Journal Article > ResearchFull Text
Lancet Global Health. 1 June 2023; Volume 11 (Issue 6); e903-e916.; DOI:10.1016/S2214-109X(23)00135-3
Broger T, Koeppel L, Huerga H, Miller P, Gupta-Wright A, et al.
Lancet Global Health. 1 June 2023; Volume 11 (Issue 6); e903-e916.; DOI:10.1016/S2214-109X(23)00135-3
BACKGROUND
Sputum is the most widely used sample to diagnose active tuberculosis, but many people living with HIV are unable to produce sputum. Urine, in contrast, is readily available. We hypothesised that sample availability influences the diagnostic yield of various tuberculosis tests.
METHODS
In this systematic review and meta-analysis of individual participant data, we compared the diagnostic yield of point-of-care urine-based lipoarabinomannan tests with that of sputum-based nucleic acid amplification tests (NAATs) and sputum smear microscopy (SSM). We used microbiologically confirmed tuberculosis based on positive culture or NAAT from any body site as the denominator and accounted for sample provision. We searched PubMed, Web of Science, Embase, African Journals Online, and clinicaltrials.gov from database inception to Feb 24, 2022 for randomised controlled trials, cross-sectional studies, and cohort studies that assessed urine lipoarabinomannan point-of-care tests and sputum NAATs for active tuberculosis detection in participants irrespective of tuberculosis symptoms, HIV status, CD4 cell count, or study setting. We excluded studies in which recruitment was not consecutive, systematic, or random; provision of sputum or urine was an inclusion criterion; less than 30 participants were diagnosed with tuberculosis; early research assays without clearly defined cutoffs were tested; and humans were not studied. We extracted study-level data, and authors of eligible studies were invited to contribute deidentified individual participant data. The main outcomes were the tuberculosis diagnostic yields of urine lipoarabinomannan tests, sputum NAATs, and SSM. Diagnostic yields were predicted using Bayesian random-effects and mixed-effects meta-analyses. This study is registered with PROSPERO, CRD42021230337.
FINDINGS
We identified 844 records, from which 20 datasets and 10 202 participants (4561 [45%] male participants and 5641 [55%] female participants) were included in the meta-analysis. All studies assessed sputum Xpert (MTB/RIF or Ultra, Cepheid, Sunnyvale, CA, USA) and urine Alere Determine TB LAM (AlereLAM, Abbott, Chicago, IL, USA) in people living with HIV aged 15 years or older. Nearly all (9957 [98%] of 10 202) participants provided urine, and 82% (8360 of 10 202) provided sputum within 2 days. In studies that enrolled unselected inpatients irrespective of tuberculosis symptoms, only 54% (1084 of 1993) of participants provided sputum, whereas 99% (1966 of 1993) provided urine. Diagnostic yield was 41% (95% credible interval [CrI] 15-66) for AlereLAM, 61% (95% Crl 25-88) for Xpert, and 32% (95% Crl 10-55) for SSM. Heterogeneity existed across studies in the diagnostic yield, influenced by CD4 cell count, tuberculosis symptoms, and clinical setting. In predefined subgroup analyses, all tests had higher yields in symptomatic participants, and AlereLAM yield was higher in those with low CD4 counts and inpatients. AlereLAM and Xpert yields were similar among inpatients in studies enrolling unselected participants who were not assessed for tuberculosis symptoms (51% vs 47%). AlereLAM and Xpert together had a yield of 71% in unselected inpatients, supporting the implementation of combined testing strategies.
INTERPRETATION
AlereLAM, with its rapid turnaround time and simplicity, should be prioritised to inform tuberculosis therapy among inpatients who are HIV-positive, regardless of symptoms or CD4 cell count. The yield of sputum-based tuberculosis tests is undermined by people living with HIV who cannot produce sputum, whereas nearly all participants are able to provide urine. The strengths of this meta-analysis are its large size, the carefully harmonised denominator, and the use of Bayesian random-effects and mixed-effects models to predict yields; however, data were geographically restricted, clinically diagnosed tuberculosis was not considered in the denominator, and little information exists on strategies for obtaining sputum samples.
