HIV and Aging: a Time for a New Paradigm Amy C. Justice, MD, MSCE, PhD Professor, Yale University Section Chief, General Internal Medicine VA Connecticut Healthcare System

Outline Epidemiology, demography of aging with HIV Describe Veterans Aging Cohort Study (VACS) – HIV Associated Non AIDS (HANA) Conditions – VACS Risk Index A new approach to comparative effectiveness and personalized medicine

Antiretroviral Therapy in 2011 Once a day pill well tolerated and achieves viral suppression in 84%* Median CD4 counts increasing Viral load declining AIDS defining events are rare * Gallent JE. et al. Tenofavir DF, Emtricitabine, and Efavirenz vs. Zidovudine, Lamivudine, and Efavirenz for HIV. NEJM : **McKinnell JA. et al ARV Prescribing Patterns in Treatment-Naïve Patients in the United States. AIDS Patient Care and STDs :79-85

More People Living with HIV Infection Every Year (+38K/yr*) CDC surveillance dataEach year: 56K new infections-18K deaths=38K*

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

>50% of Deaths Attributed to Non-AIDS Events Cumulative Mortality by COD Among Those on cART ( ) ART-CC, CID 2010:

Prevalence and Incidence Are Linked

AIDS Events Increasingly Rare ART-CC, Archives Int Med 2005:

AIDS Events Variably Associated with CD4 and Survival By Median (IQR) CD4 By Relative Hazard of Death ART-CC, CID 2009;48:

Losina et al CID 2009 Life Expectancy is not Normal Risk-adjusted HIV negative Optimal care HIV postive Mean age seroconversion of 33 years

HIV Epidemiology & Field Services Semiannual Report, NYCDOH. April 2010 Death Rate Disparities by HIV, Race/Ethnicity and Age

Delayed Presentation By Age (NA ACCORD) Altoff K. et al. In press JAIDS

Major Observations On ART, HIV is a complex chronic disease, not unlike insulin dependent diabetes or cancer in partial remission Annual new HIV infections exceed deaths; the population on ART is rapidly growing and aging We need an effective and efficient approach to caring for these individuals

VACS Long Term Objectives 1.Fully characterize treated HIV infection as a model of complex chronic disease with a dominant index condition 2.Use this model, risk stratification, and electronic medical records systems to revolutionize health care

VACS 8 SUBJECTS: 3,640 HIV infected; 3,640 uninfected –Group matched: age, race/ethnicity, and site SITES: Manhattan, Bronx, Washington DC, Baltimore, Pittsburgh, Atlanta, Houston, Los Angeles BASELINE: 2002 (8 years)

VACS Virtual Cohort Subjects –40,594 HIV infected Veterans –81,188 Age, Race, Region Matched 2:1 Scope –1998 to present –Baseline HIV infected patients at initiation of HIV care Controls selected and followed in same year

Arbitrated Clinical Events in VACS 8 ART Initiation (Complete, paper in process) Symptomatic Cirrhosis (Decompensated Liver Diseasepaper in press) Major Cancers (Nearly Complete) Myocardial Infarction (Underway) Stroke (Planned) COPD and Pneumonia (Planned)

HIV Associated Non AIDS Conditions (HANA)

*More AIDS and Non-AIDS Events Among Rx. Sparing Arm (HR 1.7 in SMART) NEJM 2006;355: Rx. Sparing Rx. Intensive Total All Cause Death Serious OI13215 Nonserious OI Major CAD, Renal, or Liver Disease Non AIDS Events Are Associated with HIV Disease Progression*

Definition: HIV Associated Non AIDS Conditions (HANA) After adjustment for established risk factors, association with HIV remains – Compare to demographically and behaviorally similar uninfected controls – Weaker (<2 fold) associations may be due to inadequate adjustment for risk factors May be due to HIV, ART or both

Freiberg M.S. et al. HIV is Associated with Clinically Confirmed MI. CROI 2011 Abstract# W-176

Fragility Fractures HIV+/- (n= 125,259) Womack J. et al. PLoS ONE February 2011 | Volume 6 | Issue 2 | e17217

Possible HANA Targeted Disease – Vascular: Myocardial Infarction, Thrombosis, and Stroke – Bone: Osteoporosis and Avascular Necrosis – Cancer: infectious e.g. Anal and non infectious e.g. Lung – Lung: pneumonia and COPD – Neurological : Peripheral neuropathy, ?dementia General Organ Injury – Liver Fibrosis: risk of, progression to, cirrhosis and hepatoma – Hematologic Disease: anemia, thrombocytopenia – Decreased Renal Function: most is not HIVAN

