Aging and HIV: Prognostication, personalization, and prevention

1. Aging and HIV: Prognostication, personalization, and prevention • R Scott Braithwaite, MD, MS, FACP Chief, Section of Value and Comparative Effectiveness New York University School of Medicine; NY; U.S.A.      

2. Personalizing screening recommendations for HIV-infected • HIV-infected population is aging • More screening recommendations applicable • Cancer, other • Increasing emphasis on personalized medicine • Health information technology • Personalize algorithms at point-of-care • How should screening recommendations be personalized for HIV-infected individuals?

3. Projected *Data from 2008, onward projected based on 2001-2007 trends (calculated by author), 2001-2007 data from CDC Surveillance Reports 2007. New York and San Francisco data from their Departments of Public Health.

4. Personalizing screening recommendations for HIV-infected • HIV is now chronic disease • Framework for personalizing screening • Braithwaite RS et al, 2009, Medical Care • Braithwaite RS et al, 2011, Med Decis Making • Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks • If benefit/harm more favorable, then earlier and/or more frequent screening favored • If benefit/harm less favorable, then later and/or less frequent screening favored • If harms > benefits considering competing risks screening not recommended

5. Illustrative cases: Screen for colorectal cancer? • Case 1: 62 year-old male, CD4 590, undetectable viral load, first-line ARV, no major comorbidities • Case 2: 62 year-old male, CD4 46, viral load 3,500; 3rd line ARV, atrial fibrillation (on coumadin), Hep C, mild anemia

6. Personalizing screening • Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks • If benefit/harm more favorable, then earlier and/or more frequent screening favored • If benefit/harm less favorable, then later and/or less frequent screening favored • If harms > benefits considering competing risks screening not recommended Braithwaite RS et al, Medical Care, 2009

7. Personalizing benefits of colorectal cancer screening • HIV increases risk for CR cancer by RR 2.3 • Therefore potential benefit from screening increased by RR 2.3 • But need to also consider other chronic diseases, medications, and risk factors

9. Personalizing benefits • Case 1 • Healthy 62 year-old well controlled HIV • Benefit 2.3 X greater than typical person because of HIV • Case 2 • 62 year old poorly controlled HIV and other chronic diseases • Benefit 2.3 X greater than typical person because of HIV

10. Personalizing screening • Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks • If benefit/harm more favorable, then earlier and/or more frequent screening favored • If benefit/harm less favorable, then later and/or less frequent screening favored • If harms > benefits considering competing risks screening not recommended Braithwaite RS et al, Medical Care, 2009

11. Personalizing harms of colorectal cancer screening • HIV itself not known to impact harms • But need to consider other chronic diseases, medications, and risk factors

13. Personalizing harms • Case 1 • Healthy 62 year-old well controlled HIV • Harm unchanged from typical person • Case 2 • 62 year-old poorly controlled HIV and other chronic diseases • Harm 4.0 X that of typical person because of coumadin

14. Personalizing screening • Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks • If benefit/harm more favorable, then earlier and/or more frequent screening favored • If benefit/harm less favorable, then later and/or less frequent screening favored • If harms > benefits considering competing risks screening not recommended Braithwaite RS et al, Medical Care, 2009

15. Personalizing competing risks • HIV: little effect if well controlled, large effect if poorly controlled • Need to consider other chronic diseases, medications, and risk factors • Instruments for quantification include • VACS index • Computer simulation

16. Veterans Aging Cohort Study Risk Index (VACS Index) • Composed of age and laboratory tests currently recommended for clinical management • HIV Biomarkers: HIV-1 RNA, CD4+ count, AIDS defining conditions • “Non-HIV Biomarkers”: Hemoglobin, hepatitis C, composite markers for liver and renal injury • Developed in US veterans, validated in Europe and North America

