1 / 19

Monitoring Entry, Retention, and ART Adherence

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

prentice
Download Presentation

Monitoring Entry, Retention, and ART Adherence

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

  3. Entry Monitoring • Entry into care shortly after dx associated with survival • Monitoring 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 benefits • Decreased morbidity/mortality • Decreased community viral load • Various metrics used • Visit adherence, gaps in care, visits per time frame • Logistics easier than for entry • Use medical records and admin data • May require integration of sources

  5. Adherence Vignette • 45 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 regimen • DRV/r in combination therapy • HIV-1 RNA <50 c/ml, CD4~300 cells/mm3

  6. Why Monitor? • Follow-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 failure • Irrespective of viral load monitoring (e.g., Bisson G, Gross R et al. PLoS Med 2008) • Intervention before failure • Same principles likely for entry and retention in care

  8. False Security of RNA Suppression • ATH02 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 Adherence interval without time shift time event or censor date Adherence interval with time shift time shift

  10. Timing of Non-Adherence

  11. Monitoring Recommendations • Assess 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 tone • Preamble admitting perfect adherence unrealistic, but desired • Allow for honesty • Specify time period of recall • Multiple potential tools • Choice of tool site specific

  13. Self-Report Examples • ACTG 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 Scale • Ask ~how many doses taken over past month • Place X on graduated line

  14. Use of Pharmacy Refill Data • Specify 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 capture • If centralized pharmacy: simple • If multiple commercial pharmacies: logistically challenging, but feasible

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

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

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

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

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

More Related