FUNDING
FIND, the Global Alliance for Diagnostics.
Sputum is the most widely used sample to diagnose active tuberculosis, but many people living with HIV are unable to produce sputum. Urine, in contrast, is readily available. We hypothesised that sample availability influences the diagnostic yield of various tuberculosis tests.
METHODS
In this systematic review and meta-analysis of individual participant data, we compared the diagnostic yield of point-of-care urine-based lipoarabinomannan tests with that of sputum-based nucleic acid amplification tests (NAATs) and sputum smear microscopy (SSM). We used microbiologically confirmed tuberculosis based on positive culture or NAAT from any body site as the denominator and accounted for sample provision. We searched PubMed, Web of Science, Embase, African Journals Online, and clinicaltrials.gov from database inception to Feb 24, 2022 for randomised controlled trials, cross-sectional studies, and cohort studies that assessed urine lipoarabinomannan point-of-care tests and sputum NAATs for active tuberculosis detection in participants irrespective of tuberculosis symptoms, HIV status, CD4 cell count, or study setting. We excluded studies in which recruitment was not consecutive, systematic, or random; provision of sputum or urine was an inclusion criterion; less than 30 participants were diagnosed with tuberculosis; early research assays without clearly defined cutoffs were tested; and humans were not studied. We extracted study-level data, and authors of eligible studies were invited to contribute deidentified individual participant data. The main outcomes were the tuberculosis diagnostic yields of urine lipoarabinomannan tests, sputum NAATs, and SSM. Diagnostic yields were predicted using Bayesian random-effects and mixed-effects meta-analyses. This study is registered with PROSPERO, CRD42021230337.
FINDINGS
We identified 844 records, from which 20 datasets and 10 202 participants (4561 [45%] male participants and 5641 [55%] female participants) were included in the meta-analysis. All studies assessed sputum Xpert (MTB/RIF or Ultra, Cepheid, Sunnyvale, CA, USA) and urine Alere Determine TB LAM (AlereLAM, Abbott, Chicago, IL, USA) in people living with HIV aged 15 years or older. Nearly all (9957 [98%] of 10 202) participants provided urine, and 82% (8360 of 10 202) provided sputum within 2 days. In studies that enrolled unselected inpatients irrespective of tuberculosis symptoms, only 54% (1084 of 1993) of participants provided sputum, whereas 99% (1966 of 1993) provided urine. Diagnostic yield was 41% (95% credible interval [CrI] 15-66) for AlereLAM, 61% (95% Crl 25-88) for Xpert, and 32% (95% Crl 10-55) for SSM. Heterogeneity existed across studies in the diagnostic yield, influenced by CD4 cell count, tuberculosis symptoms, and clinical setting. In predefined subgroup analyses, all tests had higher yields in symptomatic participants, and AlereLAM yield was higher in those with low CD4 counts and inpatients. AlereLAM and Xpert yields were similar among inpatients in studies enrolling unselected participants who were not assessed for tuberculosis symptoms (51% vs 47%). AlereLAM and Xpert together had a yield of 71% in unselected inpatients, supporting the implementation of combined testing strategies.
INTERPRETATION
AlereLAM, with its rapid turnaround time and simplicity, should be prioritised to inform tuberculosis therapy among inpatients who are HIV-positive, regardless of symptoms or CD4 cell count. The yield of sputum-based tuberculosis tests is undermined by people living with HIV who cannot produce sputum, whereas nearly all participants are able to provide urine. The strengths of this meta-analysis are its large size, the carefully harmonised denominator, and the use of Bayesian random-effects and mixed-effects models to predict yields; however, data were geographically restricted, clinically diagnosed tuberculosis was not considered in the denominator, and little information exists on strategies for obtaining sputum samples.
FUNDING
FIND, the Global Alliance for Diagnostics.
Journal Article > ResearchAbstract Only
J. Infect.. 16 May 2022; Volume S0163-4453 (Issue 22); 00292-4.; DOI:10.1016/j.jinf.2022.05.010
Dhana A, Hamada Y, Kengne AP, Kerkhoff AD, Broger T, et al.
