1. Veterans Aging Cohort Studies (VACS) Update 2008
Amy Justice, MD, PhD
For the VACS Project Team

2. The VACS Project Team (in West Haven, CT)

3. VACS Enrollment Sites Cleveland, OH (VACS 3 only)
Pittsburgh, PA-Butt A.
Houston, TX-Rodriguez-Barradas M.
Los Angeles, CA-Goetz M.
Manhattan/Brooklyn, NY-Simberkoff M.
Bronx, NY-Brown S.
Washington, DC-Gibert C.
Baltimore, MD-Oursler KA.
Atlanta, GA-Rimland D.

5. Virtual Cohort Subjects Scope of Study 33,000 HIV infected Veterans
99,000 Age, Race/Ethnicity, Region 2:1 Matched Controls
Scope of Study
1998 to present
Baseline
HIV infected patients at initiation of HIV care
Controls selected and followed in same calendar year
Administrative, laboratory, and pharmacy data

6. Full Study 8 Sites HIV+ 1:1 Group matched to HIV- Baseline 2002
age, race/ethnicity, and site primary care pts.
Baseline 2002
Ongoing annual follow up surveys
Have access to all VA databases
Local electronic medical records
National VA databases (ICR, PBM, BIRLS, Patient treatment files, DSS, etc.)
Requesting access to Medicare data
Blood and DNA bank

7. International Multi Cohort Collaborations
ART-CC
NA-ACCORD
Harvard Causal Modeling

8. VACS Career Development Awards
Awardee
Institution
Funder
Amy Justice
CWRU
NIA, RWJGFS
Matt Freiberg
U of Pittsburgh
NIAAA, NIH
Adeel Butt
NIDA, NIH
Shawn Fultz
West Haven VA
VA HSR&D
Scott Braithwaite
Yale
NIAAA,RWJFS
Kristina Crothers
CTSA, NIH
Vin Lore
U of Pennsylvania
NIAAA
Kris Ann Oursler
U of Baltimore
NIA, NIH
Nancy Kim
VA HSR&D*
*Pending

9. Current NIH Support (Excluding CDAs)
NIAAA
U10 Main Grant (Justice)
R01 Braithwaite
R21s: Papas, Lim (Fultz), Braithwaite
NHLBI
R01 Crothers
R01 Berliner (NHLBI and NIA)
R01 Freiberg/Justice pending
NCI
Site contract, NA-ACCORD Justice

10. Current VA Support (Excluding CDAs)
VA HSR&D
HERA Cohort: Brandt
Informatics Consortium (Brandt, site contract)
REAP: Kearns pending
VA Clinical Support
Smoking cessation study: Crothers

11. Foundations, etc. Robert Wood Johnson Foundation, Braithwaite
Medical Research Council, Great Britain, Justice
AMFAR pending, Mattocks

12. Publications Study # Publications Virtual Cohort 8 VACS 3 12 VACS 5 164
VACS Infrastructure 30
International Collaborations 9
Total 79

13. VACS Center Infrastructure
Staff
3.5 FT Programmers
2 FT Biostatisticians
2 FT Operations Research Support
Faculty
3 FT Informatics Faculty
1 FT Biostatistics/Epidemiology
1 FT Operations Research Faculty
1 FT Behavioral Sciences Faculty
1 FT Health Care Policy Faculty
3 FT HSR&D/Clinical Epidemiology

14. Current Major Initiatives
Directly accessing VA EMR data for
Studying comorbid illness in HIV
Drug safety research
Quality of care
Intervention research
Validating derivative VA databases for same
Designing EMR intervention that optimizes utilization of resources to achieve patient and provider behavior change

15. Comorbidities and Toxicities in Older HIV Infected Patients

16. Mortality Rates (/1000 PY) Among HIV Infected and Uninfected Controls
Study
Age
HIV +
<1998
HIV+
2000>
HIV-
Reference
Denmark
25+
124 25 11
Ann Intern Med 2007; 146:87-95
New York
“adj” na 41
Ann Intern Med 2006;145:
Barcelona
16-65 45 10 5
HIV Medicine 2007;8:251-8
10 US sites
(HOPS)
adult 70 20
J Acquir Immune Decif Syndr 2006;43:27-34
NYC IVDU
114 54 15
CID 2005:41;864-72
Nat’l VA 35
~18
Med Care 2006:44;s13-24

