Fox, Matthew P DSc, MPH*†; Ive, Prudence MBBCH‡; Long, Lawrence MCom†; Maskew, Mhairi MBBCH, MSc‡§; Sanne, Ian MBBCH‡§
In 2004, the South African government began its large-scale Comprehensive Care Management and Treatment plan for provision of antiretroviral therapy (ART).1 Over the next 3 years, South Africa saw the number of patients on ART rise to nearly 300,000 by 2007.2 Although the vast majority of patients remain on first-line therapy and achieve positive clinical, immunologic, and virologic outcomes,3-7 the absolute number of patients failing first-line therapy is likely to increase along with overall patient numbers and duration on ART. In addition, as women who receive single-dose nevirapine for the prevention of mother to child transmission seem to be more likely to develop antiretroviral-associated resistance mutations and subsequently fail first-line ART regimens that include nevirapine (NVP),8,9 the use of single-dose nevirapine may lead to an increased rate of first-line treatment failure.
Second-line regimens are far more expensive than standard first-line therapy, and concerns over the development of resistance to second-line regimens in developing countries have been raised, particularly in the absence of viral load monitoring.10,11 It is, therefore, critical to assess the effectiveness of second-line regimens in resource-limited settings, where treatment programs are still relatively new and viral load testing to confirm suppression is rare.12-14 A search of the literature identified only 1 published analysis of the outcomes of second-line therapy in developing countries. The Médecins Sans Frontières (MSF) cohort experienced high rates of survival and successful immunologic outcomes in 370 patients from different developing countries.15 Although very encouraging, this program had limited data on viral load to be able to assess virologic suppression.
South Africa, where treatment guidelines for public-sector treatment1 do allow for switching patients to second-line regimens after virologic confirmation of failure of first-line regimens, is likely to see such a rise in the near future. We used data from a large urban HIV clinic in South Africa which conducts routine viral load tests after initiation of second-line therapy to assess survival, immunological, and virologic outcomes over the first year of follow-up in a cohort of patients switched to standard public-sector second-line therapy.
The study was conducted using data collected at the Themba Lethu Clinic (TLC) in Johannesburg, South Africa. TLC is a large public-sector ART clinic located at an urban referral hospital. The clinic also receives support from a President's Emergency Plan for AIDS Relief-supported South African nongovernmental organization (NGO) called Right to Care (RTC). All care at TLC has been provided completely free of charge to patients since October 2006.
TLC is one of the largest HIV clinics in South Africa with nearly 11,000 patients initiated on ART, 3000 patients receiving pre-ART HIV wellness care and between 200 and 500 consultations per day. Treatment is conducted in accordance with the South African National Department of Health guidelines1 for the provision of ART. These include stavudine, lamivudine, and efavirenz (d4T/3TC/EFV) as the standard first-line therapy with zidovudine (AZT) and NVP as possible alternatives to d4T and EFV, respectively. Patients are initiated on ART with a CD4 count ≤200 or a World Health Organization Stage IV condition. Standard public-sector second-line therapy is AZT, didanosine, and lopinavir/ritonavir (AZT/ddI/LPVr). Treatment switching is undertaken based on toxicity and 2 consecutive viral loads >1000 copies/mL.
This analysis included all patients initiated on standard public-sector second-line therapy at TLC between April 2004 and June 2008 who were ≥18 years old and who had been previously initiated on a standard triple drug therapy for first-line treatment. We excluded patients who initiated second-line therapy after November 2007 so that all subjects had the potential to complete a minimum of 1 year of follow-up. Our study population did not include all patients who failed, as some who failed were never switched to second-line or were switched to a nonstandard second-line regimen.
We conducted a cohort analysis of data collected prospectively as part of routine HIV care. All patient data at TLC is collected in a standardized way using an electronic patient management system. Demographic data are captured at initiation of first-line ART and all visit information is collected in real-time in the clinic.
