1. PCORI Application Simplify Trial
Two for One
PCORI Application
Simplify Trial

2. Nathan Hale Network: A Research Collaboration Between YNHHS and Veterans Healthcare Administration
In response to a funding announcement from: The Patient Centered Outcomes Research Institute for Clinical Data Research Networks

3. PCORI’s Mission and Vision
The Patient-Centered Outcomes Research Institute (PCORI) is an independent, non-profit health research organization authorized by the Patient Protection and Affordable Care Act of 2010.
PCORI funds patient-centered research to assist patients, caregivers, and other stakeholders in
making informed health decisions.
Mission
PCORI helps people make informed healthcare decisions and improves healthcare delivery and outcomes by producing and promoting high integrity, evidence-based information that comes from research guided by patients, caregivers, and the broader healthcare community.
Vision
Patients and the public have the information they need to make decisions that reflect their desired health outcomes.

4. National Patient-Centered Clinical Research Network
The goal of PCORI’s National Patient-Centered Clinical Research Network Program is to improve the nation’s capacity to conduct CER efficiently, by creating a large, highly representative, national patient-centered clinical research network for conducting clinical outcomes research.
The vision is to support a learning US healthcare system, which would allow for large-scale research to be conducted with enhanced accuracy and efficiency.

5. National Patient-Centered Clinical Research Network
The core components of this network will be:
Clinical Data Research Networks (CDRNs), which are system-based networks including electronic health records (EHR) data and covering the entire clinical experience of defined populations, and
Patient-Powered Research Networks (PPRNs), which are groups of patients interested in forming a research network and in participating in research.
Specifically, this program will promote:
A more comprehensive, complete, longitudinal data infrastructure;
Broader participation of patients, clinicians, health systems, and payers in governance and use of the network, and
Data inter-operability between networks to facilitate data sharing as part of research projects.

6. National Patient-Centered Clinical Research Network: Our Vision
Steering Committee
Scientific Advisory Board
Awardees
PCORI
AHRQ, NIH, FDA, ONC, CMS
Special Expert Group
Coordinating Center Staff

7. Ideal CDRN Characteristics: Systems
Involvement of multiple (two or more) health systems, working toward data standardization and interoperability within the network and across networks to allow for efficient, valid sharing of individual or aggregate data for purposes of data analysis.
Involvement of the healthcare system leadership in governance and use of the network to enhance network efficiency, utility, and identification of models for sustainability of the network.
Capacity and willingness to support large-scale randomized trials, as well as observational comparative effectiveness studies, with substantive patient and clinician involvement in the governance and use of the network.

8. Ideal CDRN Characteristics: Patients
Coverage of a large, diverse, defined population not selected for a particular disease, condition, or procedure; ability to capture complete clinical information on this population over time, including longitudinal information on clinical care, changes in clinical characteristics and conditions, and the occurrence of clinical care or outcomes, within or outside the system.
The ability to efficiently contact patients for the purposes of recruitment; collecting patient- reported information; and maintaining consistently high levels of participation in research studies.
Demonstrated ability to engage substantial patient populations with selected conditions, both within and outside their systems, for purposes of generating research questions, participating in network governance, or in appropriate research studies.

9. Clinical Data Research Networks (CDRN)
$56 million is available to support up to 8 new or existing CDRNs for 18 months.
COOPERATIVE AGREEEMENT AWARD
18 MONTHS LATER
At least two health care systems engaged.
Willingness and capacity to work toward
data standardization with other awardees.
Willingness to participate in collaborative studies with data sharing as part of a national research infrastructure.
> 1,000,000 patients enrolled.
Data standardized within network and with other awardee networks.
Patients, system, and clinicians engaged in
governance & use.
Capable of implementing clinical trials.