17. “Non AIDS” Causes of Death Since ~2000
Source
Of Known
Leading Causes (%)
Reference
NY State
Death Certificates
26%
Alcohol/drug abuse (31%), CVD (24%), Cancer (21%)
Ann Intern Med 2006;145:
Barcelona
60%
Liver ( 23%), Infection (14%), Cancer (11%), CVD (6%)
HIV Med 2007:8;251-8
HOPS
Ascertainment
63%
Liver (18%), CVD (18%), Pulmonary (16%), Renal (12%), GI (11%), Infection (10%) Cancer (8%)
J Acquir Immune Defic Syndr 2006;43:27-34
Cascade
Liver (20%), Infections (24%), Unintentional (33%), Cancer (10%), CVD (9%)
AIDS 2006; 20;741-9

18. Braithwaite Model Computer simulation of natural history of HIV
Probabilistic
Mimics heterogeneity in clinical populations
Risk for death based on age (HIV uninfected veterans), CD4, viral load
3 major advances over other HIV models
Represents mutations in HIV genome
Represents nonadherence to HAART
Calibrated and validated using clinical data
We created a computer simulation of the natural history of HIV in the era of HAART. The simulation is probabilistic, and it therefore can mimic some of the heterogeneity seen in clinical populations. All patients were followed until death, and the risk for death was based on characteristics including age, CD4 count, and viral load. This simulation offers 3 major advances over other HIV models. First, it represents mutations in the HIV genome, and therefore can predict time to treatment failure of HAART regimens. It represents nonadherence to HAART, which has been shown to be the primary cause of virological failure in numerous patient groups and health care settings. Lastly, it has been calibrated and validated using clinical data, which increases its generalizability.
Braithwaite R. et al, Am J Med 2005;118:

19. “Background” Deaths on HAART
Percent Non-AIDS
Deaths
Age 70
Age 60
Age 50
Age 40
Age 30
CD4 >350 CD CD
CD4 <50
Braithwaite R, Am J Med 2005:

20. Incident “Comorbidity” in HIV
Aging
Treatment Toxicity
HIV Progression
Substance Use and Other Behaviors

21. Working Definition of Comorbidity
Any condition not included in the CDC list of AIDS defining conditions
“Non AIDS” Includes
HIV independent conditions
HIV associated, but not AIDS defining, conditions
Toxicity masquerading as comorbidity
One condition may have multiple causes

22. Attributable Risk
Background : event rate among uninfected individuals otherwise like those with HIV
HIV : event rate among those with HIV infection and/or disease progression
Attributable HIV Risk = HIV – Background
ARV : event rate among those on treatment
Attributable ARV Risk= ARV-HIV

23. Why Differentiate?
HIV Risk: Provide mechanistic insight into HIV and comorbidity (targets of intervention)
If mechanism similar as among uninfected, then general approach may be adapted
If mechanism atypical, may offer new insight
ARV Risk: Separate toxicity from background risk due to HIV, aging, and substance use
Consider risk of toxicity in choice of Rx
Mechanism of condition may be atypical

24. Do we have reason to believe that mechanisms of comorbidity differ among those in treatment for HIV compared to those without infection?

25. Strategies for Management of ARV Therapy (SMART)
RCT of interrupted ARV treatment based on immune reconstitution to minimize toxicity
Found that non AIDS events were higher in those randomized to interrupted therapy
HR 1.7
More liver and renal injury, and cardiovascular events
Concluded that at least some “non AIDS” events may be associated with HIV disease progression

26. [The SMART] finding suggests that elucidating the effect of uncontrolled viremia on cytokine expression, inflammation, and immune activation may also have implications for infected individuals not yet on ART.
— Rajesh T. Gandhi, MD Published in Journal Watch Infectious Diseases November 29, 2006

27. Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D)
Secondary data analysis, surprise finding
11 Cohort, >33000 pts., European
Increased risk for myocardial infarction with DDI (RR 1.5) and abacavir (RR 1.9)
Evident only while on drug or within 6 months of stopping
DAD Study Group, Lancet , published online April 2, 2008

