyflow, PointCARE) and other
methods.23,24But these tests are expensive and can be
Measuring the extent of immune activation could be
another way to provide useful information about the
virological response and immune reconstitution of a
patient taking antiretroviral therapy. In European
cohorts, the proportion of CD8+ T cells expressing the
activation marker CD38+ correlated well with the
virological response.30Although data from the Cote
d’Ivoire suggest that monitoring immune activation
could have a role in assessing treatment failure,31its use
in the presence of endemic parasitic or other co-
infections remains unclear, since such infections may
render these markers non-specific.
In poor countries, few people have access to CD4+
count testing. Therefore, it is worth investigating whether
simple laboratory tests such as total lymphocyte count and
haemoglobin levels might be used to predict
immunological failure during
investigators have shown that once a patient’s total
lymphocyte count falls below 1200 cells/µL, the likelihood
that they have a CD4+ count below 200 cells/µL is more
than 90%.32–38There is, however, no total lymphocyte
count cut-off value that has both a high enough specificity
and sensitivity for detecting patients with a CD4+ count
lower than 200 cells/µL.32–38In the Multi-AIDS Cohort
Study, a rapid fall in total lymphocyte count or
haemoglobin concentration indicated an increased
likelihood that HIV infection would progress to AIDS.39
In another study, within the first 2 years of HAART,
whether or not total lymphocyte count rose or fell
emerged as a strong marker for the direction of
concomitant change in CD4+ (sensitivity 86–94% and
specificity 80–85%, depending on length of interval).40
Despite the fact that studies demonstrate increases in
total lymphocyte count in patients receiving HAART,39,40
whether this assay could be a useful method for
monitoring the efficacy of treatment is unclear.41–43
Haemoglobin response to antiretroviral therapy may
also be monitored inexpensively. Although it might not
be the case in all patients treated with zidovudine,44
studies have shown that haemoglobin levels generally
increase in patients on HAART.45,46In a Belgian study in
patients on HAART,42an increase of haemoglobin level
and total lymphocyte count above baseline values at
week 24 diagnosed virological treatment failure with a
sensitivity of 46·9%, a specificity of 59·8%, a positive
predictive value of 76%, and a negative predictive value
of 29·2%. A fall in haemoglobin level and total
lymphocyte count below baseline was as accurate in
predicting virological treatment failure as was a fall in
CD4+ count below baseline.42
As resource-poor settings get better access to
antiretrovirals, they will need monitoring systems that
are different from those in developed countries. Without
laboratory support, especially in rural settings, there will
be a need to rely more on clinical monitoring, using
symptoms and signs predictive of virological failure.
HAART decreases the incidence of opportunistic
infection.47,48Clinical signs suggesting treatment failure,
proposed by WHO are: the appearance of new or
recurrent WHO stage III and IV conditions.1
However, during the first 3–6 months after starting
HAART, clinicians might find it difficult to diagnose
treatment success or failure on the basis of clinical
findings alone. In the months after starting on HAART,
patients can develop symptoms that are not caused by
treatment failure but instead represent the side-effects of
antiretroviral therapy, an immune reactivation inflam-
matory syndrome (IRIS),49opportunistic infections that
continue to appear because the patient is still immuno-
compromised, or an infection or re-infection by a
common endemic pathogen such as tuberculosis or
In South Africa, Grant and co-workers have shown
that individuals on HAART had a major reduction in
http://infection.thelancet.com Vol 6 January 2006
mortality, although this benefit was only seen after
several months of treatment.50If a patient adheres to a
good HAART regimen and has not previously had
antiretrovirals, very few treatment failures will occur
during the first 6 months.51After 6 months of HAART,
clinical manifestations will be more useful for predicting
treatment success or failure. This will be particularly so
in patients who were symptomatic (WHO stage III and
IV) at the start of antiretroviral therapy, a group that
currently represents most of those who start
antiretroviral therapy in countries with limited
resources. These patients’ symptoms will have regressed
or disappeared, and they will have gained weight. In
addition, although they might still have occasional
episodes of oral candidiasis, they should not develop
new severe opportunistic infections.
We therefore propose that in symptomatic patients the
effect of HAART can be seen in HIV-related symptoms
and signs—eg, prurigo, chronic diarrhoea, HIV-related
polyneuritis, and HIV-related cognitive disorders in
adults. In children, growth, neurological, and sexual
development are additional features to follow (panel).
