Presentation is loading. Please wait.

Presentation is loading. Please wait.

KZN STUDY –UPDATE South African ART Resistance Cohort Studies (SARCS)

Similar presentations

Presentation on theme: "KZN STUDY –UPDATE South African ART Resistance Cohort Studies (SARCS)"— Presentation transcript:

1 Individual level Early warning indicators for Virological failure (independent of adherence)
KZN STUDY –UPDATE South African ART Resistance Cohort Studies (SARCS) Dr. Henry Sunpath McCord Hospital,Durban.

2 ART Need and Coverage 34 million people living with HIV
23.5 M (2/3) in SSA 5.6 M (16%) in SA 16.7 million people need ART* 11 M in SSA 2.5 M in SA 9.7 million people receiving ART 7.5 M in SSA (68%) 2.1 M in SA (84%) Need Receiving Need Receiving Currently, despite the tremendous effort to roll-out ARVs, the existing need is still far from being met. According to the new guidelines, which were developed in consultation with multiple technical and implementing partners, all adolescents and adults, including pregnant women, with HIV infection and a CD4 count at or below 350 cells/mm3 should be started on antiretroviral therapy, regardless of whether or not they have clinical symptoms. Those with severe or advanced clinical disease (WHO clinical stage 3 or 4) should start antiretroviral therapy irrespective of CD4 cell count. According to a recent WHO survey, several countries have already updated their national treatment guidelines to reflect this shift towards earlier initiation of antiretroviral therapy (see section 4.1.6). *2nes (25.9 M 2013) UNAIDS 2012 DOH SA 2012

3 VF and HIV Drug Resistance: No Small Problem
Worldwide estimates 3-30% virologic failure within one year of first ART (500K – 5M)* 40-95% individuals VF have > 1 major resistance mutation % of individuals on ART will have drug resistance within one year (200K – 4.3M)* Over time triple class failure will accumulate Over time transmitted resistance will grow –despite ART coverage High estimate of 645K with at least 1 major resistance mutation in SA * Calculated for 16.7 M requiring ART (15% in SA)

4 Key Clinical/Programmatic Questions
Can we predict which patients are likely to experience virologic failure? Before starting While on treatment Can we prevent these patients from experiencing virologic failure?

5 Outline of talk 1.Previous published data on the Durban cohort – SARCS
2.Early warning indicators at the individual level 3.Reports of other challenges in KZN 4.New NIH -research project

6 Sinikithemba SOUTH AFRICAN ART RESISTANCE COHORT STUDIES (SARCS) Sunpath H; MarconI VC; Kuritzkes DK; Gordon M; Murphy RM Began caring for HIV/AIDS patients in 1992 ART available in ; PEPFAR rollout in 2004 via EGPAF Over 8,000 patients on ART (now transitioned to DOH clinics) Active clinical, education and research program, integrated care Average rate of VF at 12 mos: 4-8%* Recent reports Rural KZN 12-40% (Mutevedzi Bull WHO 2010, CROI 2011) SA NHLS with 30% overall VL >400 Our initial examination of first line virologic failure emerged out of a study examining drug resistance in this setting. The rate of VF in SKT was quite low but other reports further from city centers showed more concerning rates. SA NHLS (SA National Health Laboratory Service)

7 SARCS Resistance after First-Line ART
This figure represents the percentage of subjects in the study cohort with at least one significant resistance mutation, those with dual class resistance and those with triple class resistance. Also this is the percentage of subjects in the study cohort with at least one NRTI, NNRTI and PI mutation, respectively. Marconi CID 2008