28. …This finding was unexpected because neither drug is thought to have substantial effects on metabolic factors…
DAD Study Group, Lancet , published online April 2, 2008

29. Hayflick Limit on T Cell Response
Cytotoxic T cells (CD8 cells)
HIV and age associated with terminal “replicative senescence” after chronic antigen driven proliferation
Irreversible cell cycle arrest
Shortened telomeres
Inability to up regulate telomerase
Loss of CD28 expression
Apoptosis resistance
Reduced control of viral infection and cancer
Effros, Mechanisms of Ageing and Development. 2004:125;

30. Major Questions Does HIV increase risk of non AIDS events?
Cardiovascular disease
Liver disease
Renal disease
Cancer
What is the ARV toxicity risk for these events?
What are the likely mechanisms?

31. Veteran Non AIDS (ICD9) Events
Medical Disease
Substance Use Disorders
ANY ONE
Psychiatric Disorders
Goulet J., et al. Clin Infect Dis Dec15;45:

32. Veteran Multi Morbidity (ICD9)
Goulet J., et al. Clin Infect Dis Dec15;45:

33. Behavioral Risk Factors
Unpublished VACS Data

37. Metabolic and Vascular Disease

40. ICD9 codes for: mycardial infarction, stroke, transient ischemic attack and/or peripheral vascular disease

41. Lung Disease

42. Obstructive Lung Disease
Pack years of Smoking
Age in Years
Crothers, et al. Chest, 2006

43. Liver Disease

44. Cirrhosis, HIV, Complexity, and Age

45. Renal Disease

46. Work in progress VACS 8

47. Cancer

48. McGinnis KA, et al. Pre and post HAART cancer incidence among HIV positive veterans.   XIV International AIDS Conference, Barcelona, Spain, July 7-12, 2002.

49. Hepatocellular Carcinoma Standardized IRRs Comparing HIV Infected Patients With HIV Negative Controls (n 42,037)
Model 1 Model 2
Characteristic IRR 95% CI IRR 95% CI
HIV to to 1.6
HCV — — to 24.3
Alcohol ab/depend — — to 3.35
Age to to 1.10
Race
African American to to 2.65
Hispanic to to 8.76
Unknown/other to to 4.04
McGinnis et al. Hepatocellular Carcinoma and Non-Hodgkin’s Lymphoma: The Role of HIV, Hepatitis C Infection, and Alcohol Abuse J Clin Oncol :

50. Non-Hodgkin’s Lymphoma Standardized IRRs Comparing HIV-Positive Subjects With HIV-Negative Controls (n 41,906)
Model 1 Model 2
Characteristic IRR 95% CI IRR 95% CI
HIV to to 13.97
HCV — — to 1.06
Alcohol ab/depend — — to .96
Age to to 1.03
Race
African American to to 1.29
Hispanic to to 1.90
Unknown/other to to 1.09
McGinnis et al. Hepatocellular Carcinoma and Non-Hodgkin’s Lymphoma: The Role of HIV, Hepatitis C Infection, and Alcohol Abuse J Clin Oncol :

51. Summary
HIV is associated with comorbidities of aging with substance use
COPD, Liver and Renal disease, HCV, Cancer
HIV is not associated with common cardiovascular risk factors other than smoking
Lower risk of obesity, hyperlipidemia, diabetes, and hypertension
ARV therapy is associated with the cardiovascular risk factors listed above (of note HCV is associated with diabetes, decreased lipids, wasting)

52. Implications
Comorbid disease patterns are distinct in HIV+ / HIV- veterans
Some associated with HIV itself
Partially due to substance use
May also be due to decreased obesity and HCV infection
Guidelines for screening and treatment of comorbid disease require adaptation to HIV+ patients
Appropriate control groups (like those with HIV with respect to demographics, substance use, and HCV infection) will be essential to explore
Risk of ARV toxicity
Theories of early immune senescence
Alternative mechanisms of disease

53. How Do We Prioritize Care?
“Aggressive” HIV treatment
HAART to slow HIV disease progression
Prevention for positives
Comorbidity and toxicity
Prevention
Screening and management
Goals
Minimize HIV transmission and cumulative frailty
Maximize survival and quality of life
As efficiently as possible