In our experience in Uganda, the disappearance or
reappearance of prurigo seems to be a good indicator of
treatment success or failure. Prurigo occurs in at least
10% of African patients with advanced HIV disease.52
Once prurigo appears, in the absence of antiretroviral
treatment, itching and papular eruptions generally
persist and symptomatic treatment is ineffective.
Occasionally, the number of prurigo lesions might rise
shortly after starting HAART (probably due to IRIS) but
in most patients the prurigo disappears within a few
months after starting antiretroviral therapy and
reappears within weeks after HAART is stopped (RC,
H Byakwaga, personal observation).
Asking what symptoms were present before
antiretroviral therapy was started is important. Often,
these symptoms will have disappeared during therapy.
If some of these symptoms reappear, this, in our
experience, suggests treatment failure. HIV-related
polyneuritis is a much less useful clinical index
because these patients have been exposed to
antiretroviral and other drugs capable of producing
peripheral neuropathy. An unexplained weight loss
after an initial weight gain during antiretroviral treat-
ment is another clinical indicator that might suggest
Kaposi’s sarcoma lesions, particularly if lesions are
very extensive and associated with oedema, may increase
after starting HAART because of an IRIS.53However,
mucocutaneous lesions that reduce in size are a good
indicator that the patient is on an effective antiretroviral
A new model for monitoring antiretroviral
The traditional western model of monitoring patients on
antiretroviral treatment with regular viral load and CD4+
lymphocyte counts is not feasible in poor countries. On
the other hand, monitoring only for clinical and
immunological failure is a risky strategy, both for
patients and for the community as a whole. Waiting for
clinical failure might leave patients susceptible to
serious opportunistic infections and allowing patients to
fail virologically for prolonged periods of time will lead
http://infection.thelancet.com Vol 6 January 2006 55
Panel:HIV-related symptoms or signs predicting
Unexplained persistent diarrhoea
Unexplained persistent fever
Unexplained weight loss
Unexplained cognitive impairment
Loss of developmental milestones in children
Growth retardation in children
*Drug-induced polyneuritis should be excluded
Risk factors for virological failurePredictive value
of the criteria
Previous monotherapy or bi-therapy with NRTIs for more than 6 months
Previous exposure to nevirapine for the prevention of mother-to-child transmission of HIV
Infected with HIV by partner with a history of antiretroviral exposure
Current “weak” antiretroviral regimen (eg, 3 NsRTI, or 2 NsRTI and 1 NtRTI)
Long-term use of drugs that could reduce antiretroviral drug levels in the system
Day-to-day adherence score (?95% but ?80%)
Day-to-day adherence score (?80% but ?60%)
History of stopping an NNRTI-containing regimen without continuing NRTIs for at least 5 days
Appearance or worsening of unexplained prurigo
Reappearance of unexplained prurigo and at least one other HIV-related symptom or sign
(not in first 6 months, or not thought to be IRIS)
Reappearance of at least two other HIV-related symptoms or signs (not in first 6 months or
not thought to be IRIS)
Body weight equal or lower than the patient’s weight before starting HAART or more than 10% Minor
weight loss from peak values in the absence of signs of lipoatrophy
Development of a new WHO stage IV opportunistic infection (excluding extrapulmonary
tuberculosis and IRIS) or malignancy (not in first 6 months or not thought to be IRIS)
A recurrent WHO stage III opportunistic infection
Tuberculosis and no evidence of tuberculosis IRIS (abscess /cavity formation)
Worsening Kaposi’s sarcoma
Worsening after initial improvement of Kaposi’s sarcoma
Unexplained fall of haemoglobin of 10% on two occasions and a reduction in TLC of 50% from
peak values on consecutive testing or haemoglobin and TLC falling below baseline on two
A reduction in CD4+ count to 50% from peak values on two consecutive tests or
A CD4+ count below baseline values on two consecutive tests*
*These tests should ideally be done in the absence of an acute intercurrent illness. NRTI=nucleotide reverse trasncriptase
inhibitors, singly phosphorylated (NsRTI) or triply phosphorylated (NtRTI); NNRTI=non nucleoside reverse transcriptase
inhibitors; TLC=total lymphocyte count.
