Targeting Clinical Intervention Among Veterans with HIV Infection: Adherence, Alcohol, and Non-ARV Treatment Toxicity University of Pennsylvania Center.

Targeting Clinical Intervention Among Veterans with HIV Infection: Adherence, Alcohol, and Non-ARV Treatment Toxicity University of Pennsylvania Center for Clinical Epidemiology and Biostatistics Amy Justice, MD, PhD, MSCE November 20, 2008

The “Village” at West Haven

Why Study HIV and Aging? A new phenomenon of increasing proportion
By understanding differences in the aging process among those with/without HIV we gain insights into How immune dysfunction influences aging How aging influences immune dysfunction The role of treatment toxicity in frailty As a template for chronic disease management

Long Term Goal Use the VA as a laboratory to develop and
test innovative interventions to optimize care of chronic disease—starting with HIV infection.

Mortality Rates Among HIV-infected and Uninfected Controls
Study Age HIV+ HIV- Reference <1998 2000+ 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 IDU 114 54 15 CID 2005:41;864-72 Nat’l VA 35 ~18 Med Care 2006:44;s13-24 HIV negative populations are mostly age-adjusted Prior to 1998 HIV mortality was extremely high. It has come down since 2000, as HAART was used and become more comfortable to providers. HIV mortality remains about 2-fold higher than HIV- mortality Mortality rates are per 1000 person-years

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 If HIV were no longer an issue, then patients would die of other causes. In an effort to piece apart the causes of mortality, we need to know what mortality we would expect in the population. 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:

“Background” Deaths on HAART
Percent of death that are non-AIDS Age 70 CD4 and age at initiation of HAART Useful because it gives us a baseline for expected non-HIV related deaths Age 60 Age 50 Age 40 Age 30 CD4 >350 CD CD CD4 <50 Braithwaite R, Am J Med 2005:

“Non-AIDS” Causes of Death Since ~2000
Source Non-AIDS% 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 Given the age of these groups, we would expect about 30-40% non-HIV related deaths. Because we see 60%, this suggests that the non-AIDS deaths aren’t independent of HIV. NY used death certificate data only

Causes of Comorbidity in HIV
Aging Treatment Toxicity HIV Progression Substance Use and Other Behaviors

Agenda Compare patterns of comorbid disease among HIV infected/uninfected controls Show that modifiable mediators of comorbidity differ by HIV status Show an approach to prioritizing care in complex diseases like HIV infection Describe plans for intervention

Veterans Aging Cohort Studies
Designed to explore attributable risk for and etiologies of non-AIDS conditions among veterans with and without HIV infection

VHA as Laboratory State-of-the-art health information system
Racially and ethnically diverse population Data-driven quality improvement culture Dedicated providers

Virtual Cohort (VC) Subjects Scope of Study
33,000 HIV-infected veterans 99,000 2:1 age-, race/ethnicity-, and VISN-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

VACS 8 8 Sites~7000 subjects HIV-infected subjects matched (1:1) to HIV-negative controls age, race/ethnicity, and site primary care pts. Baseline: 2002 Ongoing annual follow-up surveys Have data from multiple 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

VACS 8 Enrollment Sites 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. Have them stand up

Analyses ICD-9 Codes: Biomarkers 2 outpatient or 1 inpatient code
validated with chart review Biomarkers FIB4>3.25 for liver cirrhosis eGFR<30 for renal insufficiency BMI>30 for obesity

Virtual Cohort Non-AIDS Events
Medical Disease Substance Use Disorders Psychiatric Disorders At Least One From ICD9 codes HIV + have more substance use disorders Controls have more medical and psychiatric disease HIV is not countered. Primary care patients in for something, so we’re biasing towards finding more comorbidity in the HIV negative group. Goulet J., et al. Clin Infect Dis Dec15;45:

Virtual Cohort Triple Morbidity
From ICD 9 codes Have all three: substance use, medical and psychiatric. Hardest to manage Goulet J., et al. Clin Infect Dis Dec15;45:

Behavioral Risk Factors
Unpublished VACS 8 Data

Baseline Despite many being on treatment, they have less hyperlipidemia May be because they had low lipids before treatment and now it’s increasing but returning to health

HIV positive are thinner
May explain lipid differences But difference in body size is greater than difference in lipids, suggesting lipids may be higher for body size in HIV-positive

Metabolic and Vascular Disease

ICD9 codes

Composite measure but similar results when analyzed individually
Composite measure but similar results when analyzed individually. In no case did HIV+ have more than HIV- ICD9 codes for: myocardial infarction, stroke, transient ischemic attack and/or peripheral vascular disease

Lung Disease

VACS 5 Obstructive Lung Disease
Despite bias towards finding more disease in HIV-negative, we find more COLD in HIV+, even when comparing by pack years Age in Years Pack years of Smoking Crothers, et al. Chest, 2006

Liver Disease

VACS 8 Liver Fibrosis Alcohol – abuse/dependence based on ICD0
Viral Hepatitis – lab & ICD9 codes

Renal Disease

Work under review VACS 8

Cancer

SEER grouping Last group is all non-HIV cancers combined McGinnis KA, et al. Pre and post HAART cancer incidence among HIV positive veterans. XIV International AIDS Conference, Barcelona, Spain, July 7-12, 2002.