J. Infect.. 16 May 2022; Volume S0163-4453 (Issue 22); 00292-4.; DOI:10.1016/j.jinf.2022.05.010
BACKGROUND
WHO recommends urine lateral-flow lipoarabinomannan (LF-LAM) testing with AlereLAM in HIV-positive inpatients only if screening criteria are met. We assessed the performance of WHO screening criteria and alternative screening tests/strategies to guide LF-LAM testing and compared diagnostic accuracy of the WHO AlereLAM algorithm (WHO screening criteria → AlereLAM) with AlereLAM and FujiLAM (a novel LF-LAM test).
METHODS
We searched MEDLINE, Embase, and Cochrane Library from Jan 1, 2011 to March 1, 2020 for studies among adult/adolescent HIV-positive inpatients regardless of tuberculosis signs and symptoms. The reference standards were 1) AlereLAM or FujiLAM for screening tests/strategies and 2) culture or Xpert for AlereLAM/FujiLAM. We determined proportion of inpatients eligible for AlereLAM using WHO screening criteria; assessed accuracy of WHO criteria and alternative screening tests/strategies to guide LF-LAM testing; compared accuracy of WHO AlereLAM algorithm with AlereLAM/FujiLAM in all; and determined diagnostic yield of AlereLAM, FujiLAM, and Xpert MTB/RIF (Xpert). We estimated pooled proportions with a random-effects model, assessed diagnostic accuracy using random-effects bivariate models, and assessed diagnostic yield descriptively.
FINDINGS
We obtained data from all 5 identified studies (n=3,504). The pooled proportion of inpatients eligible for AlereLAM using WHO criteria was 93% (95%CI 91, 95). Among screening tests/strategies to guide LF-LAM testing, WHO criteria, C-reactive protein (≥5 mg/L), and CD4 count (<200 cells/μL) had high sensitivities but low specificities; cough (≥2 weeks), haemoglobin (<8 g/dL), body mass index (<18.5 kg/m2), lymphadenopathy, and WHO-defined danger signs had higher specificities but suboptimal sensitivities. AlereLAM in all had the same sensitivity (62%) and specificity (88%) as WHO AlereLAM algorithm. Sensitivity of FujiLAM and AlereLAM was 69% and 48%, while specificity was 48% and 96%, respectively. Diagnostic yield of sputum Xpert was 29-41%, AlereLAM was 39-76%, and urine Xpert was 35-62%. In one study, FujiLAM diagnosed 80% of tuberculosis cases (vs 39% for AlereLAM), and sputum Xpert combined with AlereLAM, urine Xpert, or FujiLAM diagnosed 69%, 81%, and 92% of all cases, respectively.
INTERPRETATION
WHO criteria and alternative screening tests/strategies have limited utility in guiding LF-LAM testing, suggesting that AlereLAM testing in all HIV-positive medical inpatients be implemented. Routine FujiLAM may improve tuberculosis diagnosis.
WHO recommends urine lateral-flow lipoarabinomannan (LF-LAM) testing with AlereLAM in HIV-positive inpatients only if screening criteria are met. We assessed the performance of WHO screening criteria and alternative screening tests/strategies to guide LF-LAM testing and compared diagnostic accuracy of the WHO AlereLAM algorithm (WHO screening criteria → AlereLAM) with AlereLAM and FujiLAM (a novel LF-LAM test).
METHODS
We searched MEDLINE, Embase, and Cochrane Library from Jan 1, 2011 to March 1, 2020 for studies among adult/adolescent HIV-positive inpatients regardless of tuberculosis signs and symptoms. The reference standards were 1) AlereLAM or FujiLAM for screening tests/strategies and 2) culture or Xpert for AlereLAM/FujiLAM. We determined proportion of inpatients eligible for AlereLAM using WHO screening criteria; assessed accuracy of WHO criteria and alternative screening tests/strategies to guide LF-LAM testing; compared accuracy of WHO AlereLAM algorithm with AlereLAM/FujiLAM in all; and determined diagnostic yield of AlereLAM, FujiLAM, and Xpert MTB/RIF (Xpert). We estimated pooled proportions with a random-effects model, assessed diagnostic accuracy using random-effects bivariate models, and assessed diagnostic yield descriptively.