Table: System for assessing the risk of virological failure for a first-line regimen. To be used after at least
6 months of treatment; treatment failure is estimated as probable if at least one major or at least three
minor criteria from different categories are met.
to drug-resistance mutations in the HIV virus and
facilitate the spread of resistant viral strains.
If this happens on a large scale, the benefits of the
antiretroviral scale-up programmes will be short-lived.
To limit the development of resistance, and to keep
monitoring feasible and affordable, we propose a new
model for monitoring the virological success of
Steps to assess virological treatment failure
The table and figure show our algorithmic approach for
determining the need to change therapy or seek further
testing. The assessment system (table) uses minor and
major WHO criteria (panel) to predict virological
failure. Criteria not detailed by WHO were scored as
major or minor by doctors at the IDI, and by all co-
authors and reviewers
Acknowledgments). The steps involved in the
assessment system are as follows.
of the paper (see
Obtain an antiretroviral treatment history
Previous use of antiretrovirals either with monotherapy
or bi-therapy, particularly if these drugs are also
included in the new drug regimen, will increase the risk
of treatment failure. One dose of nevirapine during
deliver had initially been shown to cause resistance in
about 20% of women,55,56but more recently researchers
have shown using real-time PCR analysis that an
additional 40% of women with previously undetectable
resistance also had the Lys103Asn mutation.57
Therefore, women should always be asked about the use
of nevirapine during pregnancy.
A related question is whether a non-nucleoside
reverse transcriptase inhibitor regimen has been
interrupted outside efforts
transmission (eg, because of side-effects or inability to
pay for the drugs). Stopping the combination of
continuing the nucleoside reverse transcriptase
inhibitor for at least 7 days may lead to resistance
because of the long half-life of nevirapine.58
to prevent vertical
and nevirapine without
Assess the quality of the HAART regimen and concomitant
A regimen containing three nucleoside analogues may
not be sufficiently effective59–62—eg, one containing
efavirenz, tenofovir, and didanosine is associated with
treatment failure.63,64In addition, the use of concomitant
phenytoin—might reduce antiretroviral drug levels.65
Assess adherence to treatment
A clear association between extent of adherence and
treatment outcome has been documented in several
studies.66–73Assessing a patient’s treatment adherence is
believed to be difficult.69Our experience in Uganda is
that obtaining reliable information about adherence is
possible for most patients, provided that experienced,
relationship with the patient. In Uganda, the visual
analogue scale has proven to be a simple and useful
method to evaluate adherence.74
treatment cards, similar to those used for tuberculosis
patients, can be used to improve and assess a patient’s
adherence to treatment, and new devices such as
computerised pill boxes, allowing remote monitoring by
wireless technology of adherence by transmitting
information about the time the pill box is opened, could
be useful tools for the future.
Adequately monitoring treatment adherence could
help prevent treatment failure and the development of
drug resistance. A patient who meets one of the
adherence criteria for treatment failure will most
probably have a detectable level of viral load. However,
this does not automatically mean that the patient is
infected with a drug-resistant virus. If there are no other
criteria suggesting treatment failure, improving
adherence with the current treatment regimen should
be tried before considering a change in therapy.
establish a good
Assess clinical symptom development and laboratory tests
Viral load testing should be considered when patients
have mixed indicators that suggest both treatment
http://infection.thelancet.com Vol 6 January 2006
At least three minor
criteria from different
categories, or one major
No criteria Only one minor
At least two minor
Assess the patients’
Look for criteria
Will knowing viral
Measure viral load
No need for viral
have failed or
Figure:Algorithm to monitor the virological efficacy of an antiretroviral regimen when there is no or little
access to viral load testing
failure and success. However, treatment providers must
always take into account their patients’ treatment
options. For many patients in resource-poor settings,
these options are limited. If a patient without access to
second-line antiretroviral therapy has clinical symptoms
or signs suggesting treatment failure, they have only
two options—continue first-line therapy or stop it. If the
patient’s decision is made irrespective of the result of a
viral load test, then such a test is a waste of resources.