Patients are scheduled for medical visits every 1-6 months, though most patients return to the clinic every 1-2 months to collect antiretrovirals (ARVs). Visit scheduling is tracked electronically and allows for tracing of patients who have missed visits and for categorization of patients as lost to follow-up (LTF) (defined as is having missed a scheduled ARV pick up for >3 months). At each medical visit, patients are seen by a nurse, a doctor, and when appropriate, a counselor. Medical visit information is collected on tuberculosis symptom screen, weight, other vital signs and any new clinical conditions diagnosed including new opportunistic infections.
Treatment monitoring is done with CD4 counts and viral loads at 4 months after initiation of a new regimen and then approximately 6 monthly thereafter unless clinically indicated. CD4+ T-cell lymphocytes counts are done using pan-leucogated CD4+ flow cytometry (FlowCount Fluorospheres, Beckman Coulter-Immunotech, France), whereas HIV-1 RNA viral load tests are conducted using NucliSENS EasyQ HIV-1 assay (bioMérieux Clinical Diagnostics, France).
Analysis of this data was approved by the Institutional Review Board of Boston University and the Human Research Ethics Committee of the University of the Witwatersrand.
To determine if outcomes on second-line therapy differed from outcomes on first-line therapy, we time-matched each index patient who switched to second-line to 4 comparison subjects who did not begin second-line therapy. For each index patient we calculated their duration on first-line therapy. We then randomly sampled (without replacement) up to 4 comparison patients from all patients who did not go on to second-line therapy but who had been on first-line at least as long as the index patient. Four matches were found for all but 1 second-line patient. For comparison subjects person time began accruing after the amount of time the index subject had been on first-line. For example if an index patient initiated second-line treatment after 365 days on first-line treatment, we sampled 4 subjects who did not switch to second-line drugs from the list of all patients on first-line for ≥365 days. Each of the 5 subjects (the second-line index patient and the 4 matched comparison subjects) began accumulating person time from day 365 on any ART through a maximum of 730 days.
Definition of Analytic Variables
We examined 3 measures of treatment success by 1 year after initiating second-line ART: (1) alive and in care (AIC), defined as not known to have died and not LTF (missed a scheduled ARV pick up for longer than 3 months); (2) achieving an undetectable viral load (UDVL) (<400 copies/mL); and (3) increases in CD4 count. For UDVL we only included patients with ≥1 viral load measure within 12 months after initiating second-line treatment. Patients transferred out of care were excluded from the AIC analysis unless they completed 1 year of follow-up before transfer and from the UDVL analysis unless they had a viral load prior to transfer.
Follow-up time for each subject began on the date of initiating second-line therapy for index subjects or the equivalent duration on first-line for time-matched comparisons. For AIC, follow-up time ended at the date of the earliest of (1) completion of 1 year of follow-up; (2) closing the data set; and (3) death or LTF. For UDVL, follow-up time ended at the date of the earliest of (1) last viral load up to 1 year on second-line; (2) first UDVL on second-line.
We summarized treatment outcomes as simple proportions achieving each outcome and corresponding 95% CIs. Changes in CD4 count after initiating second-line therapy were summarized as mean changes over time and 95% CIs. We looked for predictors of UDVL by 1 year among those on second-line drugs. We calculated crude Kaplan-Meier curves of viral suppression stratified by predictors of interest and calculated crude hazard ratios (HRs) using Cox proportional hazards regression. We fit adjusted multivariable models adjusted for age and sex and other important predictors. To look for predictors of second-line treatment failure, multivariable models included any variable with a Wald P < 0.3. Potential predictors were those that could plausibly be related to either first or second-line failure and included variables related to initiation of any ARV [eg, regimen, CD4 count, body mass index (BMI), calendar year, history of tuberculosis (TB)], variables occurring during first-line (incident TB, development of toxicities, compliance, months on first-line, number of detectable viral loads before switch, time from failure to switching, etc.) and variables relating to the time of initiation of second-line (CD4 count, BMI, CD4 nadir, etc.).
Finally, we calculated HRs comparing subjects on second-line therapy to the time-matched comparison group to determine whether outcomes on second-line therapy differed from those on first-line therapy for patients on ART for an equal duration.
Table 1 compares the 328 index patients initiated on second-line therapy with 2 other groups: (1) all the 9694 patients initiated on first-line ART at TLC not switched to second-line therapy; and (2) the 1311 time-matched comparison patients (described under Methods).