General Observations on HANA Multiple interacting HIV and non HIV causes – HIV typically not the most influential risk factor Incidence of event different from relative risk Adjusted relative risk HIV+/- highly variable – Association with CD4 variable – Degree to which these occur prematurely difficult to quantify – Competing risk of death is changing and unmasking risk associated with HIV

Warning! All these conditions have multiple, interacting, causes among HIV+/- The mix of causes driving these events among HIV+ may differ from HIV- Until we understand this mix, we must focus on what drives health outcomes in our patients

Veterans Aging Cohort Study Risk Index (VACS Index) Justice, AC. et. al, HIV Med Feb;11(2): Epub 2009 Sep 14. An index composed of routinely collected laboratory values that accurately predicts all cause mortality among those with HIV infection

Rationale for Multivariable Risk Index A single, summary measure of disease Identifies important thresholds for lab tests Resolves conflicting results Informs prioritization Has major statistical advantages – Decreased measurement error – Each person has a measurable outcome at any time point Justice AC. HIV and aging: time for a new paradigm. Curr HIV/AIDS Rep May;7(2):69-76.

Veterans Aging Cohort Study Risk Index (VACS Index) Composed of age and laboratory tests currently recommended for clinical management –HIV Biomarkers: HIV-1 RNA and CD4 Count –non HIV Biomarkers: Hemoglobin, hepatitis C, composite markers for liver and renal injury Developed in US veterans, validated in Europe and North America

32 Composite Biomarkers 32 AGE * AST PLT * sqrt(ALT ) FIB 4 = eGFR = * CREAT * AGE * FEM_VAL * BLACK_VAL FEM_VAL =0.742 if female, 1 if male BLACK_VAL =1.21 if black, 1 otherwise

Index Score RestrictedVACS Age (years)< to > CD4> cells/mm to to to to < HIV-1 RNA< copies/ml500 to 1x > 1x Hemoglobin> 140 g/dL12 to to < 1038 FIB-4< to > eGFR mL/min> to to < 3026 Hepatitis C Infection5 Age HIV Specific Biomarkers Biomarkers of General Organ System Injury Tate J. et al. IDSA 2010 Vancouver, BC October 21-24th. Poster 1136 VACS Index Thresholds and Weights

Age PLTALTAST FIB 4 Values by Age, ALT, and AST (Platelets 100k) FIB 4 >3.25 is worth 25 points, is worth 6 points

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

Discrimination of VACS vs. Restricted Index Justice AC. et al. A Prognostic Index for those Aging with HIV. CROI 2011 Poster # 793 Subgroup VACS Index C-stat Restricted Index C-stat p-value** Overall < Male Female <0.001 White Black Hispanic <0.001 Age <50 >= <0.001 < HIV-1 RNA <500 >= <0.0001

Calibration of VACS vs. Restricted Index (5 Year Mortality) Justice AC. et al. A Prognostic Index for those Aging with HIV. CROI 2011 Poster # 793

The Holy Grail: Surrogate Endpoint Must be an accurate predictor of target outcome Respond to changes in risk of the outcome due to treatment Detect differences in outcome due to treatment among different treatment arms

VACS Index Response to 1 st Year of cART (+/- 80% adherence) 39 Solid lines indicate >80% adherence

VACS Index Correlated with Biomarkers of Inflammation Justice AC et al,Biomarkers of Inflammation, Coagulation, and Monocyte Activation are Strongly Associated with the VACS Index among Veterans on cART CROI 2011 Poster # 796

VACS Index Summary Is associated with markers of inflammation Accurately predicts mortality among HIV patients in the US and Europe Responds to changes in risk associated with ART initiation Will likely prove a more reliable surrogate endpoint than any single biomarker

Why Is This Important? Uses lab tests currently part of routine care Identifies modifiable risk at earlier thresholds Incorporates age, and effects of HANA and toxicity Computation easy, can be included in lab reports and available through websites/apps Offers approach to personalizing and prioritizing care that goes beyond CD4 count and HIV-1 RNA

Example: Framingham Index Assigns points based on 6 factors (5 modifiable) Estimates risk of MI or death over the next 5-10 years ranging from 1% to >56% Assumes that change in risk due to smoking cessation is same as never having smoked, etc. 43 DAgostino RB. Et al. Validation of the Framingham Coronary Heart Disease Prediction Scores: Results of a Multiple Ethnic Groups Investigation. JAMA 2001;286:

