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 *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 1.2-25.5% 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 * 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. SinikithembaSOUTH AFRICAN ART RESISTANCE COHORT STUDIES (SARCS) 2005-2012Sunpath H; MarconIVC; Kuritzkes DK; Gordon M; Murphy RM Began caring for HIV/AIDS patients in 1992 ART available in 2002-3; PEPFAR rollout in 2004 via EGPAF Over 8,000 patients on ART (now transitioned to DOHclinics) 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
7. SARCSResistance after First-Line ART Marconi CID 2008
8. Variable Odds Ratio Confidence Interval p value Age (<35) 3.27 0.92-11.63 0.07 Recent OI (within 6 months of study enrollment) 2.20 0.70-6.88 0.18 CD4 count at study enrollment 0.87 0.23-3.33 0.84 HIV-1 RNA Viral Load at study enrollment <100,000 c/mL 7.97 0.82-77.21 0.10 Risk Factors for Drug Resistance(Multivariable Analysis) Marconi CID 2008
9. SARCSVirologic Suppression at 6 mo † * † * */† Significant N = 186 120 20 46 Murphy AIDS 2010
10. Clinical Outcomes at 6 mo * † 6 3 * 14 9 7 5 † 1 1 HC Hosp/OI ND */† Significant N = 186 120 20 46 Died, clinic default = 5 3 2 0 Murphy AIDS 2010
11. Risk Factors for Death at 6 mos after Switch NS in MV for VF or Death Murphy AIDS 2010

12. Murphy RA- Sunpath H,”, J Acquir Immune DeficSyndr. 2012 Virologic outcome according to adherence level over time

    Proportion with VL <50 c/mL
13. SARCS Adherence Pharmacy refill increases after initiation of 2nd line therapy, then declines; associated with virologic response Murphy –Sunpath JAIDS2012
14. 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
15. 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
16. HIVDR Early Warning Indicators (EWI) Programmatic Level* Prescribing practices LTFU 12 mos ART Retention on 1st Line ART at 12 mos/VL UD Timely ARV pickup ARV appointments ARV shortages Adherence Baseline HIVDR Individual Level Pharmacy Refill Data/Clinic Visits Pill Counts/Self-Reported Adherence Clinical Risk Factors Baseline Minority Drug Resistance Psychosocial Risk Factors *WHO recommends (http://www.who.int/hiv/topics/drugresistance/indicators/en/index.html)
17. Socioeconomic, Cultural and Psychological Determinants of Health Patient Adapted from Munoz 1996 Social Ecological Model
18. Barriers to Clinical Care Poverty/Economic Transportation Food Insecurity Disability Grants Poor social support Institutional Long wait times Negative staff experiences Poor health literacy Limited substance abuse treatment and mental health facilities Sociocultural Perceived stigmatization Influence of charismatic churches Traditional healers Gender Inequalities Political Migration Controversy over provision of HIV Tx Unfavorable policies KageeJ Health Pscyhol, Global Public Health 2010 Western Cape
19. Individual level Risk Factors for Virologic Failure Study Marconi VC, Sunpath H, Del Rio C. et al AIDS Pt Care STDs, August 2013
20. 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.
21. 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
22. 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
23. 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,lowCD4 counts, family recommending HIV care, and using a TV/radio as ART reminders (compared to mobile phones)
24. 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
25. Proposed Individual-Level EWI *These factors do not include those that were identified as baseline risk factors. Marconi, Sunpath et al AIDS Pt Care STDs
26. 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 VIROLOGICFAILURE IS AN EMERGENCY W/ OR W/O RESISTANCE
27. Institutional, Community and Societal Factors Comorbid Illness Socioeconomics Access “My pastor says I should not take ARVs” “I miss appointments because the clinic is crowded” “I miss appointments because the clinic is too far to travel” “I do not take my pills if I have to take it in front of others” “The lines are too long” “I forget to take my pills” “I feel too tired to go to the clinic” “I do not like to take my pills as they make me feel sick” VL Adherence Medications Psychosocial
28. 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
29. 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 Univariatelogistic regression was performed on sociodemographic data, gathered from semi-structured interviews and chart review, for the outcome of VF.Variablesfound significant (p<.05) were used in multivariate models.
30. Results Of 158 cases and 300 controls=35% were male and median age was 40 years. Median CD4+ T-cell count was 300 cells/uLand 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.
31. 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.
32. Pill Count plus Self-Reported Adherence Improves Prediction of ART Failure in SAWu 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.
33. 458 were enrolled from October 2010 to June 2012. 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 (0.99-1.14) for cases and 1.13 (1.08-1.18) for controls. The median MPR was 1.00 (0.97-1.07) for cases and 1.03 (0.96-1.07) 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).
34. 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. 
35. 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 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.
36. 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
37. New projectAntiretroviral drug resistance in KwaZulu Natal:1R01AI098558-01A1 8/1/13-7/31/17 . NIH/NIAID KZNHIV 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 forduration of treatment follow-up. DURATION 3 years SAMPLE SIZE 1000 subjects (500 at RKK -periurbanand 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.
38. 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.
39. Specific Aims -2 To determine the risk factors for VF and HIV DR in rural and peri-urban settings in the KZNprovince 1.Assess participants for risk factors for low ARV levels, suboptimal ART adherence andVF with or without DR and identify EWIfor 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 quantitativequestionnaire.
40. 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, virologicfailure 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
41. 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 theprevalence and clinical significance of minority drug-resistant variants in a subtype C-infected populationreceiving 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 virologicfailure will be selected at random for study. ASPCRwill be performed on stored baselinesamples of patients in the random cohort, and in all remaining patients with virologic failure not included in therandom cohort, to detect and quantify minority K103N, Y181C and M184V mutants. Samples will also be analyzed by deep sequencing using the IlluminaSolexa platform, and results of the two approaches compared.
42. ANALYSIS FOR ROLE OF MINOITY VARIANTS IN INDIVIDUALS The prevalence of minoritydrug resistant variants in the virus population will be determined from the random cohort; the effect of theseresistance mutations on the risk of virologic failure will be determined by univariable and multivariable logisticregression analyses; the multivariable analysis will control for additional risk factors including baseline VL, CD4cell count, treatment adherence and clinic setting. In exploratory analyses we will compare the frequencydistribution of minority drug resistance mutations with the frequency predicted from population genetic theory toestimate the fitness cost of these mutations compared to the wild type in the absence of drugs. The probabilitythat a minority variant becomes fixed in the population and leads to resistance and VF will also be determined.
43. 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. 2008,46:1589-1597. 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. 2010 Apr 24; 24(7):1007-12. PMID: 20397305. PMCID: PMC2902159. 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.JAcquir Immune DeficSyndr. .2011 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 DeficSyndr. 2012 Jun 11. [Epub ahead of print]. PMID: 22692090 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: 22739389 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).
44. 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 YunusMoosa Emory University Vincent Marconi Hannah Appelbaum Carlos del Rio Anna Hare Monique Hennink Brent Johnson Rachel Kearns David Stephens BaohuaWu Peng Wu Harvard/Einstein/MSF/JHU Daniel Kuritzkes Zhigang Lu Richard Murphy Jean Nachega Acknowledgments Support NIH/NIAID 1R01AI098558-01A1 Emory University CFAR Harvard University CFAR Bayer Diagnostics Gilead Pharmaceuticals