If treatment failure is suspected, there is little sense in
maintaining the antiretroviral regimen in view of the
high cost of treatment, the public health risk of
transmitting the resistant virus to others, and the risk to
the patient of accumulating genetic mutations that
might make future treatment less likely to work. On the
other hand, even a failing antiretroviral regimen,
particularly one containing nucleoside analogues and a
protease inhibitor, can still have a beneficial effect on a
patient’s immune system.75This might be one reason to
continue a failing antiretroviral regimen if no other
treatment option is available. If this option is chosen, at
each visit health-care workers must reinforce the use of
condoms to prevent the transmission of resistant virus.
The model’s potential effect
With an optimal non-nucleoside reverse transcriptase
inhibitor first-line regimen and a good strategy to obtain
the best adherence to treatment, we expect that at least
80% of people will have an undetectable viral load at
12 months, similar to the rates in industrialised
and in reports from resource-poor
If we assume that our model could identify 90% of the
80% of patients on a successful treatment and 15% of
the 20% patients on a failing regimen, viral load testing
will be needed to guide therapeutic decisions in only
15% of patients. Thus, only a few samples would need
to be sent to a laboratory. If the medical infrastructure
in poor countries could provide reference laboratories
and new, simple, and inexpensive but reliable ways to
send samples to these laboratories, the application of
our model could greatly reduce the number of
expensive laboratory tests
compromising antiretroviral treatment.
Whether or not our model will achieve its potential
will need to be proven in prospective studies in a variety
of settings and by different categories of health-care
Our model aims to improve the sensitivity, specificity,
and positive predictive value of the WHO’s criteria for
Although our model is based mainly on experience
and few published data, we felt it important to publish
our proposal since a growing number of people in poor
countries are on ineffective antiretroviral regimens, and
doctors in these countries find it difficult to diagnose
The model’s effectiveness will rely on health-care
workers having good clinical skills. These workers might
need training to improve clinical skills and the ability to
interpret supporting laboratory results.
Evaluation of adherence has a key role in the model.
antiretroviral programmes must include strategies to
obtain optimal treatment adherence to prevent the
emergence of drug-resistance. It is probably more cost-
effective to fund training programmes for treatment
counsellors and to support a multidisciplinary medical
staff dedicated to following up each patient than to
spend money on expensive laboratory testing.
This model could become a valuable tool during the
scaling-up of antiretroviral treatment in developing
countries, but it needs further study to determine
whether this strategy will be useful in resource-limited
Conflicts of interest
We declare that we have no conflicts of interest.
We thank David Bangsberg, Alain Bouckenooghe, Emanuel Bottieau,
Stevens Callens, Paul De Munter, Meg Doherty, Luc Kestens, Allan
Ronald, Walter Schlech, Patrick Soentjens, Eric Van Wyngaerden, and
Ian Woolley for reviewing the paper before submission. We also thank
the Academic Alliance Foundation for financial support.
1 WHO. Scaling up antiretroviral therapy in resource-limited
settings: treatment guidelines for a public health approach. 2003
revision. Geneva: WHO, 2003.
2 Stephenson J. Cheaper HIV drugs for poor nations bring a new
challenge: monitoring treatment. JAMA 2002; 288: 151–53.
3 Kumarasamy N, Flanigan TP, Mahajan AP, Carpenter CC,
Mayer KH, Solomon S. Monitoring HIV treatment in the
developing world. Lancet Infect Dis 2002; 2: 656–57.
4 Smith CJ, Staszewski S, Sabin CA, et al. Use of viral load measured
after 4 weeks of highly active antiretroviral therapy to predict
virologic outcome at 24 weeks for HIV-1-positive individuals.
J Acquir Immune Defic Syndr 2004; 37: 1155–59.
5 Nkengasong JN, Adje-Toure C, Weidle PJ. HIV antiretroviral drug
resistance in Africa. AIDS Rev 2004; 6: 4–12.
6 Schupbach J, Boni J, Bisset LR, et al. HIV-1 p24 antigen is a
significant inverse correlate of CD4 T-cell change in patients with
suppressed viremia under long-term antiretroviral therapy. J Acquir
Immune Defic Syndr 2003; 33: 292–99.
7 Greengrass VL, Turnbull SP, Hocking J, et al. Evaluation of a low
cost reverse transcriptase assay for plasma HIV-1 viral load
monitoring. Curr HIV Res 2005; 3: 183–90.
