1. Monitoring Entry, Retention, and ART Adherence Robert Gross, MD MSCE Associate Professor of Medicine (ID) and Epidemiology University of Pennsylvania Perelman School of Medicine Penn Infectious Diseases CCEB

2. Monitoring Overview Most research on adherenceMost research on adherence Entry and retention have emerged as highly importantEntry and retention have emerged as highly important –Less data available on “how to” –More local logistics come into play Overarching messageOverarching message –“Monitoring provides key data on which patients need interventions”

3. Entry Monitoring Entry into care shortly after dx associated with survivalEntry into care shortly after dx associated with survival Monitoring challengeMonitoring challenge –Multiple sources of data (e.g., dedicated testing sites, clinics) –Responsible parties need to be identified and logistics arranged

4. Retention Monitoring Retention has multiple benefitsRetention has multiple benefits –Decreased morbidity/mortality –Decreased community viral load Various metrics usedVarious metrics used –Visit adherence, gaps in care, visits per time frame Logistics easier than for entryLogistics easier than for entry –Use medical records and admin data –May require integration of sources

5. Adherence Vignette 45 y.o. HIV infected man45 y.o. HIV infected man –Philadelphia VAMC –Serial monoRx in 90s, then HAART –Excellent adherence, but multiple resistance mutations acquired –CD4=0 (0%) x 3 years New regimenNew regimen –DRV/r in combination therapy –HIV-1 RNA <50 c/ml, CD4~300 cells/mm 3

6. Why Monitor? Follow-up visitFollow-up visit –HIV-1 RNA<50 copies/ml –Queried re: adherence as always –Had stopped meds entirely for 3 wks! –New onset depression –Depression/non-adherence overcome –Resumed adherence and no subsequent virologic failure

7. Need for Continued Monitoring Can detect impending failureCan detect impending failure –Irrespective of viral load monitoring (e.g., Bisson G, Gross R et al. PLoS Med 2008) Intervention before failureIntervention before failure Same principles likely for entry and retention in careSame principles likely for entry and retention in care

8. False Security of RNA Suppression ATH02 studyATH02 study –Observational –EFV-based regimen –HIV-1 RNA<75 copies/ml –Monitored RNA monthly –MEMS for adherence monitoring –Follow until breakthrough or 1 year Gross R et al, HIV Clinical Trials, 2008

9. Timing of Adherence and Outcome time event or censor date time shift Adherence interval without time shift Adherence interval with time shift

10. Time Shift Prior to Event Date VL<1000n=109 VL>1000 n=7 p value 0 days 96% (83-100%) 38% (12-100%) 0.03 30 days 96% (86-100%) 63% (24-100%) 0.08 60 days 96% (87-100%) 71% (42-96%) 0.04 90 days 95% (86-100%) 57% (51-72%) 0.008 Timing of Non-Adherence

11. Monitoring Recommendations Assess adherence each visitAssess adherence each visit –Self-report –Pharmacy refill data (MPR) –Do not recommend microelectronic monitors at this time –Do not recommend drug concentrations at this time –Do not recommend routine pill counts

12. Self-Reports Must use non-judgmental toneMust use non-judgmental tone –Preamble admitting perfect adherence unrealistic, but desired –Allow for honesty Specify time period of recallSpecify time period of recall Multiple potential toolsMultiple potential tools –Choice of tool site specific

13. Self-Report Examples ACTG questionnaireACTG questionnaire –How many doses missed yesterday, 1, 2, and 3 days before –How many doses missed over w/e? –When last dose missed? Visual Analog ScaleVisual Analog Scale –Ask ~how many doses taken over past month –Place X on graduated line

14. Use of Pharmacy Refill Data Specify period of interestSpecify period of interest –Past 1, 2, 3 months for example –Cannot be shorter than length of days supply –Too long may be irrelevant data Ensure full data captureEnsure full data capture –If centralized pharmacy: simple –If multiple commercial pharmacies: logistically challenging, but feasible

15. Medication Possession Ratio Fourth fill }}} First fill Second fill Third fill First interval Second interval Third interval Adherence metric: ( Σ interval days supply) / (4 th fill date-1 st fill date) Time Grossberg R et al, J Clin Epi 2004

16. Drug Concentrations Variable association with outcomeVariable association with outcome –Some drugs strongly associated –Different pts on different drugs –Variability across drugs limits programmatic utility Logistical limitationsLogistical limitations –Need for specimens (blood, hair) –Need for sophisticated lab –Turnaround time –Cost

17. Pill Counts Weak association with outcomeWeak association with outcome –Yet commonly used –Demanding of staff time Other valueOther value –Limits dispensing expensive drug if supply not used –Can add information to pharmacy refill data

18. Microelectronic monitors Strongly associated with outcomeStrongly associated with outcome –Can provide objective feedback –Useful in intervention –Granular view of dose timing and daily taking Logistical limitationsLogistical limitations –Cumbersome –Inconvenient (cannot pocket doses) –Cost

19. Conclusions Monitor entry in careMonitor entry in care –Collate sources of data –Establish responsibilities for linkage Monitor retentionMonitor retention –Track clinic administrative records Monitor adherenceMonitor adherence –Self-report or refill records –Other techniques need refinement or replacement