Efficacy of routine viral load, CD4 cell count, and clinical monitoring of adults taking antiretroviral therapy in Rural

1. Efficacy of routine viral load, CD4 cell count, and clinical monitoring of adults taking antiretroviral therapy in Rural Uganda Alex Coutinho MD MPH DTM&H Jonathan Mermin MD MPH et al CROI Boston, USA February 2008

2. Obstacles to rural HIV care • Dispersed population with limited transportation • Access to ART associated with cost of transport to health center • Prices for ART drugs have decreased dramatically in Africa and other costs now significant barriers for patients • Laboratory facilities often limited and testing expensive

3. Laboratory monitoring in HIV care • Baseline CD4 cell count and viral load associated with prognosis • CD4 cell count useful in determining eligibility for ART • Viral load during ART associated with clinical outcomes • Routine CD4 cell count and viral load every 3 months is norm in U.S. and Europe

4. Home-based AIDS Care Program • Adds ART and TB to care and prevention package for 1,000 people with HIV • Family VCT, basic care package and drug adherence • ART provided to all eligible adults and children in household • Weekly home visits by lay workers; no scheduled clinic visits after enrollment

5. HBAC monitoring evaluation Open cohort of 1,000 adults with HIV and their family members Arm A Arm B Arm C Weekly home visits CD4 cell counts Viral loads Weekly home visits CD4 cell counts Weekly home visits Severe morbidity and mortality at 3 years

6. Study Study area area Kampala Kampala Setting

7. Eligibility criteria • CD4 cell count ≤ 250 cells/µL or WHO clinical stage III or IV • Excluding isolated pulmonary TB • AST or ALT <5 times upper limit of normal • Creatinine clearance ≥25 ml/min • Karnofsky Performance Score ≥40%

8. Antiretroviral regimens • 1st line was nevirapine, lamivudine, and stavudine • Efavirenz for concomitant TB treatment • 2nd line was lopinovir/ritonovir, didanosine, and tenofovir

9. Data collection • Viral load and CD4 collected quarterly • Data collected from home visits, clinic visits and hospitalizations • Clinical conference on all deaths, hospitalizations, opportunistic illnesses, abnormal labs and changes in ART regimens

10. Treatment failure definition • First response adherence support • Arm A • 2 consecutive detectable viral loads However, if 50-5000 copies/ml and clinically well, could continue • CD4 cell count

11. Treatment failure for Arms B and C • Arm B • Persistently declining CD4 count measured on two separate occasions • Clinical failure • Arm C • Unintentional weight loss of >10% • CDC category C illness • Diarrhea or fever for >1 month without correctable cause • New or recurrent oral, esophageal, vaginal candidiasis

12. Analyses • Kaplan-Meier analysis of time to first event of severe morbidity or mortality, and death alone • Cox proportional hazard models • Poisson regression analyses for hospitalizations, morbidity • Intention-to-treat from date of randomization and per protocol from >90 days after initiating ART

13. Results • 1116 ART-naïve individuals randomized • 1094 started ART • 8% WHO stage IV; 31% stage III • Median follow-up 3 years • 126 deaths (11.2%) • 47% in first 3 months • 148 AIDS-defining illnesses • 57% in first 3 months • 61 (5.8%) had 2 viral loads >500 copies/ml • 28 (2.7%) changed to 2nd line drugs

14. Participant characteristics at baseline

15. Time to event of severe morbidity or mortality Per protocol Intention-to-treat A vs. B p=0.46 B vs. C p=0.034 A vs. C p=0.004 A vs. B p=0.27 B vs. C p=0.22 A vs. C p=0.02

16. Time to death Intention-to-treat Per protocol A vs. B p=0.73 B vs. C p=0.36 A vs. C p=0.21 A vs. B p=0.75 B vs. C p=0.14 A vs. C p=0.25

17. Cox proportional hazards model First morbidity or mortality event Intention-to-treat

18. Cox proportional hazards model First morbidity or mortality event Per protocol

19. Specific disease morbidity IRR p-value • Tuberculosis • C vs. A 1.7 p=0.043 • C vs. B 1.7 p=0.045 • Pneumocystis jiroveci pneumonia • C vs. A 8.7 p=0.01 • C vs. B 17.2 p=0.009 • Cryptococcal disease • C vs. A 2.3 p=0.044 • C vs. B 3.1 p=0.013 • Kaposi’s sarcoma • C vs. A 3.3 p=0.07 • C vs. B 1.6 p=0.39

20. Cox proportional hazards models comparison of mortality • Intention-to-treat adjusted hazard ratio • Arm C compared with A 1.58 (0.97-2.6) p=0.07 • Arm C compared with B 1.38 (0.9-2.2) p=0.18 • Arm B compared with A 1.14 (0.7-1.9) p=0.60 • Per protocol adjusted hazard ratio • Arm C compared with A 1.58 (0.9-2.8) p=0.14 • Arm C compared with B 1.72 (0.9-3.2) p=0.09 • Arm B compared with A 1.23 (0.7-2.1) p=0.43

21. Treatment failure • Similar number of people with 2 viral loads >500 copies/ml per arm: • Arm A: 16, Arm B: 26, Arm C: 19 • Having viral loads >500 copies/ml was associated with increased severe morbidity or mortality (18% vs. 10%; p=0.049)

22. Response to treatment 90% complete viral suppression at 1 year

23. Viral load response

24. Arm C • 15 people changed to 2nd line therapy with undetectable viral load, all were changed because of AIDS-defining events: • Number of cases • Cryptococcal disease 6 • Tuberculosis 6 • Kaposi’s Sarcoma 4 • Cervical cancer 2 • Cytomegalovirus 1 • Recurrent pneumonia 1 • All occurred >1 year after starting therapy

25. Why did Arms A and B do better? • Not only because of earlier regimen change in failing patients • <50% in Arms A and B with VL >500 copies changed • Adherence resulted in subsequent suppression • Viral load and CD4 cell count monitoring detected adherence issues before the occurrence of morbidity or mortality • Clinical criteria were poorly sensitive and poorly specific to detect adherence challenges

26. Conclusions • All study arms performed well • 1 year mortality in Arm C (9%) lower than all but one study in Africa • Rates of viral suppression high • Lay workers can effectively deliver drugs, support adherence, and monitor patients without scheduled clinic visits • Supporting adherence is the important determinant of success

27. How should ART be monitored? • Clinical monitoring alone was associated with increased rate of new AIDS-defining events and trend towards increased mortality • Build laboratory capacity • No benefit seen for adding quarterly viral load measurements to CD4 cell counts • However there is need to determine long-term outcomes and cost-effectiveness

28. Dr. David Moore Dr. Rebecca Bunnell Dr. Jordan Tappero Dr. Willy Were Dr. Paul Weidle Dr. Sam Malamba Dr. Elizabeth Madraa Dr. Robert Downing Paul Ekwaru Dr. Richard Degerman HBAC participants CDC-Uganda staff in Tororo and Entebbe Uganda Ministry of Health TASO Uganda Uganda PEPFAR Team CDC-Atlanta USAID OGAC DSMB Acknowledgements