Framingham Risk Assessment Risk score results: Age:60 Gender:male Total Cholesterol:280 mg/dL HDL Cholesterol:100 mg/dL Smoker:Yes Systolic Blood Pressure:120 mm/Hg On medication for HBP: No Risk Score*10% * The risk score shown was derived on the basis of an equation. Other NCEP materials, such as ATP III print products, use a point-based system to calculate a risk score that approximates the equation-based one. To interpret the risk score and for specific information about CHD risk assessment as part of detection, evaluation, and treatment of high blood cholesterol, see ATP III Executive Summary and ATP III At-a-Glance.ATP III Executive SummaryATP III At-a-Glance Results View:

Uses of Framingham Index Assesses relative importance of CHD risk for individual patients Quantifies absolute level of CHD risk for individual patients Allows clinicians and patients to match the level of treatment to the level of risk CHD guidelines are based on these estimates DAgostino RB. Et al. Validation of the Framingham Coronary Heart Disease Prediction Scores: Results of a Multiple Ethnic Groups Investigation. JAMA 2001;286:

Case Example 50 year old, HIV infected male on ART with an HIV-1 RNA<500, CD4 count 500, normal hemoglobin, creatinine, AST, ALT, and platelets. HCV negative. score 8; expected mortality* 4% – CD4 count 400 cells/mm3, score 18; expected mortality* 9% – Hemoglobin g/dL, score 28; expected mortality* 15% – Hemoglobin g/dL, score 40; expected mortality* 24% *In all cases referring to estimated 5 year mortality risk.

Risk and Revolution of Care Comprehensive Observational Data Finely Grained Risk Assessment for Major Outcomes Identification of Modifiable Risk Factors Link to Evidence Based Treatments through Integrated Decision Support with Point and Click Action RCTs of the Strategy of Care Tailored to Risk and Using Change in Risk as Outcome

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In Development: Interpretation Your score is XX. Among 100 veterans in VA care with HIV infection with this score, we would expect that YY would be alive at five years and ZZ would have died. The figures in grey represent those expected to live 5 years and the figures in black represent those expected to have died. 49

Counseling (Hypothetical) Based on your drinking pattern and use of tobacco, you could reduce your 5 year risk of mortality to XX if you stopped both If you stop smoking, your risk will be YY and if you stop drinking your risk will be XX Websites where you can learn more about – How to stop drinking include XX – How to stop smoking include XX If you would like to help us improve this site click here 50

Examples of Advice: Liver Disease Because you appear to have liver injury and have HCV infection, there are a number of things you can do to reduce you VACS Index Score… Review all your medications with your provider to identify any potentially liver toxic medications Cut down or abstain from alcohol Make sure not to skip doses of your ARVs Talk to your provider about taking medications to treat you HCV infection

Future Work Informatics: Develop information tool that calculates index, counsels on risk, identifies modifiable risk, and suggests patient action Observational Analyses: estimate likely effect size for potential interventions: eg, alcohol cessation, HCV treatment, adherence, etc. RCT: strategy trial among those with abnormal FIB 4 who drink alcohol

PI and Co-PI: AC Justice, DA Fiellin Scientific Officer (NIAAA): K Bryant Participating VA Medical Centers: Atlanta (D. Rimland), 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, H Leaf, J Leung), Pittsburgh (A Butt, E Hoffman), and Washington DC (C Gibert, R Peck) Core Faculty: K Akgun, S Braithwaite, C Brandt, K Bryant, R Cook, K Crothers, J Chang, S Crystal, N Day, R Dubrow, M Duggal, J Erdos, M Freiberg, M Gaziano, M Gerschenson, A Gordon, J Goulet, N Kim, M Kozal, K Kraemer, V LoRe, S Maisto, K Mattocks, P Miller, P OConnor, C Parikh, C Rinaldo, J Samet Staff: H Bathulapalli, T Bohan, D Cohen, A Consorte, P Cunningham, A Dinh, C Frank, K Gordon, J Huston, F Kidwai, F Levin, K McGinnis, L Park, C Rogina, J Rogers, L Sacchetti, M Skanderson, J Tate, E Williams Major Collaborators: VA Public Health Strategic Healthcare Group, VA Pharmacy Benefits Management, Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Yale Center for Interdisciplinary Research on AIDS (CIRA), Center for Health Equity Research and Promotion (CHERP), ART-CC, NA-ACCORD, HIV-Causal Major Funding by: National Institutes of Health: NIAAA (U10-AA13566), NIA (R01-AG029154), NHLBI (R01-HL095136; R01-HL090342; RCI-HL100347), NIAID (U01-A ), NIMH (P30- MH062294), and the Veterans Health Administration Office of Research and Development (VA REA ) and Office of Academic Affiliations (Medical Informatics Fellowship). Veterans Aging Cohort Study

National VACS Project Team 2010