Predictors for antiretroviral therapy (ART) failure in an urban HIV/AIDS clinic in Addis Ababa, Ethiopia

Transcription

1 Predictors for antiretroviral therapy (ART) failure in an urban HIV/AIDS clinic in Addis Ababa, Ethiopia Katherine Niemeyer, Akilah King, Sophie Mengistu, Nils Hennig Department of Medical Education, Department of Preventative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York

2 Background HIV = human immunodeficiency virus Retrovirus with rapid replication and high mutation rate AIDS = acquired immune deficiency syndrome HAART = highly active antiretroviral therapy combination of multiple antiretrovirals

3 Background Treatment failure is categorized as: Clinical a clinical event that indicates severe immunodeficiency Immunologic CD4 count under 100 cells/mm 3, or under 200 cells/mm 3 for children under 5 years of age Virologic greater than 1000 copies/ml on two consecutive viral loads Start ART HIV RNA suppression HIV RNA >1,000 copies/ml Adherence Support Confirmed virologic failure >1,000 copies/ml Start Second-line ART Time to Failure Time to Switch

4 Background Progressive failure of the immune system Opportunistic infections Cancers Death High viral load increases risk of transmission Restricting viral replication early is imperative to effective treatment.

5 Introduction Ethiopia s HIV burden is substantial with 790,000 people living with HIV/AIDS, an adult HIV/AIDS prevalence of around 1.2%, at least 45,000 AIDS related deaths, and 900,000 orphans due to AIDS in 2013.

6 Introduction In Addis Ababa, Ethiopia, World Wide Orphans (WWO) addresses this by providing a home, education, and psychosocial care for orphaned and vulnerable HIV positive children In 2006, World Wide Orphans (WWO), in partnership with the AIDS Healthcare Foundation (AHF), instituted the Family Health Clinic (FHC) to provide diagnostic services and HIV/AIDS care to both adults and children. In 2012 alone, FHC provided HIV testing to over 50,000 people and treatment to around 2,000 adults and children.

7 Objective After eight years at the site, WWO has found a number of patients at the FHC who fail to respond to primary anti-retroviral therapy (ART). This quality improvement project evaluated patient characteristics to find those predictive of treatment failure at the FHC.

8 Methods A case control, retrospective chart review of 55 patients on secondline ART and 55 matched controls who continued to respond to firstline ART was conducted

9 Methods Patients were matched based on: sex baseline age treatment type treatment duration orphan status (pediatric patients) Bivariate analyses were performed and variables with p < 0.20 were used for logistic regression modeling Paired t-tests, McNemar s test Binary logistic regression

10 Results TABLE 1. Match criteria descriptive statistics for control and treatment failure cohorts Variables Control Treatment Failure P Value n Sex Male 30 (54.55%) 30 (54.55%) 1.00 Female 25 (45.45%) 25 (45.45%) Baseline Age (years) Treatment Type d4t-3tc-nvp d4t-3tc-efv 2 2 AZT-3TC-NVP AZT-3TC-EFV 9 9 TDF-3TC-NVP 8 8 TDF-3TC-EFV 7 7 Mo on 1st line treatment Orphan Yes 13 (50.00%) 13 (50.00%) 1.00 No 13 (50.00%) 13 (50.00%)

11 Results Treatment failure is categorized as: Clinical (16) a clinical event that indicates severe immunodeficiency Immunologic (23) CD4 count under 100 cells/mm 3, or under 200 cells/mm 3 for children under 5 years of age Virologic (32) greater than 1000 copies/ml on two consecutive viral loads TABLE 2. Characterization of patients who failed first-line ART Full Cohort Pediatric Adult Switch CD4 (cell/mm3) Switch viral load (copies/ml) 151.3x x x10 3 When viral load testing is performed, most patients have extremely high counts, indicating that they have failed treatment earlier than it is recognized

12 Results 1. Cough for > 2 weeks 2. Fever for > 2 weeks 3. Weight loss > 3kg 4. Night sweats > 2mo 5. History of TB contact One month of either: Fair adherence, 85-95% of doses taken Poor adherence, < 85% doses taken Soft drugs: Marijana, Khat TABLE 3. Baseline characteristics used for modeling risk factors of first-line treatment failure Variable Control Treatment Failure P Value WHO stage 1 11(20.00%) 5(9.09%) (29.09%) 15(27.27%) 3 24(43.64%) 34(61.82%) 4 4(7.27%) 1(1.82%) Baseline TB symptoms Yes 3(9.68%) 11(35.48%) 0.039* No 28(90.32%) 20(64.52%) Baseline CD4 count (cells/mm 3 ) * Previous Treatment Yes 15(27.27%) 25(45.45%) No 40(72.73%) 30(54.55%) Fair/Poor Adherence 2(3.85%) 14(26.92%) 0.002* Adult Education Yes 21(80.77%) 25(96.15%) No 5(19.23%) 1(3.85%) Alcohol use Yes 19(82.61%) 11(50.00%) No 4(17.39%) 11(50.00%) Soft drug use Yes 15(83.33%) 9(50.00%) No 3(16.67%) 9(50.00%) Hard drug use Yes 14(82.35%) 9(52.94%) No 3(17.65%) 8(47.06%) *p-value < 0.05, statistically significant

13 Results TABLE 4. Predictors for first-line treatment failure based on binary logistic regression modeling Variable Odds Ratio (95% CI) P Value Full cohort Fair or poor adherence ( ) Previous treatment 4.12 ( ) TB symptoms 3.64 ( ) Adults Soft drug use 0.02 ( ) CD4 count (cell/mm 3 ) ( ) Three factors were significant predictors for treatment failure: the presence of at least one month of fair or poor adherence, previous treatment at another clinic prior to entry at the FHC, and positive TB symptoms at baseline. For the cohort of adults, two other variables were predictive of treatment failure: lack of soft drug use and baseline CD4 count.

14 Discussion At least one month of fair or poor adherence More likely to fail treatment Previous treatment at another clinic prior to entry at the FHC More likely to fail treatment Positive TB symptoms at baseline More likely to fail treatment

15 Discussion Baseline CD4 count Patients with high CD4 are less likely to fail treatment Soft drug use Patients who use soft drugs are less likely to fail treatment

16 Discussion These predictors can be used to identify patients at high risk of treatment failure and allow for quicker viral load monitoring. A more rapid transition to second-line therapy suppresses viral replication and can improve outcomes. By recognizing these risk factors, clinicians can provide better care at the WWO/AHF Family Health Clinic in Addis Ababa, Ethiopia. Further research is needed to evaluate the non-use of soft drug and treatment failure.

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