Among the entire cohort of patients who ever initiated first line, 21.4% were LTF, 9.6% had died and 5.4% had transferred out of care over their entire duration of follow-up. Patients initiated on second-line had been on ART a median of 1.3 years [interquartile range (IQR) 0.8-1.9] at the time of treatment switch. Most patients were initiated on d4T-3TC-EFV (79%) as first-line therapy. Compared with those not switched to second-line or the time-matched comparisons, patients switched were more immunosuppressed at initiation of first-line therapy as they were more likely to have had a CD4 count ≤50 than either comparison group (43% vs. 35% and 32%, respectively) and more likely to have a history of TB (19% vs. 9% and 11%, respectively). Although underreporting was common 40 (12%) of second-line patients were noted to have had noncompliance with their first regimen by a clinician. For the 287 patients who had 2 detectable viral loads >1000 before switch, the median time from the first detectable viral load to switch was 84 days (IQR 32-183). The median viral load at the second detectable viral load was 13,000. For the 41 patients who did not have records of 2 detectable viral loads >1000 in their patient file, more than half (N = 22) had confirmed virologic failure listed as the reason for treatment switch listed in the data set. The subjects would have had confirmed virologic failure using viral load testing, but the actual lab values would not have been captured in our data set.
Outcomes on Second-line Therapy
Alive and In Care
Perhaps the most general measure of the success of treatment is being AIC. After excluding 15 patients who were transferred out of care to another treatment facility, 78% (95% CI: 73%-82%) of patients switched to second-line treatment (243/313) were AIC at the end of 1 year on second-line therapy. Of the 70 patients who experienced a negative outcome, 17 died (24%) in a median of 4 months (IQR 0.9-7.7). The remaining 53 were LTF (76%) in a median of 5.6 months (IQR 3.7-9.0).
For treatment to show long-term effectiveness patients must achieve and maintain viral suppression. After excluding 5 transferred patients, we had viral load measures for 262 (81%) patients. Of the 61 with missing viral load measures, 20% died (N = 12), whereas 52% were LTF (N = 32). We detected no differences in CD4 count, BMI, hemoglobin, and age at initiation of first-line ART between those who did and did not have a viral load outcome. Of the remaining 262 with a viral load measure, 203 (77%; 95% CI: 72%-82%) had achieved viral suppression by 1 year on second-line treatment. Median time to viral suppression was 118 days (IQR 96-150). Of the 59 patients who did not achieve viral suppression, median viral load was 7000 (range 450-130,000). Of these 59, 30 (51%) were still AIC at the end of 1 year of follow-up. Of the remaining 29, 8 (14%) died, whereas 19 (32%) were LTF and 2 transferred out of care (3%). Of the 202 subjects who reached virally suppression by 1 year, 18 (9%; 95% CI: 6%-13%) had viral rebound within 1 year of initiating second-line with a median viral load or 1700 (range 420-62,000).
Figure 1 shows population mean increases in CD4 counts from initiation of second-line therapy. At initiation of second-line therapy, 249 patients had a CD4 count up to 4 months before the switch. Among those the mean CD4 count was 203 cells/μL (95% CI: 187-219). This increased to 266 cells/μL by 6 months (95% CI: 241-292) and to 318 cells/μL (95% CI: 288-348) by 1 year on second-line treatment. The mean increase from initiation of second-line when limited to the 110 patients with both an initiation of second-line and 6-month CD4 count was 59 cells/μL (95% CI: 37-80). Among the 102 with both an initiation of second-line and 12-month CD4 count the mean increase was 133 cells/μL (95% CI: 106-160). The increase was similar by 1 year when limited to those who achieved viral suppression (129; 95% CI: 93-165) with little increase among those not virally suppressed (44; 95% CI: −29 to 118).