VC 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 :

VC Non-Hodgkin’s Lymphoma Standardized IRRs Comparing HIV-infected 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 :

Patterns Differ by HIV Status
HIV is associated with comorbidities of aging substance users COPD, Liver and Renal disease, HCV, Cancer HIV is not associated with conventional cardiovascular risks, other than smoking Lower risk of obesity, hyperlipidemia, diabetes, and hypertension Guidelines may need to be changed for HIV patients Need better control groups that are more appropriate comparisons to explore: ARV toxicity, immune senescence, alternative causes

VACS Results in Sum: HIV Infected Veterans Have
More prevalent: Liver disease Renal disease Pulmonary disease Intracranial hemorrhage Thrombosis Cancer Multi-morbidity All cause mortality Less prevalent (maybe): Obesity Diabetes Hyperlipidemia Hypertension Cardiovascular disease Adjusting for risk factors More prevalent CVD—especially among HCV+ Than Among Uninfected, Demographically Similar Controls.

How Can We Prioritize Care? An Index of Pathologic Injury for HIV
Which conditions drive outcomes?

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

Hypotheses Markers of injury will add substantially to the ART-CC model for predicting mortality.

Antiretroviral Treatment Cohort Collaboration (ART-CC) Model
13 cohorts starting combination ART 12,574 patients 344 deaths, 750 AIDS events Median follow up 1.75 years (longest 5.5 years) State-of-the-art methods Based on: CD4 cells (<50; 50-99; ; ; >350) HIV-1 viral load (<5 log or >5 log) Age > 50 years IV drug use Stratified by cohort and CD4 count

Steps to Develop an Index
Begin with established prognostic variables from ART-CC model Add indicators of injury Age Anemia Liver disease Renal disease HCV/HBV chronic infection

Methods: Virtual Cohort
HIV only 7516 started HAART between Jan 1, 1999 and Aug 1, 2002 6439 (86%) CD4, viral load, and hemoglobin levels First VA HAART regimen (CD4 and VL before treatment, started at least 3 drugs) – 70% truly HAART-naïve by chart-review Timeframe for when treatment fairly stable and providers comfortable. Stopped early enough to have longer-term follow-up for mortality

Analyses Use multivariate survival analyses to determine whether markers of pathophysiologic injury add to ART-CC Consider association of these markers to CD4 and AIDS-defining illnesses

Poisson Regression*: Overall Mortality (n=4813; 1241 deaths)
Variable IRR IRR 95% CI Age 50-64 1.46 1.29 1.65 Age ≥65 2.43 1.93 3.07 Hemoglobin <10 1.62 1.36 1.95 Hemoglobin 10-12 1.53 1.33 1.76 FIB-4 >3.25 1.55 1.31 1.84 FIB-4<1.45 0.75 0.65 0.85 eGFR<30 2.02 1.54 2.65 HCV or HBV 1.35 1.20 About 75% of original sample had available data. Looking into imputation methods. Age >65 substantially increased risk. ART-CC model only looked at >50 Moderate anemia – still a risk *Unpublished data; also adjusted for CD4 cell count, viral load, and AIDS conditions

Unpublished data C Statistics: ART-CC: 0.66 +Labs: 0.71
For each model: Calculate predicted mortality Rank and create deciles Then plotted ACTUAL mortality for those in that decile C-stat- goodness of fit – randomly select two pairs and it is the probability that the assigned risk order is consistent with the observed Unpublished data

What If We Omit All AIDS-associated Variables?
(CD4 cell count, HIV-1 viral load, and AIDS-defining conditions)

Observed Mortality Rates by Deciles of Risk
Deaths/100 PY C statistic still significantly better

Prediction Deciles and Median CD4 Count
CD4 Cell Count (median) per mm3 Deciles determined without CD4 and VL Yet, CD4 count goes progressively down as risk of death increases. Suggests that non-AIDS-related conditions are in part HIV or treatment related.