54. Developing and Validating a Preclinical Frailty Index for HIV
Amy Justice, MD, PhD for the VACS Project Team

55. Frailty is
evident over time through an excess vulnerability to stressors, with reduced ability to maintain or regain homeostasis after a destabilizing event…[it] is currently identified through characteristics that are directly related to physical function and that at the same time are consequences of the accumulation of subclinical conditions, acute and chronic disease, and behavioral and social risk factors.
—AGS/NIA Conference on Frailty in Older Adults
Walston et al. J Am Geriatr Soc 54: , 2006

56. Pre Clinical Frailty is
The sum total “accumulation of subclinical conditions, acute and chronic disease, and behavioral and social risk factors” that provide an pre clinical indication of vulnerability.
Pre clinical frailty should be predictive of:
Mortality
Hospitalizations and urgent visits
Functional status
Progression of pre clinical frailty

57. Likely Markers of Pre Clinical Frailty among HIV Infected Individuals
Chronic viral infection/ inflammation (HIV, HCV, HBV)
Immune dysfunction/ reduced function
Bacterial and fungal infection
Immune “reconstitution” syndrome
Cancer
Aging/ substance use organ system dysfunction
Obesity and wasting
Pulmonary dysfunction
Vascular dysfunction
Renal dysfunction
Liver dysfunction
Bone marrow dysfunction
CNS dysfunction

58. Hypotheses
Markers of preclinical frailty will add substantially to the ART CC model
These will modify the association of ART-CC variables with mortality

59. 1st Step: Develop a Preclinical Frailty Index for HIV
Begin with established prognostic variables in HIV and mortality outcome
Add indicators of preclinical frailty
Begin with consistently measured and widely available measures
Add other important measures to determine marginal improvement in accuracy

60. Antiretroviral Treatment Cohort Collaboration (ART-CC) Model
13 cohorts of HIV infected starting combination ART (CART)
12,574 patients
344 deaths, 750 AIDS events
Median follow up 1.75 years (longest 5.5 years)
State of the art methods (Weibull model)
Based on
CD4 cells (<50; 50-99; ; ;>350)/ml3
HIV-1 viral load >5 log/ ml3
Age>50 years
IV Drug Use
Stratified by cohort and CD4 count
C statistic 0.73 in development paper

61. Not Included –Preclinical Frailty
Age>65
Organ system (Bone Marrow; Renal; Liver; GI-Wasting)
Behaviors (non IV Drug use; Alcohol; Tobacco; Adherence)
High risk diagnoses (ICD-9, labs, pharmacy/ self report)
Cancer (nonsquamous cell)
Viral hepatitis (HCV, HBV)
Other infectious diseases (fungal, mycobacterium, pneumonia, sepsis)
Diabetes
Pulmonary Disease (COPD)
Vascular Disease
Dementia
Depression
Functional status

62. Methods Development: Virtual Cohort- at CART initiation
7516 started CART 1/1/1999-8/1/2002
6439 (86%) CD4, VL, and hemoglobin levels 6 months
4,170 (56%) not missing any variables at baseline
1168 deaths within first 6 yrs
Censored at 6 yrs to comply with modeling assumptions
Validation and extension: VACS baseline
3,300 HIV+ and 3,300 HIV-
Approximately 600 deaths within first 4 years (2/3rds in the HIV+)

63. Analyses
Consider covariance and relative bivariate associations in grouping variables
Nested Models (log linear, Poisson, Cox)
ART-CC: cd4, vl, age 50+, IV drug use
ART-CC + labs: add FIB 4, eGFR, anemia, age 65+
ART-CC + labs + diagnoses: add cancer, copd or pneumonia, vascular disease, diabetes
Compare
C statistics
Calibration in the small
Discrimination
Short term and long term prediction

64. Etiologic Analyses
Consider association with CD4, age, and substance use
In strata of high and low CD4 determine whether variables still discriminate mortality
Stratify models by calendar year of CART initiation to see if this alters association with mortality

65. Validation Analyses in VACS
Evaluate missing data by imputing values
Validate in separate data (VACS)
Determine impact of variables in HIV- sample
Extend by testing additional variables in HIV+/-
Patient reported diagnoses
Wasting, Obesity
Functional Status
Smoking
IV drug use

66. Further Etiologic Analyses in VACS: CART Exposure
Divide HIV infected patients into 3 strata of CART exposure
Test whether, after adjustment for CD4, VL, adherence, and age, variables are associated with levels of CART exposure

67. Do Other Factors Contribute to Prognosis
Do Other Factors Contribute to Prognosis? And Do These Modify Association between ART CC and Mortality?