8 Stevens G, Rekhviashvili N, Scott LE, Gonin R, Stevens W.
Evaluation of two commercially available, inexpensive alternative
assays used for assessing viral load in a cohort of human
immunodeficiency virus type 1 subtype C-infected patients from
South Africa. J Clin Microbiol 2005; 43: 857–61.
9 Schupbach J, Boni J, Flepp M, Tomasik Z, Joller H, Opravil M.
Antiretroviral treatment monitoring with an improved HIV-1 p24
antigen test: an inexpensive alternative to tests for viral RNA.
J Med Virol 2001; 65: 225–32.
10 Ledergerber B, Flepp M, Boni J, et al. Human immunodeficiency
virus type 1 p24 concentration measured by boosted ELISA of heat-
denatured plasma correlates with decline in CD4 cells, progression
to AIDS, and survival: comparison with viral RNA measurement.
J Infect Dis 2000; 181: 1280–88.
11 Pascual A, Cachafeiro A, Funk ML, Fiscus SA. Comparison of an
assay using signal amplification of the heat-dissociated p24 antigen
with the Roche Monitor human immunodeficiency virus RNA
assay. J Clin Microbiol 2002; 40: 2472–75.
http://infection.thelancet.com Vol 6 January 200657
12 Braun J, Plantier JC, Hellot MF, et al. A new quantitative HIV load
assay based on plasma virion reverse transcriptase activity for the
different types, groups and subtypes. AIDS 2003; 17: 331–36.
13 De Baets AJ, Edidi BS, Kasali MJ, et al. Pediatric human
immunodeficiency virus screening in an African district hospital.
Clin Diagn Lab Immunol 2005; 12: 86–92.
14 Sharma PL, Nurpeisov V, Hernandez-Santiago B, Beltran T,
Schinazi RF. Nucleoside inhibitors of human immunodeficiency
virus type 1 reverse transcriptase. Curr Top Med Chem 2004; 4:
15 Coetzee D, Hildebrand K, Boulle A, et al. Outcomes after two years
of providing antiretroviral treatment in Khayelitsha, South Africa.
AIDS 2004; 18: 887–95.
16 Harries AD, Libamba E, Schouten EJ, Mwansambo A,
Salaniponi FM, Mpazanje R. Expanding antiretroviral therapy in
Malawi: drawing on the country’s experience with tuberculosis.
BMJ 2004; 329: 1163–66.
17 Laurent C, Kouanfack C, Koulla-Shiro S, et al. Effectiveness and
safety of a generic fixed-dose combination of nevirapine, stavudine,
and lamivudine in HIV-1-infected adults in Cameroon: open-label
multicentre trial. Lancet 2004; 364: 29–34.
18 Florence E, Lundgren J, Dreezen C, et al. Factors associated with a
reduced CD4 lymphocyte count response to HAART despite full
viral suppression in the EuroSIDA study. HIV Med 2003; 4: 255–62.
19 Lederman HM, Williams PL, Wu JW, et al. Incomplete immune
reconstitution after initiation of highly active antiretroviral therapy
in human immunodeficiency virus-infected patients with severe
CD4+ cell depletion. J Infect Dis 2003; 188: 1794–803.
20 Brigido L, Rodrigues R, Casseb J, et al. CD4+ T-cell recovery and
clinical outcome in HIV-1-infected patients exposed to multiple
antiretroviral regimens: partial control of viremia is associated with
favorable outcome. AIDS Patient Care STDS 2004; 18: 189–98.
21 Buckheit RW Jr. Understanding HIV resistance, fitness, replication
capacity and compensation: targeting viral fitness as a therapeutic
strategy. Expert Opin Investig Drugs 2004; 13: 933–58.
22 Wolday D, Hailu B, Girma M, Hailu E, Sanders E, Fontanet AL.
Low CD4+ T-cell count and high HIV viral load precede the
development of tuberculosis disease in a cohort of HIV-positive
Ethiopians. Ethiop Med J 2003; 41 (suppl 1): 67–73.
23 Lyamuya EF, Kagoma C, Mbena EC, et al. Evaluation of the
FACScount, TRAx CD4 and Dynabeads methods for CD4
lymphocyte determination. J Immunol Methods 1996; 195: 103–12.
24 Mwaba P, Cassol S, Pilon R, et al. Use of dried whole blood spots to
measure CD4+ lymphocyte counts in HIV-1-infected patients.