Major Contributors to Mortality Risk at HAART Initiation
Age CD4 cell counts Anemia Likely liver fibrosis Likely renal failure Viral hepatitis (beyond fibrosis and renal injury) AIDS Defining Conditions Viral load Ordered by strength of impact in model Yellow identifies those that VACS work newly demonstrates should be emphasized

Biomarker Index Can Be Used To
Predict mortality Identify potentially modifiable sources of morbidity/mortality As an intermediate outcome Adjust for overall severity of illness

First Target: Liver Injury
Intervention Plans First Target: Liver Injury

Approach Use observational data to design intervention
Characterize population Identify modifiable mediators of outcomes Study inter-relationships among mediators Inform power calculations Event rates Effect size Large multi-level, multi-modal, strategy trials Translational research Integrated into VA healthcare Built from evidence based components Focused on easily identified groups at increased risk Individually tailored Randomized at the clinic level

“Non AIDS” Causes of Death Since ~2000
Source % Not AIDS 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 AIDS 2006;43:27-34 Cascade Liver (20), Infections (24), Unintentional (33), Cancer (10), CVD (9) AIDS 2006; 20;741-9

Liver Disease & Causes of Death
Vascular Disease Cancer HCV and MI risk Hepatotoma Liver Disease

Markers of Liver Disease
Most never have liver biopsies Noninvasive tests include Ultra sound Elaborate bench assays Ratios of available tests (APRI & FIB-4) FIB 4 =age X AST /(platelets X Sqrt of ALT) >3.25 likely liver fibrosis/cirrhosis <1.45 unlikely liver fibrosis/cirrhosis <1.45: 89% Negative Predictive Value

Alcohol & Liver Disease
Percent FIB-4 >3.25 Lim et al, in preparation

Multiple, Overlapping Root Causes
Substance use Drugs, ALCOHOL Major cause of nonadherence Viral hepatitis Chronic Hepatitis C and B Medication toxicity Antiretrovirals (nevaripine, mitochondrially toxic D drugs) non-HIV medications HIV infection Chronic inflammation Immune compromise coupled with immune deregulation Liver Disease

Veterans at Risk of Liver Injury
Fib-4>1.45 Outside “protected range” 23% HIV- in VACS 42% HIV+ in VACS Rather than focusing on etiology, we focus on modifying clinical risk

The Intervention Saving lives by minimizing liver injury among HIV infected veterans in care Acronym anyone?

Combined Literature Interactive behavior change technology
Adaptive prevention Multiple health behavior change “Five As”: assess, advise, agree, assist, arrange Motivational interviewing Behavioral cognitive therapy Pharmacologic treatment

Key Concepts/Goals Concepts Goals No level of alcohol consumption safe
Early detection of risk of liver injury Intervention targeted at multiple root causes Local expert for pharmacologic treatment Long term maintenance critical Goals Screen for contributing causes Empower patient to “own” behavior and care Provide decision support to MD Coordination of new and existing services

Multi Modal Intervention
EMR to: Identify those at risk Alert for and coordinate existing care options Identify liver toxic medications Measure processes and outcomes Offer decision tools Coordinate care “Health Coach” to: Provide tailored risk education and personalized feedback Empower patient to ask for care Enhance and reflect patient’s motivations Reinforce importance of and problem solve around adherence Use biomarkers as feedback/motivator

Intervention Outline Subjects: HIV infected veterans with FIB-4>1.45 Providers in VACS HIV clinics to be randomized Data collection only, otherwise usual care Intervention Behavior Targets: Patient Any alcohol use ARV adherence below 95% Provider Chronic viral hepatitis treatment Liver toxic medications Primary outcomes: biomarker index and FIB 4 changes

Manualized Screens/Prioritization Directly Administered
Health Coach Manualized Care Summary Manualized Screens/Prioritization Treatments Assessment Issues Identified Directly Administered Gives MD Orders Risk Level of mortality risk Level of liver risk Risk module Activation module Risk echo life expectancy Irrelevant measures Biomarkers (Before Visit) Medication Reconciliation Liver Toxic Meds OTC, Non VA, & VA Need Refills OTC module VA, non VA toxic Med alert, Refill alert Alcohol Any Hazardous Abuse or dependence Alcohol module Alcohol echo Treatment referral Viral Hepatitis Not completely tested Active HCV, not Rxd Active HBV, not Rxd HCV module HBV module HCV referral Rx. recommendation HCV,HBV tests Vaccinations ARV Adherence Homelessness Side Effects Poor Adherence ARV Side Effects Adherence module Adherence echo Side effects alert Social work referral Depression Depressive Symptoms/ Suicidal Ideation None SSRI or mental health Ref. recommendation Drugs Active Illicit /Prescription Drug Abuse Drugs alert Tobacco Tobacco Use Smoking alert