8 Risk Factors for Drug Resistance (Multivariable Analysis)
Odds Ratio Confidence Interval p value Age (<35) 3.27 0.07 Recent OI (within 6 months of study enrollment) 2.20 0.18 CD4 count at study enrollment 0.87 0.84 HIV-1 RNA Viral Load at study enrollment <100,000 c/mL 7.97 0.10 Multivariate regression analysis of variables associated with virologic failure and at least one significant mutation. Variables chosen were those found to be significant or near significant in the univariate analysis or known to (or potentially could) confound the variables if not adjusted for in the model. Adherence and Prior ARVs not associated with resistance in univariate, but were not able to put in the multivariate model. Results using a convergence model adjusting for each variable. Results for age, CD4 count, viral load and hemoglobin were not found to be more significant as continuous variables than the categorical variables used. The age of 35 was the mean age for the cohort, CD4 count threshold was chosen as an indicator for therapy initiation or AIDS (other categories were not significant), and hemoglobin was chosen as was clinically relevant (other categories were not significant). HIV-1 viral load of 300,000 copies/mL was found to be the most significant threshold, other categories were not significant. WHO stage compared IV (AIDS) to I-III (non-AIDS) using 2005 criteria. Marconi CID 2008

9 SARCS Virologic Suppression at 6 mo
* * With Resistance ITT (n) AT (n) Changed NRTI only (NNRTI) 33% (3) 50% (2) Changed NRTI only (PI) 100% (1) same No Change (NNRTI) 100% (3) same No Change (PI) 0% (1) same PI to NNRTI % (2) same Recycled NNRTI % (6) 80% (5) Without Resistance Changed NRTI only (NNRTI) 50% (2) 100% (1) Changed NRTI only (PI) 0% (1) same No Change (NNRTI) 57% (7) 80% (5) NNRTI to NNRTI 100% (2) same */† Significant N = Murphy AIDS 2010

10 Clinical Outcomes at 6 mo
* 6 3 * 14 9 7 5 1 1 Deaths: No Resistance PIN Enrolled Changed Reg Died SWO0006 6/27/ /15/05 SWO0022 8/22/05 10/3/05 11/3/05 (NRTI) SWO0100 8/28/ /6/06 Resistance SWO0010 8/2/05 9/19/05 10/31/05 (PI) SWO0012 7/25/ /4/05 SWO0032 9/26/05 10/17/05 12/31/05 (PI) SWO0026 9/5/ /31/05 ICH0027 3/22/ ? HC Hosp/OI ND */† Significant N = Died, clinic default = Murphy AIDS 2010

11 Risk Factors for Death at 6 mos after Switch
NS in MV for VF or Death Murphy AIDS 2010

12 Proportion with VL <50 c/mL
Murphy RA- Sunpath H,”, J Acquir Immune Defic Syndr Virologic outcome according to adherence level over time Proportion with VL <50 c/mL The link between adherence using medication poss ratio and virologic outcome was clear in the first 12 mnonths of 2nd line but broke down Unclear if this is due to survivor effect or if once pts are suppressed lapses in adherence are less critical than early on.

13 SARCS Adherence Pharmacy refill increases after initiation of 2nd line therapy, then declines; associated with virologic response Murphy –Sunpath JAIDS 2012

14 RFVF Study – McCord Hospital
Case control study –risk factors for VF (cases VL>1000 c/ml0 Between 3 Aug 2010 and 17 Mar 2011 – 585 patients initiated TDF/3TC/NNRTI 35 (6%) patients VF after 6 months 23/33 (69.7%)* VF patients had K65R/N Additional mutations with K65R/N Y115F (5), L74V (1), Y115F/S (1), M184V (3), T69D/N (2), K70T (1) V179D (4), Y181C (6), V106M (13), Y188C (3), G190A/E (6), V108I (1), A98G (1), K103N (6) No M184V/I Median baseline CD4 94 cells/uL (49-160) Median VL at VF 47,000 copies/mL (30, ,537) Sunpath H, Wu B, Gordon M, Hampton J, Johnson B, Moosa MY, Ordonez C, Kuritzkes DR, Marconi VC, “High rate of K65R for ART naïve patients with subtype C HIV infection failing a TDF-containing first-line regimen in South Africa”, AIDS, 2012 Jun PMID: High rate may be related to less frequent VL monitoring, in vitro rate, transmitted resistance. Recently up to 53 failures with same percentage of K65R. Sunpath AIDS 2012 *Compared to 2-5% in subtype B

15 Outline of talk 1.Previous published data on the Durban cohort – SARCS 2.Early warning indicators at the individual level 3.Reports of other challenges in KZN 4.New NIH -research project