Lancet 2003; 362: 1459–60.
25 Teav S, Lynen L, Vereecken C, De Munter P, Srey C, Kestens L.
Alternative CD4 counting using Cyflow in Cambodia: precision
and comparison with Facscount. XV International AIDS
Conference; Bangkok, Thailand; July 11–16, 2004.
26 Beretta A, Barbesti S, Carcelain G. A simplified flow cytometry of
CD4 and CD8 cell counting based on termoresistant reagents:
implications for large scale monitoring of HIV infected patients in
resource limited settings. 2nd IAS Conference on HIV
Pathogenesis and Treatment; Paris, France; July 13–16, 2003.
27 Cohen J. Monitoring treatment: at what cost? Science 2004; 304:
28 Kent DM, McGrath D, Ioannidis JP, Bennish ML. Suitable
monitoring approaches to antiretroviral therapy in resource-poor
settings: setting the research agenda. Clin Infect Dis 2003; 37 (suppl
29 Diagbouga S, Chazallon C, Kazatchkine MD, et al. Successful
implementation of a low-cost method for enumerating CD4+ T
lymphocytes in resource-limited settings: the ANRS 12-26 study.
AIDS 2003; 17: 2201–08.
30 Tilling R, Kinloch S, Goh LE, et al. Parallel decline of
CD8+/CD38++ T cells and viraemia in response to quadruple
highly active antiretroviral therapy in primary HIV infection.
AIDS 2002; 16: 589–96.
31 Ondoa P, Koblavi-Deme S, Borget MY, Nolan ML, Nkengasong JN,
Kestens L. Assessment of CD8 T cell immune activation markers to
monitor response to antiretroviral therapy among HIV-1 infected
patients in Cote d’Ivoire. Clin Exp Immunol 2005; 140: 138–48.
32 Post FA, Wood R, Maartens G. CD4 and total lymphocyte counts as
predictors of HIV disease progression. QJM 1996; 89: 505–08.
33 Blatt SP, Lucey CR, Butzin CA, Hendrix CW, Lucey DR. Total
lymphocyte count as a predictor of absolute CD4+ count and CD4+
percentage in HIV-infected persons. JAMA 1993; 269: 622–26.
34 Beck EJ, Kupek EJ, Gompels MM, Pinching AJ. Correlation
between total and CD4 lymphocyte counts in HIV infection: not
making the good an enemy of the not so perfect. Int J STD AIDS
1996; 7: 422–28.
35 Mekonnen Y, Dukers NH, Sanders E, et al. Simple markers for
initiating antiretroviral therapy among HIV-infected Ethiopians.
AIDS 2003; 17: 815–19.
36 Jacobson MA, Liu L, Khayam-Bashi H, Deeks SG, Hecht FM,
Kahn J. Absolute or total lymphocyte count as a marker for the
CD4 T lymphocyte criterion for initiating antiretroviral therapy.
AIDS 2003; 17: 917–19.
37 Kamya MR, Semitala FC, Quinn TC, et al. Total lymphocyte count
of 1200 is not a sensitive predictor of CD4 lymphocyte count
among patients with HIV disease in Kampala, Uganda. Afr Health
Sci 2004; 4: 94–101.
38 Akinola NO, Olasode O, Adediran IA, et al. The search for a
predictor of CD4 cell count continues: total lymphocyte count is
not a substitute for CD4 cell count in the management of HIV-
infected individuals in a resource-limited setting. Clin Infect Dis
2004; 39: 579–81.
39 Lau B, Gange SJ, Phair JP, Riddler SA, Detels R, Margolick JB.
Use of total lymphocyte count and hemoglobin concentration for
monitoring progression of HIV infection. J Acquir Immune Defic
Syndr 2005; 39: 620–25.
40 Mahajan AP, Hogan JW, Snyder B, et al. Changes in total
lymphocyte count as a surrogate for changes in CD4 count
following initiation of HAART: implications for monitoring in
resource-limited settings. J Acquir Immune Defic Syndr 2004; 36:
41 41Badri M, Wood R. Usefulness of total lymphocyte count in
monitoring highly active antiretroviral therapy in resource-limited
settings. AIDS 2003; 17: 541–45.
42 Florence E, Schrooten W, Van Esbroek M, et al. The role of non-
viral load surrogate markers in HIV-positive patient monitoring
during antiviral treatment. HAART monitoring without viral load.