Comparison With Those on First-Line for Equal Duration
To determine if 1 year outcomes on second-line therapy differed from outcomes among patients on first-line for an equivalent duration, we compared the proportion alive in care by 1 year of time on second-line to matched controls who did not switch to second-line (Table 2). A smaller proportion of patients on second-line therapy were AIC after 1 year on treatment compared with those time-matched comparisons still on first-line (77% vs. 91%). After adjusting for age, sex, race, first-line regimen, and year of initiating any ART, patients on second-line therapy were somewhat less likely to be AIC by 1 year (HR 0.84; 95% CI: 0.73-0.97) than those still on first-line for equal duration.
Predictors of Virologic Success Among Those on Second-Line
Table 3 summarizes crude and adjusted predictors of virologic suppression. In adjusted analyses, the 2 largest predictors of achieving viral suppression was being switched to second-line for reasons other than noncompliance (HR 1.83; 95% CI: 1.14-2.93) and being switched before having 2 consecutive detectable viral loads before switching to second-line (HR 1.68; 95% CI: 1.08-2.61). We also found that not having a history of tuberculosis (HR 1.39; 95% CI: 0.95-2.04), having a BMI ≥ 17.5 at initiation of any ART (HR 1.67; 95% CI: 0.97-2.88) and having a CD4 count >200 at initiation of any ART vs. <100 (HR 1.96; 95% CI: 1.21-3.17) were predictive of achieving viral suppression. Of note, we did not detect a relationship between CD4 count at initiation of second-line therapy or CD4 nadir to be predictive of achieving viral suppression.
As the scale up of ART in developing countries continues, the number of patients switching to second-line therapy will inevitably increase. Decisions about the use of second-line regimens in these areas will depend, in part, on the success of these regimens, but to date there has been little evidence to assess the effectiveness of these regimens in patients failing first-line therapies.14 We have demonstrated in a large urban cohort in South Africa that outcomes on second-line therapy, whether measured in terms of remaining AIC or by virologic suppression showed between 77% and 78% success rates by 1 year after initiating second-line treatment. In comparison, a pooled analysis of mortality on first-line regimens in low-income countries showed 1-year survival rates of roughly 94% when actively tracing patients.16
Our results are very similar to the recent findings by Pujades-Rodriguez15 using the MSF cohort encompassing countries throughout the developing world. Our data are not strictly comparable as their data included several second-line regimens and the majority of those patients had d4T-3TC-NVP as their first-line regimen. In their cohort of 370 patients on second-line therapy, 86% (95% CI: 81%-90%) were AIC by 1 year, similar, though somewhat higher than our finding (78%; 95% CI: 73%-82%). Our ability to show viral load suppression in 77% (95% CI: 72%-82%) of patients demonstrates that patients are not only surviving but are achieving maximum viral suppression. Equally important, we found that although outcomes among patients on second-line therapy were not as positive as patients who were still on first-line therapy for equal total duration on any ART, differences were small. Taken together these findings suggest that patients who are switched to second-line treatment have a positive prognosis, at least through 1 year after initiation.
We have also demonstrated a substantial mean increase in CD4 count in patients on second-line therapy. Patients on second-line therapy increased an average of 133 cells/μL over the first year on treatment. This is again similar to the MSF cohort, where patients had a median CD4 count increase at 12 months of 135 cells/μL.15 Although not universally accepted, an increase of 100 cell/μL over the first year on therapy can be seen as a marker of treatment success on first-line therapy when most patients are initiated at CD4 counts below 200. In comparison, the average gain of 133 cells in 1 year on second-line seen in our study represents substantial immune recovery.
As more patients are initiated on second-line regimens, being able to identify which patients fail these regimens and why will be critical to the long-term durability of these regimens. We found that patients who were switched off their first-line regimen for reasons other than noncompliance were more likely to achieve viral suppression when switched to second-line. We did not, however, have data on adherence to second-line therapy, potentially the most important predictor of achieving viral suppression. Overall adherence was likely high as treatment success rates were high, but over time adherence rates may decline leading to more failures. However, our data suggest that patients for whom poor adherence to first-line treatment was problematic will need additional counseling and support to benefit from second-line regimens.
We also found that those who patients who were switched to second-line without a second detectable viral load >1000 were more likely to achieve viral suppression. In many cases the switch occurred in patients who had been on standard first-line before coming to the clinic but who were still not virally suppressed, whereas in other cases the reason for switch was both virologic failure along with toxicity. For these patients, changing therapy faster seemed to be associated with better rates of viral suppression, however, this needs to be confirmed in other cohorts as we did not have reason for treatment switch for all patients.