16 Determinants of ART Response
Discussion- Determinants of ART Response Increased Immune Activation Immunologic Decline Disease Progression Increased Transmission Poor QOL and High Mortality Ongoing Viral Replication Access to Potent cART (Properly prescribed Combinations) Viral Replication Capacity, Virulence and Resistance Host Immune and Intrinsic Factors Pharmacokinetics Absorption Metabolism Drug Interactions Toxicity, Adverse Effects, Tolerability Treatment Fatigue Acceptance Adherence and Uptake Systemic and Intracellular Concentration Inhibition of Viral Replication Decreased Immune Activation Immune Reconstitution Arrested Disease Progression Decreased Transmission Improved QOL and Survival Behavioral Socioeconomic and Cultural Factors Nachega/Marconi IDDT 2011

17 HIVDR Early Warning Indicators (EWI)
Programmatic Level* Individual Level Prescribing practices Pharmacy Refill Data/Clinic Visits LTFU 12 mos ART Pill Counts/Self-Reported Adherence Retention on 1st Line ART at 12 mos/VL UD Clinical Risk Factors Timely ARV pickup Baseline Minority Drug Resistance ARV appointments ARV shortages Psychosocial Risk Factors Adherence Baseline HIVDR The EWI assess the extent to which sites are functioning optimally to prevent HIVDR. These indicators evaluate issues known to be associated with the emergence or prevention of HIVDR at the ART site level, including prescribing practices, losses to follow-up during the first year of ART the extent to which patients pick up their ARV drugs on time, and ARV drug shortages at the site level. EWI monitoring evaluates the key factors in ART sites without the expense of HIVDR testing, and results can be used to optimize both ART site and national ART program functioning. EWI are abstracted retroactively so that results are available for analysis more quickly than for the prospective surveys. WHO recommends monitoring six EWI (plus two optional EWI) to supplement results from HIVDR monitoring surveys at sentinel ART sites. Hong JAIDS 2010 Introduction: HIV drug resistance (HIVDR) testing is not routinely available in many resource-limited settings, therefore, antiretroviral therapy (ART) program and site factors known to be associated with HIVDR should be monitored to optimize the quality of patient care and minimize the emergence of preventable HIVDR. Methods: In 2009, Namibia selected 5 World Health Organization Early Warning Indicators (EWIs) and piloted abstraction at 9 ART sites: “ART prescribing practices, patients lost to follow-up at 12 months, patient retention on first-line ART at 12 months, on-time antiretroviral drug pick-up, and antiretroviral drug-supply continuity”. Results: Records supported monitoring of 3 of 5 selected EWIs. Nine of 9 (100%) sites met the target of 100% initiated on appropriate first-line regimens. Eight of 9 (89%) sites met the target of ≤20% lost to follow-up, although 20.8% of ART starters (range: 4.6%-44.6%) had a period of absence without documented ART coverage of 2.3 months (range: months). Six of 9 (67%) sites met the target of 0% switched to a second-line regimen. Conclusions: EWI monitoring directly resulted in public health action which will optimize the quality of care, specifically the strengthening of ART record systems permitting monitoring of 5 EWIs in future years and protocols for improved ART patient defaulter tracing. CROI 2011 #626 – Adherence and ARV shortages were most commonly suboptimal at 21 sites reviewed *WHO recommends (

18 Socioeconomic, Cultural and Psychological Determinants of Health
Patient In order to explain why the epidemic is concentrated in certain areas around the globe, we need to understand disease from a socio-ecological perspective. Adapted from Munoz 1996 Social Ecological Model

19 Barriers to Clinical Care
Poverty/Economic Sociocultural Transportation Perceived stigmatization Food Insecurity Influence of charismatic churches Disability Grants Traditional healers Poor social support Gender Inequalities Institutional Political Long wait times Negative staff experiences Migration Controversy over provision of HIV Tx Poor health literacy Unfavorable policies Limited substance abuse treatment and mental health facilities Although these barriers were identified in South Africa, it is striking how similar they are to here in Atlanta which is why I show them. There are many similarities with this area and many LMIC. Kagee J Health Pscyhol, Global Public Health 2010 Western Cape