Int J STD AIDS 2004; 15: 538–42.
43 Schreibman T, Friedland G. Use of total lymphocyte count for
monitoring response to antiretroviral therapy. Clin Infect Dis 2004;
44 Buskin SE, Sullivan PS. Anemia and its treatment and outcomes in
persons infected with human immunodeficiency virus. Transfusion
2004; 44: 826–32.
45 Semba RD, Shah N, Klein RS, et al. Highly active antiretroviral
therapy associated with improved anemia among HIV-infected
women. AIDS Patient Care STDS 2001; 15: 473–80.
46 Moyle G, Sawyer W, Law M, Amin J, Hill A. Changes in
hematologic parameters and efficacy of thymidine analogue-based,
highly active antiretroviral therapy: a meta-analysis of six
prospective, randomized, comparative studies. Clin Ther 2004; 26:
47 Badri M, Wilson D, Wood R. Effect of highly active antiretroviral
therapy on incidence of tuberculosis in South Africa: a cohort
study. Lancet 2002; 359: 2059–64.
48 Hung CC, Chang SC. Impact of highly active antiretroviral therapy
on incidence and management of human immunodeficiency virus-
related opportunistic infections. J Antimicrob Chemother 2004; 54:
49Shelburne SA III, Hamill RJ, Rodriguez-Barradas MC, et al.
Immune reconstitution inflammatory syndrome: emergence of a
unique syndrome during highly active antiretroviral therapy.
Medicine (Baltimore) 2002; 81: 213–27.
50 Grant A, Fielding K, Charalambous S. Risk factors for early
mortality among HIV-infected individuals starting antiretroviral
therapy in South Africa. 3rd IAS Conference on HIV Pathogenesis
and Treatment; Rio de Janeiro, Brazil; July 24–27, 2005. Abstract
51 van Leth F, Phanuphak P, Ruxrungtham K, et al. Comparison of
first-line antiretroviral therapy with regimens including nevirapine,
efavirenz, or both drugs, plus stavudine and lamivudine: a
randomised open-label trial, the 2NN study. Lancet 2004; 363:
http://infection.thelancet.com Vol 6 January 2006
Personal View Download full-text
52 Colebunders R, Mann JM, Francis H, et al. Generalized papular
pruritic eruption in African patients with human
immunodeficiency virus infection. AIDS 1987; 1: 117–21.
53 Leidner R, Aboulafia D. Flare of AIDS-associated Kaposi’s sarcoma
(KS) following HAART: a manifestation of immune reconstitution
syndrome (IRS). 8th International Conference on Malignancies in
AIDS and Other Immunodeficiencies; Bethesda, USA; April 29–30,
54 Gill J, Bourboulia D, Wilkinson J, et al. Prospective study of the
effects of antiretroviral therapy on Kaposi sarcoma—associated
herpesvirus infection in patients with and without Kaposi’s
sarcoma. J Acquir Immune Defic Syndr 2002; 31: 384–90.
55 Eshleman SH, Guay LA, Mwatha A, et al. Comparison of
nevirapine (NVP) resistance in Ugandan women 7 days vs. 6-8
weeks after single-dose nvp prophylaxis: HIVNET 012. AIDS Res
Hum Retroviruses 2004; 20: 595–99.
56 Eshleman SH, Guay LA, Mwatha A, et al. Characterization of
nevirapine resistance mutations in women with subtype A vs. D
HIV-1 6-8 weeks after single-dose nevirapine (HIVNET 012).
J Acquir Immune Defic Syndr 2004; 35: 126–30.
57 Johnson J. Resistance emerges in the majority of women provided
intrapartum single-dose nevirapine. 12th Conference on
Retroviruses and Opportunistic Infections; Boston, MA, USA; Feb
58 Mackie NE, Fidler S, Tamm N, et al. Clinical implications of
stopping nevirapine-based antiretroviral therapy: relative
pharmacokinetics and avoidance of drug resistance. HIV Med
2004; 5: 180–84.
59 Gallant JE, Rodriguez A, Weinberg W, Young B, Berger D,
Lim ML. Early non-response to tenofovir DF + abacavir and
lamivudine in a randomized trial compared to efavirenz + abacavir
and 3TC: ESS30009 unplanned interim analysis. 43rd Interscience
Conference on Antimicrobial Agents and Chemotherapy; Chicago,
IL, USA; Sept 14–17, 2003.