Health Coach Patient Contacts
First Contact Tailored risk assessment/interpretation/advice Means of decreasing risk (adherence, alcohol, Hepatitis Rx) Targeted advice re discussion with provider Medication reconciliation Case Management Vaccinate for hepatitis A or B if negative and not previously done 3 additional face to face treatments first month Update medication reconciliation, Pill counts Barrier identification and problem solving Review, encourage, action plan FU phone/ /website/chat room at monthly, more if needed As requested by patient additional face to face Final treatment visit at 6 months face to face Updated 5As with new behavior, biomarker, and medication data Support phone/ /website/chat room, by request, throughout

Alcohol Pharmacotherapy
Screened for hazardous or worse alcohol Referral recommended to provider Patient activated to request/accept referral Trained alcohol pharmacotherapy doctor Can use: Naltrexone, Topiramate, or Disulfiram (Acomprosate nonformulary) Manages and titrates medications Monitors toxicity and outcomes May choose to refer to substance use clinic

Provider Contacts Orientation/education re liver injury
Initial encounter Risk assessment Very specific recommendations for Non-action (irrelevant performance measures) Action (echo, treat, refer) Medication reconciliation with liver summary (all sources) Patient receptivity/ requests Subsequent contacts Alert if risk changes Alert if new liver toxic medication Additional visits if patient requests them

MD Relief Medication reconciliation Performance measure resolution
Alcohol Depression Tobacco (partial) Turn off screens that don’t apply Colon cancer

MD Advice Requests Liver risk—reduction
OTC toxic medications—avoidance Any alcohol use—abstinence Not adherent—improvement

MD Medication Requests
SSRIs (or referral) If depressed Pick ARV active against HBV If HBV+ Consider discontinuation of toxic medications Treat side effects of ARVs If nonadherent

MD Referral Requests Alcohol pharmacotherapy Hepatology (HCV Clinic)
If dependent Hepatology (HCV Clinic) If patient willing If no absolute contraindications to treatment Mental health (or SSRI Rx) If depressed

Graded Alcohol Treatment
All receive Risk assessment, medication reconciliation Patient activation Advice to quit drinking (health coach and MD) Evaluation for viral hepatitis Evaluation of ARV adherence Hazardous and above drinkers receive Request for pharmacologic treatment May also be referred to substance use clinic If referred for pharmacologic treatment Selection of medications Graded dosing Continued monitoring

Characteristics of Our Target Study Sample

VACS HIV+ Demographics
FIB-4 >1.45 <1.45 Proportion of all HIV+ 42.3% 57.7% Age (years) 52.3% 46.1% Female 1.6% 3.4% Black 69.1% 65.0% Hispanic 8.9% 9.5% White 18.7% 21.6%

VACS HIV+ Substance Use (last 12 months)
FIB-4 >1.45 <1.45 Binge weekly or more 18.0% 15.0% Abuse or Dependence Alc. 29.7% 17.2% Hazardous Alcohol 52.6% 36.7% Any Current Alcohol Cocaine 26.8% 20.9% Opioids 12.2% 4.6% IV Drugs 11.0% 5.1%

VACS HIV+ Adherence, Hepatitis, and Liver Toxic Medications
FIB-4 >1.45 <1.45 ARV Adherence (<95%) 51.2% 53.2% HCV (treatment) 66.3 (4.1)% 47.4 (4.3)% HBV (treatment) 8.8 (51.5)% 3.5 (0)% Acetominophen (opioid) 8.1% 10.6% Sulfa drugs 26.2% 1.4% Other Antibiotics 6.8% 6.2% Statins 18.4% 9.6% Epileptic Drugs 2.7% 1.9% TB Drugs 0.6% 0.8% Any L. toxic medication 47.9% 42.6%

VACS Outcomes FIB-4 >1.45 <1.45 12 month mortality (%) 5.2 1.8
SF12 PCS Score (out of 100) 42.1 44.2 End Stage Liver Disease 7.1% 0.9%

Timeline This fall/winter Next spring/summer Next fall
Estimate expected effect size using VACS longitudinal data Pilots of Naltrexone, Adherence, Life expectancy CDA application for adherence intervention Work on computer automation of study components LOI to Cooperative Studies/possibly other funders as well Next spring/summer Analyze, report results from pilots Launch computerized surveys in VACS full study Finalize primary endpoint, sites, subjects, and time horizon Next fall Launch 6 month pilot of full trial

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.

Targeting Clinical Intervention Among Veterans with HIV Infection: Adherence, Alcohol, and Non-ARV Treatment Toxicity University of Pennsylvania Center.

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