20 Individual level Risk Factors for Virologic Failure Study
Marconi VC, Sunpath H, Del Rio C. et al AIDS Pt Care STDs, August 2013

21 Aim -Develop individual-level Early Warning for clinicians Design - A case control study was conducted at a Durban clinic. Patients after > 5 months of first-line antiretroviral therapy (ART) were defined as cases if they had VF (HIV-1 viral load, [VL] >1000 copies/mL) and controls (2:1) if they had VL < 1000 copies/mL. Methods-Pharmacy refills and pill counts were used as adherence measures. Participants responded to a questionnaire including validated psychosocial and symptom scales. Data were also collected from the medical record.

22 Methods Data Collection:
Semi-structured interview in preferred language, coordinator blinded to case/control status Questionnaire – demographic, socioeconomic (including a wealth index, employment, education and cohabitants), psychological (including substance abuse, food insecurity, traditional medicine use, safe sex practices, faith, stigma and intimate partner violence), modified ACTG adherence questionnaire, and clinic satisfaction indices Neurocognitive assessment and Pill count Study physician history/physical Symptom screen Karnofsky score Clinical information, pharmacy refills and laboratory data from the chart

23 Methods Statistical Analysis:
Access was calculated using the medication possession ratio (MPR) Adherence was calculated using unannounced pill counts and expected pill count from the pharm refills Multivariate model selection was performed by domain; significant variables were carried over to final models Model 1 – Baseline variables Model 2 – Complete model without Adherence or Access Model 3 – Complete model with Adherence and Access

24 Results Multivariable logistic regression models of VF included factors associated with VF (p<0.05) in univariable analyses. In the final multivariable model ,factors that were associated with VF independent of adherence measures are - Male gender, not having an active religious faith, practicing unsafe sex, having a family member with HIV, not being pleased with the clinic experience, symptoms of depression, fatigue or rash,low CD4 counts, family recommending HIV care, and using a TV/radio as ART reminders (compared to mobile phones)

25 Conclusions Low CD4 count, younger age and male gender were associated with VF, confirming previous studies Economic/structural barriers were associated with VF- none of these factors remained in the final models Psychological factors had the greatest universal impact including depression and fatigue, having no active faith, the clinic experience, family members with HIV, and who recommended the patient for treatment. Unsafe sex is likely a marker of risky behavior The use of d4T was associated with VF when compared to ZDV, TDF, ABC and ddI The models could also be a useful adjunctive measure if viral loads are not available

26 Proposed Individual-Level EWI
Baseline (While Initiating or Suppressed on ART) On ART Without Access/Adherence Measures* On ART With Access/Adherence Measures* Age Gender Faith Family Member HIV+ Treatment Supporter Clinic Recommendation Current Regimen Fluconazole Use Depression Unsafe sex practices Clinic Experience Fatigue Diarrhea Lipodystrophy Current CD4 count ARV Reminders Rash Adherence *These factors do not include those that were identified as baseline risk factors. Marconi, Sunpath et al AIDS Pt Care STDs

27 Summary Consider all aspects of the treatment paradigm with a key focus on adherence Pharmacy refills and pill counts are inadequate alone to predict failure Important to focus on both structural (institutional and economic) as well as psychosocial factors when designing interventions for patients Need to validate model in other settings (rural and peri-urban) Using individual-level EWI, interventions can be tailored VIROLOGIC FAILURE IS AN EMERGENCY W/ OR W/O RESISTANCE

28 Institutional, Community and Societal Factors
“I miss appointments because the clinic is crowded” “The lines are too long” Institutional, Community and Societal Factors Access VL Adherence Socioeconomics Comorbid Illness Psychosocial Medications “I feel too tired to go to the clinic” “I miss appointments because the clinic is too far to travel” “My pastor says I should not take ARVs” “I do not like to take my pills as they make me feel sick” “I do not take my pills if I have to take it in front of others” “I forget to take my pills”

29 Outline of talk 1.Previous published data on the Durban cohort – SARCS 2.Early warning indicators at the individual level 3.Reports of other findings in the Durban cohort 4.New NIH -research project

30 Gender-specific risk factors for virologic failure in KwaZulu-Natal: Automobile ownership and financial insecurity Hare a; Del Rio C, Sunpath H, Marconi VC.(CROI) We sought to test which socioeconomic indicators are risk factors for virologic failure among HIV-1 infected patients receiving antiretroviral therapy in SA Retrospective analysis of data from the RFVF study –case control Univariate logistic regression was performed on sociodemographic data, gathered from semi-structured interviews and chart review, for the outcome of VF.Variables found significant (p<.05) were used in multivariate models.

