efficacy of routine viral load, cd4 cell count, and clinical monitoring of adults taking...
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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
CROIBoston, USA
February 2008
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
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
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
HBAC monitoring evaluation Open cohort of 1,000 adults with HIV
and their family members
Weekly home visitsCD4 cell counts Viral loads
Weekly home visitsCD4 cell counts
Weekly home visits
Arm A Arm CArm B
Severe morbidity and mortality at 3 years
Setting
Kampala
Study
areaKampala
Study
area
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%
Antiretroviral regimens
• 1st line was nevirapine, lamivudine, and stavudine
• Efavirenz for concomitant TB treatment
• 2nd line was lopinovir/ritonovir, didanosine, and tenofovir
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
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
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
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
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
Participant characteristics at baseline
Arm AClinical monitoring
CD4 counts +VLN= 368
Arm BClinical monitoring
CD4 cell countsN=371
Arm CClinical monitoringN=377
P-value
Median age in years 37 38 39 P=0.96
Female (%) 75% 75% 67% P = 0.01
Median CD4 cell count (cells/ µL)
128 [61 - 194] 127 [62 - 130] 131 [70 - 197] P=0.65
HIV viral load (copies/ml)
Median [IQR]
233,000[77,900 - 513,000]
201,000{63,600 - 520,000]
210,000[74,600 - 570,000]
P=0.63
Depression Scale
Depressed (23-60) 148 (40%) 169 (46%) 153 (41%) P=0.59
Not depressed (0-22) 205 (56%) 189 (51%) 208 (55%)
Missing 15 (4%) 13 (4%) 16 (4%)
Time to event of severe morbidity or mortality
Log rank p=0.0671
A B C
Prop
orti
on s
till
SMM
fre
e
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
ITT time to first SMM (yrs)
0 1 2 3 4
A vs. B p=0.27B vs. C p=0.22A vs. C p=0.02
Log rank p=0.0089
A B C
Prop
orti
on s
till
SMM
fre
e
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
PP time to first SMM (yrs)
0 1 2 3 4
Intention-to-treat Per protocol
A vs. B p=0.46B vs. C p=0.034A vs. C p=0.004
Time to death
A vs. B p=0.73B vs. C p=0.36A vs. C p=0.21
Intention-to-treat Per protocol
A vs. B p=0.75B vs. C p=0.14A vs. C p=0.25
Log rank p=0.4283
A B C
Prop
orti
on s
urvi
ving
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
ITT time to all cause Death (yrs)
0 1 2 3 4
Log rank p=0.2857
A B C
Prop
orti
on s
urvi
ving
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
PP time to all cause Death (yrs)
0 1 2 3 4
Cox proportional hazards model First morbidity or mortality event
Number
participants
Events Follow-up
Time
Rate per 100 PYO
Adjusted
Hazard Ratio
Compared
to A
P-value Compared
to B
P-value
Arm A 368 47 979.4 4.8 -- --
Arm B 371 58 971.6 6.0 1.28
[0.84-1.97]
0.26 --
Arm C 377 72 950.9 7.6 1.88
[1.25-2.84]
0.002 1.47
[1.00-2.15]
0.047
Intention-to-treat
Number
participants
Events Follow-up
Time
Rate per 100 PYO
Adjusted
Hazard Ratio
Compared
to A
P-value Compared
to B
P-value
Arm A 349 24 884 2.7 -- --
Arm B 346 29 878 3.3 1.25
[0.71-2.19]
0.44 --
Arm C 352 47 852 5.5 2.25
[1.35-3.76]
0.002 1.80
[1.12-2.91]
0.016
Cox proportional hazards model First morbidity or mortality event
Per protocol
Specific disease morbidityIRR 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
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
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)
Response to treatment
≥2 viral loads >500 copies/ml after 90 days
Of these,changed to2nd line
Total changedto 2nd line
Changed to 2nd
line withdetectable viral load
Arm N (%) N (%) N N (%)
A 16 (5) 7 (44) 7 7 (100)
B 26 (8) 4 (15) 4 4 (100)
C 19 (5) 2 (11) 17 2 (12)
ALL 61 (6) 13 (21) 28 13 (46%)
90% complete viral suppression at 1 year
Viral load responseArm A Arm B Arm C P-value
Median viral load prior to change (copies/ml)
2500 13855 65750 0.25
Median viral load 6 months after change
<50 <50 348 0.66
Days with viral load >500 before change
189 170 548 0.0053
Median viral load prior to not changing
60200 2735 6330 0.37
Median viral load 6 month after not changing
<50 1340 7340 0.0082
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
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
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
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
AcknowledgementsDr. David MooreDr. Rebecca BunnellDr. Jordan TapperoDr. Willy WereDr. Paul WeidleDr. Sam MalambaDr. Elizabeth MadraaDr. Robert DowningPaul EkwaruDr. 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