FINDINGS
We obtained data from all 5 identified studies (n=3,504). The pooled proportion of inpatients eligible for AlereLAM using WHO criteria was 93% (95%CI 91, 95). Among screening tests/strategies to guide LF-LAM testing, WHO criteria, C-reactive protein (≥5 mg/L), and CD4 count (<200 cells/μL) had high sensitivities but low specificities; cough (≥2 weeks), haemoglobin (<8 g/dL), body mass index (<18.5 kg/m2), lymphadenopathy, and WHO-defined danger signs had higher specificities but suboptimal sensitivities. AlereLAM in all had the same sensitivity (62%) and specificity (88%) as WHO AlereLAM algorithm. Sensitivity of FujiLAM and AlereLAM was 69% and 48%, while specificity was 48% and 96%, respectively. Diagnostic yield of sputum Xpert was 29-41%, AlereLAM was 39-76%, and urine Xpert was 35-62%. In one study, FujiLAM diagnosed 80% of tuberculosis cases (vs 39% for AlereLAM), and sputum Xpert combined with AlereLAM, urine Xpert, or FujiLAM diagnosed 69%, 81%, and 92% of all cases, respectively.
INTERPRETATION
WHO criteria and alternative screening tests/strategies have limited utility in guiding LF-LAM testing, suggesting that AlereLAM testing in all HIV-positive medical inpatients be implemented. Routine FujiLAM may improve tuberculosis diagnosis.
Journal Article > ResearchFull Text
S Afr Med J. 1 September 2009
Cornell M, Technau KG, Fairall L, Wood R, Moultrie H, et al.
S Afr Med J. 1 September 2009
OBJECTIVES: To introduce the combined South African cohorts of the International epidemiologic Databases to Evaluate AIDS Southern Africa (IeDEA-SA) collaboration as reflecting the South African national antiretroviral treatment (ART) programme; to characterise patients accessing these services; and to describe changes in services and patients from 2003 to 2007. DESIGN AND SETTING: Multi-cohort study of 11 ART programmes in Gauteng, Western Cape, Free State and KwaZulu-Natal. SUBJECTS: Adults and children (<16 years old) who initiated ART with > or =3 antiretroviral drugs before 2008. RESULTS: Most sites were offering free treatment to adults and children in the public sector, ranging from 264 to 17,835 patients per site. Among 45,383 adults and 6,198 children combined, median age (interquartile range) was 35.0 years (29.8-41.4) and 42.5 months (14.7-82.5), respectively. Of adults, 68% were female. The median CD4 cell count was 102 cells/microl (44-164) and was lower among males than females (86, 34-150 v. 110, 50-169, p<0.001). Median CD4% among children was 12% (7-17.7). Between 2003 and 2007, enrolment increased 11-fold in adults and 3-fold in children. Median CD4 count at enrolment increased for all adults (67-111 cells/microl, p<0.001) and for those in stage IV (39-89 cells/microl, p<0.001). Among children <5 years, baseline CD4% increased over time (11.5-16.0%, p<0.001). CONCLUSIONS: IeDEA-SA provides a unique opportunity to report on the national ART programme. The study describes dramatically increased enrolment over time. Late diagnosis and ART initiation, especially of men and children, need attention. Investment in sentinel sites will ensure good individual-level data while freeing most sites to continue with simplified reporting.
Journal Article > ResearchFull Text
AIDS. 10 September 2010; Volume 24 (Issue 14); DOI:10.1097/QAD.0b013e32833d45c5
Cornell M, Grimsrud A, Fairall L, Fox MP, van Cutsem G, et al.