In resource-limited settings where ART failure is determined based predominantly on clinical and possibly on immunological failure, substantial nucleoside reverse transcriptase inhibitor resistance has been shown to occur.17 In this cohort, where HIV-1 RNA levels were routinely monitored, switching was likely done earlier than it would have been had only clinical assessment been used. This would be expected to limit the amount of genotypic resistance acquired and ensure the better outcomes on second-line regimen as patients would have fewer thymidine analog mutations (TAMS) or other nucleoside reverse transcriptase inhibitor mutations. This is particularly important in South Africa, where second-line therapy contains AZT and therefore relies on minimal TAMS having been accumulated to be successful.
Our findings should be considered in light of their limitations. In particular, each of our outcome measures was subject to some misclassification. We have used AIC as a proxy for vital status. The realities of working in a large urban treatment facility are that some patients will be lost and no outcome can be recorded for them. In many settings being lost means no longer on ART,18 as most patients who discontinue care will likely die within 1 year of stopping treatment.19,20 In 2 studies of patients who dropped out of care at clinics in Johannesburg, 27% and 48% of those who could be traced had died.21,22 It thus seems reasonable to use AIC as a proxy for vital status, particularly as this would be a conservative estimate of overall treatment success.
In addition, our comparison of patients who were on second-line therapy with those on first-line for equivalent times likely suffers from some survivor bias. Patients who are failing first-line but survive long enough to be switched to second-line may be at reduced risk of poor outcomes and death, compared with those who were eligible for second-line but either chose not to initiate, or died or were LTF before receiving it. This would tend to bias mortality rates on second-line towards those on first-line and cause us to underestimate differences between the groups. Still, the lack of any large effect observed suggests that the bias would have to be substantial to lead to a strong increased risk in those on second-line. In addition, as patients who switched were matched to patients on ART for an equivalent amount of time should reduce such bias as those subjects also had to survive long enough to be eligible to be matches for those switched.
The South African Comprehensive Care Management and Treatment program for ART includes the monitoring of treatment using CD4+ count and viral load. Whereas the national treatment program advocates treatment switching for virologic failure as defined by 2 consecutive viral loads >5000 copies/mL, the TLC switches treatment based on 2 detectable viral loads >1000 copies/mL. The results presented here may therefore reflect a more stringent monitoring policy.
In conclusion, we found that patients who were initiated on second-line therapy in a large urban HIV clinic in Johannesburg, South Africa, had high rates of immunologic and virologic success and low rates of mortality over the first year on second-line treatment. Further research is needed to determine if these findings can be extended to different settings and longer follow-up will be needed to determine if these early outcomes can be sustained over the following years of treatment.
M.F. conducted the primary analyses for this publication in conjunction with M.M. and L.L. and wrote the first draft of the manuscript. P.I. contributed to the conception of the study, designing additional analyses to be performed, interpreting the results and editing the final manuscript. M.M. contributed to the conception of the study, collection, and cleaning of the data, designing additional analyses to be performed and editing the final manuscript. L.L. contributed to designing of additional analyses to be performed, interpreting the results and editing the final manuscript. I.S. contributed to the conception of the study, designing additional analyses to be performed, interpreting the results and editing the final manuscript. The opinions expressed herein are those of the authors and do not necessarily reflect the views of the colleagues mentioned below. We express our gratitude to the directors and staff of the TLC who helped to collect and interpret the data and to RTC, the NGO supporting the study sites through a partnership with United States Agency for International Development. We also thank the Gauteng and National Department of Health for providing for the care of the patients at the TLC as part of the Comprehensive Care Management and Treatment plan. Most of all we thank the patients attending the clinic for their continued trust in the treatment provided at the clinic. We are also extremely grateful to Professor Patrick MacPhail and Babatyi Malope-Kgokong for their work in establishing the Themba Lethu Clinical Cohort data set. The authors also thank Sydney Rosen for her comments on earlier drafts of the manuscript.
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