31 Results Of 158 cases and 300 controls=35% were male and median age was 40 years. Median CD4+ T-cell count was 300 cells/uL and Median HIV-1 viral load was 95,221 copies/mL among cases. In univariate and multivariate analyses, both automobile ownership and variables signifying financial insecurity were significantly associated with VF. Gender stratification revealed automobile ownership was a risk factor among males in multivariate analyses, while variables of financial insecurity (unemployment, non-spouse family paying for care, living with family) was a risk factor for women.

32 Conclusions In this cohort, financial insecurity among women and automobile ownership among men were risk factors for VF. Structural risk factor identification improves focus on high-risk individuals and provides direction for potential interventions.

33 Pill Count plus Self-Reported Adherence Improves Prediction of ART Failure in SA Wu P, Sunpath H, Marconi Vcet al. (CROI) Secondary analysis of data from a retrospective case-control study. Patient self-report, pill counts and pharmacy refills have been utilized to monitor adherence- limited data on the accuracy of combining them to help predict virologic failure in a real-clinic setting. Determine which method was most highly associated with VF after at least 6 months on ART.  At enrollment, pharmacy refill data were collected retrospectively from the medical chart, pill counts were performed to derive a pill count adherence ratio and a self-report questionnaire was administered to all participants.  

34 458 were enrolled from October 2010 to June 2012. Of these, 158 (34
458 were enrolled from October 2010 to June Of these, 158 (34.5%) had VF (cases) and 300 (65.5%) did not have VF (controls). The median (IQR) pill count adherence ratio was 1.10 ( ) for cases and 1.13 ( ) for controls. The median MPR was 1.00 ( ) for cases and 1.03 ( ) for controls. Univariate comparisons suggested that the pill count adherence ratio was higher for controls (p<0.0001) . Parametric smooth splines and receiver operator characteristic (ROC) analyses were utilized to assess the accuracy of the adherence methods. ROC analyses showed that the combination of pill count ART adherence plus self-reported questions were highly associated with VF (AUC=0.72).

35 Conclusion In this setting, a combination of pharmacy refill and self-report adherence questions had the highest diagnostic accuracy. Further validation of this simple and low-cost combination of measures is warranted in large prospective studies. 

36 Discussion-ART Program Use of EWI Results
Strengthened record keeping systems Formation of clinic specific care optimizing committees1 Validation of existing electronic record keeping systems1, 2,3 Adjustments in pharmacy record keeping to permit on time pill pick up assessments3 Pilot of enhanced defaulter tracing to identify patients missing drug pick-ups with the goal of reengaging in care within 48 hours1 General strengthening of records4,5,6,7,8 Seek funding support from partners to scale-up EWI9 District teams to support adherence and trace patients LTFU1,10,11 Scale-up viral load testing5 Regular review of patient pill pick-up and establishment of formal referral system to document transfers-in/out6 This pilot is planned to …. 1Hong et al. JAIDS 2010; 2 Anna Jonas, MoHSS Namibia, personal communication; 3Dawn Pereko, MSH Namibia, personal communication; 4Jack N et al. CID (in press); 5Ye M et al. CID (in press); 6Daonie e et al. CID (in pres); 7Nhan DT el al. CID (in press); Hedt BL et al., Anti Viral Ther 2008; 9Paula Mundari, Uganda National ART Programme, IAS 2010, Vienna; 10Evelyne B, National ART Program, Burundi, personal communication; 11Anna Jonas, MoHSS Namibia, personal communication.