17. VACS Index Highly Predictive of Long Term (5 Year) All Cause Mortality Justice, AC. et. al, HIV Med. 2010 Feb;11(2):143-51. Epub 2009 Sep 14. Justice AC. HIV and Aging: Time for a New Paradigm. Curr HIV/AIDS Rep. 2010 May;7(2):69-76

18. VACS Index in OPTIMA Brown S.T. et al. Poster Presentation, Abstract #16436 International AIDS Conference 2010

19. VACS Index Response to 1st Year of cART (+/- 80% adherence) Solid lines indicate >80% adherence Tate J. et al. Change in a prognostic index for survival in HIV infection after one year on cART by level of adherence. IDSA 2010. Poster # 1136

20. Computer Simulation • Widely published, calibrated and validated • Braithwaite RS et al, Am J Med, 2005 • Braithwaite RS et al, J AntimicrobChemother 2006 • Braithwaite RS et al, Value in Health, 2007 • Braithwaite RS et al, Annals Intern Med, 2008 • Braithwaite RS et al, Clin Infectious Dis 2009 • Braithwaite RS et al, Med Care, 2010 • Mechanistic, represents reasons for failing ARV • Nonadherence to ARV • Resistance accumulation • Estimates life expectancy based on age, sex, baseline CD4, baseline viral load, baseline resistance, ART adherence, ART initiating criteria, switching criteria, and sequencing

22. Calibration

23. Validation

24. Personalizing screening • Estimate benefit/harm ratio based on personalized benefits, harms, and competing risks • If benefit/harm more favorable, then earlier and/or more frequent screening favored • If benefit/harm less favorable, then later and/or less frequent screening favored • If harms > benefits considering competing risks screening not recommended Braithwaite RS et al, Medical Care, 2009

25. Personalizing competing risks • Case 1 • Healthy 62 year-old well controlled HIV • VACS index: Life Expectancy >>10 years • Simulation: Life Expectancy >>10 years • Case 2 • 62 year-old poorly controlled HIV and other chronic diseases • VACS Index: Life Expectancy 4.1 years • Simulation: Life Expectancy 5.1 years

26. Case 1: Personalized harm/benefit of colorectal cancer screening • Benefits increased by 2.3-fold • Harms unchanged • Therefore personalized benefit/harm ratio = 2.3 • Competing risks minimally affected • HIV well controlled and does not add clinically significant mortality burden • Therefore Life Expectancy >> 10 years using either VACS index or computer simulation

27. Life expectancy needed for benefits from CR screening to exceed harms Braithwaite et al, Medical Care, 2009

28. Case 1: Personalized harm/benefit of colorectal cancer screening • Since Case 1 would require 5.3 years to have benefits exceed harms and is expected to live >> 10 years, Case 1 would benefit from colorectal cancer screening more than typical person • Raises question of whether screening should begin at earlier age or with greater frequency

29. Case 2: Personalized harm/benefit of colorectal cancer screening • Benefits increased by 2.3-fold • Harms increased by 4.0-fold • Therefore personalized benefit/harm ratio = 0.6 • Competing risks increased greatly • VACS index: Life Expectancy 4.1 years • Simulation: Life Expectancy: 5.1 years

30. Life expectancy needed for benefits from CR screening to exceed harms Braithwaite et al, Medical Care, 2009

31. Case 2: Personalized harm/benefit of colorectal cancer screening • Since Case 2 would require 6.0 years to have benefits exceed harms and has life expectancy of only 4.1 years (VACS index) or 5.1 years (Computer Simulation), Case 2 would not benefit from colorectal cancer screening • Benefit exceeds harms • Screening should not be part of “denominator” for quality measure or P4P

32. Conclusions • HIV-infected persons may benefit from personalized screening recommendations • Sometimes favor more aggressive screening • Sometimes favor less aggressive or no screening • Personalization occurs by considering effects of HIV, other chronic diseases, and risk factors • Screening-attributable benefits • Screening-attributable harms • Competing risks • Personalizing may be facilitated by HIT and EMRs