AIDS. 10 September 2010; Volume 24 (Issue 14); DOI:10.1097/QAD.0b013e32833d45c5
OBJECTIVE: Little is known about the temporal impact of the rapid scale-up of large antiretroviral therapy (ART) services on programme outcomes. We describe patient outcomes [mortality, loss-to-follow-up (LTFU) and retention] over time in a network of South African ART cohorts. DESIGN: Cohort analysis utilizing routinely collected patient data. METHODS: Analysis included adults initiating ART in eight public sector programmes across South Africa, 2002-2007. Follow-up was censored at the end of 2008. Kaplan-Meier methods were used to estimate time to outcomes, and proportional hazards models to examine independent predictors of outcomes. RESULTS: Enrolment (n = 44 177, mean age 35 years; 68% women) increased 12-fold over 5 years, with 63% of patients enrolled in the past 2 years. Twelve-month mortality decreased from 9% to 6% over 5 years. Twelve-month LTFU increased annually from 1% (2002/2003) to 13% (2006). Cumulative LTFU increased with follow-up from 14% at 12 months to 29% at 36 months. With each additional year on ART, failure to retain participants was increasingly attributable to LTFU compared with recorded mortality. At 12 and 36 months, respectively, 80 and 64% of patients were retained. CONCLUSION: Numbers on ART have increased rapidly in South Africa, but the programme has experienced deteriorating patient retention over time, particularly due to apparent LTFU. This may represent true loss to care, but may also reflect administrative error and lack of capacity to monitor movements in and out of care. New strategies are needed for South Africa and other low-income and middle-income countries to improve monitoring of outcomes and maximize retention in care with increasing programme size.
Journal Article > ResearchFull Text
PLOS One. 28 February 2013; Volume 8 (Issue 2); e57611.; DOI:10.1371/journal.pone.0057611
Estill J, Egger M, Johnson LF, Gsponer T, Wandeler G, et al.
PLOS One. 28 February 2013; Volume 8 (Issue 2); e57611.; DOI:10.1371/journal.pone.0057611
OBJECTIVES
Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference.
DESIGN
Mathematical modelling study based on data from ART programmes.
METHODS
We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained.
RESULTS
RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality.
CONCLUSIONS
VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.
Mortality in patients starting antiretroviral therapy (ART) is higher in Malawi and Zambia than in South Africa. We examined whether different monitoring of ART (viral load [VL] in South Africa and CD4 count in Malawi and Zambia) could explain this mortality difference.
DESIGN
Mathematical modelling study based on data from ART programmes.
METHODS
We used a stochastic simulation model to study the effect of VL monitoring on mortality over 5 years. In baseline scenario A all parameters were identical between strategies except for more timely and complete detection of treatment failure with VL monitoring. Additional scenarios introduced delays in switching to second-line ART (scenario B) or higher virologic failure rates (due to worse adherence) when monitoring was based on CD4 counts only (scenario C). Results are presented as relative risks (RR) with 95% prediction intervals and percent of observed mortality difference explained.
RESULTS
RRs comparing VL with CD4 cell count monitoring were 0.94 (0.74-1.03) in scenario A, 0.94 (0.77-1.02) with delayed switching (scenario B) and 0.80 (0.44-1.07) when assuming a 3-times higher rate of failure (scenario C). The observed mortality at 3 years was 10.9% in Malawi and Zambia and 8.6% in South Africa (absolute difference 2.3%). The percentage of the mortality difference explained by VL monitoring ranged from 4% (scenario A) to 32% (scenarios B and C combined, assuming a 3-times higher failure rate). Eleven percent was explained by non-HIV related mortality.
CONCLUSIONS
VL monitoring reduces mortality moderately when assuming improved adherence and decreased failure rates.
Journal Article > ResearchFull Text
J Acquir Immune Defic Syndr. 1 June 2011; Volume 57 (Issue 2); DOI:10.1097/QAI.0b013e3182199ee9
Lawn SD, Campbell L, Kaplan R, Boulle AM, Cornell M, et al.