68. C Statistics:
Artcc: 0.66
+Labs: 0.71
+Diagnoses: 0.73

69. What if We Omit All AIDS Associated Variables?
(Omitting CD4 cell count, HIV-1 viral load, and AIDS Defining Conditions)

70. Observed Mortality Rates by Deciles of Risk
Deaths/100 PY

71. Prediction Deciles and Median CD4 Count
CD4 Cell Count (median) per mm3

73. Major Contributors to Mortality Risk
Advancing age
Low CD4 cell counts
Likely liver fibrosis
Likely renal failure
Anemia
Viral hepatitis
Cancer
Drug or alcohol abuse/addiction

74. Less Impact, But Significant
HIV-1 Viral Load
AIDS Defining Conditions
Vascular disease
Pulmonary disease
Diabetes

75. Next Steps Finalize variables in models
Impute missing and run with and without
Do nesting in the opposite direction as well
Finalize content of 1st, 2nd, 3rd papers
Adds information for mortality and associated with functional status (dev VC and val VACS)
6 month changes in markers important (VC and VACS)
Predicts renal and liver toxicity after drug exposure

76. How Do We Prioritize Care?
“Aggressive” HIV treatment
HAART to slow HIV disease progression
Prevention for positives
Comorbidity and toxicity
Prevention
Screening and management
Goals
Minimize HIV transmission and cumulative frailty
Maximize survival and quality of life
As efficiently as possible

77. Veterans Aging Cohort Study
PI and Co-PI: AC Justice, DA Fiellin
Scientific Officer (NIAAA): K Bryant
Participating VA Medical Centers: Atlanta (D. Rimland, C Jones-Taylor), Baltimore (KA Oursler, R Titanji), Bronx (S Brown, S Garrison), Houston (M Rodriguez-Barradas, N Masozera), Los Angeles (M Goetz, D Leaf), Manhattan-Brooklyn (M Simberkoff, D Blumenthal, J Leung), Pittsburgh (A Butt, E Hoffman), and Washington DC (C Gibert, R Peck)
Core Faculty: K Mattocks (Deputy Director), S Braithwaite, C Brandt, K Bryant, R Cook, J Conigliaro, K Crothers, J Chang, S Crystal, N Day, J Erdos, M Freiberg, M Kozal, M Gaziano, M Gerschenson, B Good, A Gordon, J Goulet, M Hernan, K Kraemer, J Lim, S Maisto, P Miller, L Mole, P O’Connor, R Papas, H Paek, J Robins, C Rinaldo, M Roberts, J Samet, B Tierney, J Whittle
Staff: D Cohen, A Consorte, K Gordon, F Kidwai, F Levin, K McGinnis, M Rambo, J Rogers, M Skanderson, F Whitsett
Major Collaborators: Immunology Case Registry, Pharmacy Benefits Management, Framingham Heart Study, Women’s Interagency HIV Study, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Health Economics Research Center (HERC), Center for Health Equity Research and Promotion (CHERP), ART-CC, NA-ACCORD
Funded by: National Institute on Alcohol Abuse and Alcoholism (2U10 AA 13566); National Institute on Aging (K23 G00826); Robert Wood Johnson Generalist Faculty Scholar Award; an Inter-Agency Agreement between National Institute on Aging, National Institute of Mental Health, and the Veterans Health Administration; the VHA Office of Research and Development; and, VHA Public Health Strategic Health Care Group.

78. Biometric Proxies for Comorbidity
FIB4>3.25 for liver cirrhosis
=age X AST /(platelets X Sqrt of ALT)
eGFR<30 for renal insufficiency
=186.3 X creatinine X age X (1-(0.258 X gender)) X (1+(0.21 X black))
BMI>30 for obesity
=703 X weight (lbs) / height2 (in2)