37 Outline of talk 1.Previous published data on the Durban cohort – SARCS 2.Early warning indicators at the individual level 3.Reports of other challenges in Durban cohort 4.New NIH -research project

38 KZN HIV Drug Resistance Surveillance Study
New project Antiretroviral drug resistance in KwaZulu Natal: 1R01AI A1 8/1/13-7/31/17 . NIH/NIAID KZN HIV Drug Resistance Surveillance Study DESIGN: Enrollment Study Visit & Follow-Up Study Visit. Case control study- Cases with VF (VL >1000 cpm); controls will be virologically suppressed patients matched 1:1 for duration of treatment follow-up.  DURATION 3 years  SAMPLE SIZE subjects (500 at RKK -periurban and 500 at BETHESDA HOSP-urban )) POPULATION-Treatment-naïve patients with HIV-1, defined as HIV-infected, ART-naïve, men and women ≥18 years of age about to initiate ART at one of the two participating clinical sites with any CD4+ T-cell count.

39 Specific Aims-1 To determine ARV drug levels in red blood cells (RBC) associated with VF and HIV-1 DR at the time of 1st-line ART failure in KZN. A case-control study will be conducted to identify the relationship between ARV levels in RBC (measured using dried blood spots [DBS]) and VF with or without HIV-1 DR. Genotypic resistance testing -at the National Health Services Laboratory, Inkosi Albert Luthuli Hospital (IALH) in Durban on plasma samples from the first 200 cases. ARV drug levels in DBS - measured on cases and controls after at least 5 months on ART. ARV levels in RBC will be correlated with pharmacy refill data, pill counts, self-reported adherence and ARV levels in hair, as well as virologic outcomes and presence of drug resistance mutations.

40 Specific Aims -2 To determine the risk factors for VF and HIV DR in rural and peri-urban settings in the KZN province 1.Assess participants for risk factors for low ARV levels, suboptimal ART adherence and VF with or without DR and identify EWI for VF and HIV-1 DR along with their proximal sources. 2. Determine the extent to which use of Traditional African Medicine affects the virologic response to ART. Data will be collected from the medical record, a neurocognitive assessment, and from a semi-structured interview using a mixed qualitative and quantitative questionnaire.

41 Traditional African Medicine
WHO (2008) est 80% Africans use TAM; 70% Canadians, 42% US use CM SARCS and RFVF Study (Marconi CID 2008, Murphy AIDS 2010, Sunpath AIDS 2012, Marconi AIDS Pt Care STDs 2013) 70-80% have prior to enrollment at SKT 5-20% have some TAM involvement after ART initiation No relationship to drug resistance, virologic failure or clinical events Sutherlandia v. Placebo RCT (Wilson et al.) CD4 > 350, no concomitant ART No impact on CD4 count or VL, no toxicities; currently assessing QOL ACTG A5175 Additionally, both literature and anecdotal reports point to the possibility of dual use of TAM and WM by HIV patients in South Africa (Cook, 2009; Dahab et al., 2008; Hammond-Tooke, 1989; Karim et al., 2009; Malangu, 2007; Peltzer, 2001; Peltzer et al., 2006; Peltzer & Mngqundaniso, 2008; Peltzer et al., 2008; Peltzer et al., 2010). For example, one article suggested that HIV positive patients receiving ART may be reluctant to disclose TAM use to their medical providers because they fear they will receive worse medical treatment if their doctor(s) knows they consult TAM (Reid, 2008, p. 448) . Another study reported that Western health care providers’ perceptions of TAM may influence whether or not their patients report it, suggesting, for instance, that if a patient believes that his/her doctor thinks TAM is ineffective, it will not necessarily discourage the patient from using TAM; rather, that patient would use it in secret, and not report it (Thomas et al., 2010). 5175 – 4% TAM use at baseline, 10% at follow up

42 Specific Aims -3 To determine the effect of spontaneously arising drug-resistant minority variants on the risk of VF in patients receiving 1st-line ART. A case-cohort design will be used to determine the prevalence and clinical significance of minority drug-resistant variants in a subtype C- infected population receiving 1st-line NNRTI-based ART Participants will be patients initiating an NNRTI-based 1st-line ART. A sub cohort of participants including patients with and without virologic failure will be selected at random for study. ASPCR will be performed on stored baseline samples of patients in the random cohort, and in all remaining patients with virologic failure not included in the random cohort, to detect and quantify minority K103N, Y181C and M184V mutants. Samples will also be analyzed by deep sequencing using the Illumina Solexa platform, and results of the two approaches compared.