J Acquir Immune Defic Syndr. 1 June 2011; Volume 57 (Issue 2); DOI:10.1097/QAI.0b013e3182199ee9
We studied the time interval between starting tuberculosis treatment and commencing antiretroviral treatment (ART) in HIV-infected patients (n = 1433; median CD4 count 71 cells per microliter, interquartile range: 32-132) attending 3 South African township ART services between 2002 and 2008. The overall median delay was 2.66 months (interquartile range: 1.58-4.17). In adjusted analyses, delays varied between treatment sites but were shorter for patients with lower CD4 counts and those treated in more recent calendar years. During the most recent period (2007-2008), 4.7%, 19.7%, and 51.1% of patients started ART within 2, 4, and 8 weeks of tuberculosis treatment, respectively. Operational barriers must be tackled to permit further acceleration of ART initiation as recommended by 2010 WHO ART guidelines.
Journal Article > ResearchAbstract
J Acquir Immune Defic Syndr. 22 January 2013; Volume 63 (Issue 1); DOI:10.1097/QAI.0b013e318287c1fe
Hoffman CJ, Schomaker M, Fox MP, Mutevedzi, Giddy J, et al.
J Acquir Immune Defic Syndr. 22 January 2013; Volume 63 (Issue 1); DOI:10.1097/QAI.0b013e318287c1fe
In many resource-limited settings monitoring of combination antiretroviral therapy (cART) is based on the current CD4 count, with limited access to HIV RNA tests or laboratory diagnostics. We examined whether the CD4 count slope over 6 months could provide additional prognostic information.
Journal Article > ResearchFull Text
J Acquir Immune Defic Syndr. 1 August 2010; Volume 54 (Issue 5); DOI:10.1097/QAI.0b013e3181e0c4cf
Fenner L, Brinkhof MW, Keiser O, Weigel R, Cornell M, et al.
J Acquir Immune Defic Syndr. 1 August 2010; Volume 54 (Issue 5); DOI:10.1097/QAI.0b013e3181e0c4cf
BACKGROUND: Many HIV-infected children in Southern Africa have been started on antiretroviral therapy (ART), but loss to follow up (LTFU) can be substantial. We analyzed mortality in children retained in care and in all children starting ART, taking LTFU into account. PATIENTS AND METHODS: Children who started ART before the age of 16 years in 10 ART programs in South Africa, Malawi, Mozambique, and Zimbabwe were included. Risk factors for death in the first year of ART were identified in Weibull models. A meta-analytic approach was used to estimate cumulative mortality at 1 year. RESULTS: Eight thousand two hundred twenty-five children (median age 49 months, median CD4 cell percent 11.6%) were included; 391 (4.8%) died and 523 (7.0%) were LTFU in the first year. Mortality at 1 year was 4.5% [95% confidence interval (CI): 2.8% to 7.4%] in children remaining in care, but 8.7% (5.4% to 12.1%) at the program level, after taking mortality in children and LTFU into account. Factors associated with mortality in children remaining in care included age [adjusted hazard ratio (HR) 0.37; 95% CI: 0.25 to 0.54 comparing > or =120 months with <18 months], CD4 cell percent (HR: 0.56; 95% CI: 0.39 to 0.78 comparing > or =20% with <10%), and clinical stage (HR: 0.12; 95% CI: 0.03 to 0.45 comparing World Health Organization stage I with III/IV). CONCLUSIONS: In children starting ART and remaining in care in Southern Africa mortality at 1 year is <5% but almost twice as high at the program level, when taking LTFU into account. Age, CD4 percentage, and clinical stage are important predictors of mortality at the individual level.
Journal Article > ResearchFull Text
J Int AIDS Soc. 16 March 2017; Volume 20; DOI:10.7448/IAS.20.4.21668
Davies MA, Tsondai PR, Tiffin N, Eley B, Rabie H, et al.
J Int AIDS Soc. 16 March 2017; Volume 20; DOI:10.7448/IAS.20.4.21668
To evaluate long-term outcomes in HIV-infected adolescents, it is important to identify ways of tracking outcomes after transfer to a different health facility. The Department of Health (DoH) in the Western Cape Province (WCP) of South Africa uses a single unique identifier for all patients across the health service platform. We examined adolescent outcomes after transfer by linking data from four International epidemiology Databases to Evaluate AIDS Southern Africa (IeDEA-SA) cohorts in the WCP with DoH data.
