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

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0.0

0.1

0.2

0.3

0.4

0.5

0.6

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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

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till

SMM

fre

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0.4

0.5

0.6

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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

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urvi

ving

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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