The prevalence of minority drug resistant variants in the virus population will be determined from the random cohort; the effect of these resistance mutations on the risk of virologic failure will be determined by univariable and multivariable logistic regression analyses; the multivariable analysis will control for additional risk factors including baseline VL, CD4 cell count, treatment adherence and clinic setting. In exploratory analyses we will compare the frequency distribution of minority drug resistance mutations with the frequency predicted from population genetic theory to estimate the fitness cost of these mutations compared to the wild type in the absence of drugs. The probability that a minority variant becomes fixed in the population and leads to resistance and VF will also be determined.

44 References 1. Marconi VC, Sunpath H, Lu Z, Gordon M, Koranteng-Apeagyei K, Hampton J, et al. Prevalence of HIV-1 drug resistance after failure of a first highly active antiretroviral therapy regimen in KwaZulu Natal, South Africa. Clin Infect Dis. 2008,46: 2. Murphy RA, Sunpath H, Lu Z, Chelin N, Losina E, Gordon M, Ross D, Ewusi AD, Matthews LT, Kuritzkes DR, Marconi VC; South Africa Resistance Cohort Study Team. “Outcomes after virologic failure of first-line ART in South Africa”, AIDS Apr 24; 24(7): PMID: PMCID: PMC 3. Singh A, Sunpath H, Green TN et al. Drug Resistance and Viral Tropism in HIV-1 Subtype C- Infected Patients in KwaZulu-Natal, South Africa:  Implications for Future Treatment Options.J Acquir Immune Defic Syndr Jun 24. 4 Murphy RA, Sunpath H, (joint first authors)Castilla C, Ebrahim S, Court R, Nguyen H, Kuritzkes D, Marconi VC, Nachega JB, “Second-line antiretroviral therapy: long-term outcomes in South Africa”, J Acquir Immune Defic Syndr Jun 11. [Epub ahead of print]. PMID: 5. Sunpath H, Wu B, Gordon M, Hampton J, Johnson B, Moosa MY, Ordonez C, Kuritzkes DR, Marconi VC, “High rate of K65R for ART naïve patients with subtype C HIV infection failing a TDF- containing first-line regimen in South Africa”, AIDS, 2012 Jun 27. [Epub ahead of print]. PMID: 6. Marconi, V., Wu, B., Hampton, J., Ordonez, C., Johnson, B., Singh, D., …Sunpath, H. (2013). Early Warning Indicators for first-line virologic failure independent of adherence measures in a South African urban clinic”. AIDS Patient Care and STDs 2013, (accepted).

45 Harvard University CFAR Gilead Pharmaceuticals
Acknowledgments Emory University Vincent Marconi Hannah Appelbaum Carlos del Rio Anna Hare Monique Hennink Brent Johnson Rachel Kearns David Stephens Baohua Wu Peng Wu Harvard/Einstein/MSF/JHU Daniel Kuritzkes Zhigang Lu Richard Murphy Jean Nachega McCord Hospital Sabelo Dladla Jane Hampton Helga Holst Sally John Roma Maharaj Phacia Ngubane Claudia Ordonez Melisha Pertab Sifiso Shange Henry Sunpath UKZN/DDMRI/RKK/Bethesda Jaysingh Brijkumar Kelly Gate Michelle Gordon Yunus Moosa Support NIH/NIAID 1R01AI A1 Emory University CFAR Harvard University CFAR Bayer Diagnostics Gilead Pharmaceuticals There are many individuals who should rightfully share in the triumph of the great work being accomplished in Durban. These are a few of them. I would also like to thank my mentor Dan Kuritzkes and the patients of Sinikithemba and iThemba clinics for their participation as well as to recognize their struggles. Are there any questions?

Download ppt "KZN STUDY –UPDATE South African ART Resistance Cohort Studies (SARCS)"

Similar presentations

Ads by Google