Journal Article > ResearchFull Text
AIDS. 10 September 2009; Volume 23 (Issue 14); 1867-74.; DOI:10.1097/QAD.0b013e32832e05b2
Keiser O, Tweya H, Boulle AM, Braitstein P, Schechter M, et al.
AIDS. 10 September 2009; Volume 23 (Issue 14); 1867-74.; DOI:10.1097/QAD.0b013e32832e05b2
BACKGROUND
In high-income countries, viral load is routinely measured to detect failure of antiretroviral therapy (ART) and guide switching to second-line ART. Viral load monitoring is not generally available in resource-limited settings. We examined switching from nonnucleoside reverse transcriptase inhibitor (NNRTI)-based first-line regimens to protease inhibitor-based regimens in Africa, South America and Asia.
DESIGN AND METHODS
Multicohort study of 17 ART programmes. All sites monitored CD4 cell count and had access to second-line ART and 10 sites monitored viral load. We compared times to switching, CD4 cell counts at switching and obtained adjusted hazard ratios for switching (aHRs) with 95% confidence intervals (CIs) from random-effects Weibull models.
RESULTS
A total of 20 113 patients, including 6369 (31.7%) patients from 10 programmes with access to viral load monitoring, were analysed; 576 patients (2.9%) switched. Low CD4 cell counts at ART initiation were associated with switching in all programmes. Median time to switching was 16.3 months [interquartile range (IQR) 10.1-26.6] in programmes with viral load monitoring and 21.8 months (IQR 14.0-21.8) in programmes without viral load monitoring (P < 0.001). Median CD4 cell counts at switching were 161 cells/microl (IQR 77-265) in programmes with viral load monitoring and 102 cells/microl (44-181) in programmes without viral load monitoring (P < 0.001). Switching was more common in programmes with viral load monitoring during months 7-18 after starting ART (aHR 1.38; 95% CI 0.97-1.98), similar during months 19-30 (aHR 0.97; 95% CI 0.58-1.60) and less common during months 31-42 (aHR 0.29; 95% CI 0.11-0.79).
CONCLUSION
In resource-limited settings, switching to second-line regimens tends to occur earlier and at higher CD4 cell counts in ART programmes with viral load monitoring compared with programmes without viral load monitoring.
In high-income countries, viral load is routinely measured to detect failure of antiretroviral therapy (ART) and guide switching to second-line ART. Viral load monitoring is not generally available in resource-limited settings. We examined switching from nonnucleoside reverse transcriptase inhibitor (NNRTI)-based first-line regimens to protease inhibitor-based regimens in Africa, South America and Asia.
DESIGN AND METHODS
Multicohort study of 17 ART programmes. All sites monitored CD4 cell count and had access to second-line ART and 10 sites monitored viral load. We compared times to switching, CD4 cell counts at switching and obtained adjusted hazard ratios for switching (aHRs) with 95% confidence intervals (CIs) from random-effects Weibull models.
RESULTS
A total of 20 113 patients, including 6369 (31.7%) patients from 10 programmes with access to viral load monitoring, were analysed; 576 patients (2.9%) switched. Low CD4 cell counts at ART initiation were associated with switching in all programmes. Median time to switching was 16.3 months [interquartile range (IQR) 10.1-26.6] in programmes with viral load monitoring and 21.8 months (IQR 14.0-21.8) in programmes without viral load monitoring (P < 0.001). Median CD4 cell counts at switching were 161 cells/microl (IQR 77-265) in programmes with viral load monitoring and 102 cells/microl (44-181) in programmes without viral load monitoring (P < 0.001). Switching was more common in programmes with viral load monitoring during months 7-18 after starting ART (aHR 1.38; 95% CI 0.97-1.98), similar during months 19-30 (aHR 0.97; 95% CI 0.58-1.60) and less common during months 31-42 (aHR 0.29; 95% CI 0.11-0.79).
CONCLUSION
In resource-limited settings, switching to second-line regimens tends to occur earlier and at higher CD4 cell counts in ART programmes with viral load monitoring compared with programmes without viral load monitoring.