60 Khanlou H, Yeh V, Guyer B, Farthing C. Early virologic failure in a
pilot study evaluating the efficacy of abacavir, lamivudine and
tenofovir in treatment naive HIV-infected patients. AIDS Patient
Care STDS 2005; 3: 135–40
61 Gulick RM, Ribaudo HJ, Shikuma CM, et al. Triple-nucleoside
regimens versus efavirenz-containing regimens for the initial
treatment of HIV-1 infection. N Engl J Med 2004; 350: 1850–61.
62 Ruane PJ, DeJesus E. New nucleoside/nucleotide backbone
options: a review of recent studies. J Acquir Immune Defic Syndr
2004; 37: S21–29.
63 Moyle G, Maitland D, Hand J, Mandalia S, Nelson M, Gazzard B.
Early virological failure in persons with viral loads >100,000 cps/ml
and CD4 counts <200 cells/mm3 receiving ddI/tenofovir/efavirenz
as initial therapy: results from a randomized comparative trial. 44th
Interscience Conference on Antimicrobial Agents and
Chemotherapy; Washington DC, USA; Oct 30–Nov 2, 2004.
64 Podzamczer D, Ferrer E, Gatell JM, Niubo J, Dalmau D, Leon A.
Early virological failure and occurrence of resistance in naïve
patients receiving tenofovir, didanosine and efavirenz. Antivir Ther
2004; 9: S172.
65 Back D, Gibbons S, Khoo S. Pharmacokinetic drug interactions
with nevirapine. J Acquir Immune Defic Syndr 2003; 34 (suppl 1):
66 Weiser S, Wolfe W, Bangsberg D, et al. Barriers to antiretroviral
adherence for patients living with HIV infection and AIDS in
Botswana. J Acquir Immune Defic Syndr 2003; 34: 281–88.
67 Weidle PJ, Malamba S, Mwebaze R, et al. Assessment of a pilot
antiretroviral drug therapy programme in Uganda: patients’
response, survival, and drug resistance. Lancet 2002; 360: 34–40.
68 Bangsberg DR, Hecht FM, Charlebois ED, et al. Adherence to
protease inhibitors, HIV-1 viral load, and development of drug
resistance in an indigent population. AIDS 2000; 14: 357–66.
69 Paterson DL, Potoski B, Capitano B. Measurement of adherence to
antiretroviral medications. J Acquir Immune Defic Syndr 2002; 31
(suppl 3): S103–06.
70 Paterson DL, Swindells S, Mohr J, et al. Adherence to protease
inhibitor therapy and outcomes in patients with HIV infection.
Ann Intern Med 2000; 133: 21–30.
71 Perno CF, Ceccherini-Silberstein F, De Luca A, et al. Virologic
correlates of adherence to antiretroviral medications and
therapeutic failure. J Acquir Immune Defic Syndr 2002; 31 (suppl 3):
72 Press N, Tyndall MW, Wood E, Hogg RS, Montaner JS. Virologic
and immunologic response, clinical progression, and highly active
antiretroviral therapy adherence. J Acquir Immune Defic Syndr 2002;
31 (suppl 3): S112–17.
73 Walsh JC, Mandalia S, Gazzard BG. Responses to a 1 month self-
report on adherence to antiretroviral therapy are consistent with
electronic data and virological treatment outcome. AIDS 2002; 16:
74 Oyugi JH, Byakika-Tusiime J, Charlebois ED, et al. Multiple
validated measures of adherence indicate high levels of adherence
to generic HIV antiretroviral therapy in a resource-limited setting.
J Acquir Immune Defic Syndr 2004; 36: 1100–02.
75 Deeks SG, Hoh R, Neilands TB, et al. Interruption of treatment
with individual therapeutic drug classes in adults with multidrug-
resistant HIV-1 Infection. J Infect Dis 2005; 192: 1537–44.
76 Koenig SP, Leandre F, Farmer PE. Scaling-up HIV treatment
programmes in resource-limited settings: the rural Haiti
experience. AIDS 2004; 18 (suppl 3): S21–25.
http://infection.thelancet.com Vol 6 January 200659