10. What We Proposed: Nathan Hale Network
Yale New Haven Health System
750,000 engaged patients
225,000 cohort eligible
4 sites for participation
National VA
5.9 million engaged patients
1.8 million cohort eligible
12 VA sites consented for surveys, planning, and trials

11. Nathan Hale Idea

12. How Will It Start? 3 observational cohorts:
Obesity (required): BMI>30
HIV (already have a large VA cohort)
Glioma (existing VA and YNHHS cohorts)
Each cohort has two components:
Waiver cohort identified electronically
Nested survey cohort consented and enrolled

13. Cohort Design

14. Nathan Hale Organizational Chart

15. How Can The Community Participate?
Give us feedback on current plans
Get the word out to those who might be interested
Volunteer yourself:
For the Community Advisory Boards
If eligible, participate in study

16. Community Advisory Boards
Meet monthly for about an hour
Elect representatives for steering committee
Interact with providers and investigators
Training available
Give feedback on study design
Consent
Surveys
(in future) Intervention studies

17. How Can We Enrich Discussion/Participation?
Natasha Ray and Karen Wang
Working relationship on community engagement
Will attend CABS and Exec Meetings to facilitate increasing community engagement
Development/training:
Georgina Lucas, Director RWJFCSP Committee On Community Research Projects
Session on managing committees for CAB leaders
Margaret Grey, Director Yale CTSA Translational Research Core
Training on understanding/applying research

18. What Groups Have We Contacted?
VA, Yale, YNHHS, CT
Veterans of Foreign Wars
Disabled Veterans
American Legion
Cooperative Studies Informed Consent/Privacy
Yale RWJFCSP Steering Committee on Community Research Projects
YNHHS Patient and Family Advisory Group
Community Foundation for Greater New Haven
Cohort Specific
Ryan White Consumer Subcommittee (HIV)
Center for Interdisciplinary Research on HIV/AIDS (HIV)
Connecticut Brain Tumor Alliance (Glioma)
Obesity Action Coalition (Obesity)

20. THE SIMPLIFY TRIAL A Proposal to VA Cooperative Studies
Medication Simplification Based on Evidence, Cardiovascular Risk, and Preferences
THE SIMPLIFY TRIAL
A Proposal to VA Cooperative Studies

21. Polypharmacy Typically defined as >5 chronic drugs
Associated with diminished marginal benefit from additional medication due to:
Non adherence
Drug-drug interactions
Cumulative toxicity
Risk of adverse events increases approximately 10% with each additional medication
Salazar JA. Expert Opin Drug Saf (2007) 6(6):
Gandhi TK. N Engl J Med 2003;348:

22. Chronic Medication Count by Age and HIV Status (VACS)
LIKELY AN UNDERESTIMATE
Looking at medication counts by age – age on x-axis here, medication count on y-axis we see that within each age grouping, HIV+ oare cnsistely on more medications and exposed to polypharmacy at younger ages.
Edelman EJ et al. IDSA [oral], San Francisco, California, October 2-6, 2013.

23. Age, Polypharmacy and Risk of Drug Interactions with ARVs (U of Toronto)
Pts in clinic since 2010, on ARVs as of 2012
Drug interactions at recent visit prior to Jan
Chart review with medications verified after visit
Prescribed
Over the counter and CAM
71% were 50+ years
Mean medication count was 9 (7, <50 yrs)
71% had 1+ potential drug-drug interaction (55%, <50 yrs.)
Cardiovascular medications a driver of polypharmacy
Tseng A. et al. Association of Age with Polypharmacy and Risk of Drug Interactions with Antiretroviral Medications in HIV Positive Patients. Annals of Pharmacy 2013:47 (11);

24. Medication Count and Mortality (VACS)
Seven or more medications is associated with an increased risk of mortality after adjusting for HIV status and disease severity.
Currently working on propensity adjusted models to more accurately estimate magnitude of association.
We then wanted to examine the association between individual medication counts and mortality. After adjusting for HIV status, demographics, VACS index score and comorbid conditions, we found that. . .
*Note: reference is 3 medications
Edelman EJ et al. IDSA [oral], San Francisco, California, October 2-6, 2013.

25. Polypharmacy in HIV
In the context of aging and multimorbidity, individual prioritization of care is essential
Prioritization should consider
Risk of short term mortality
Maximizing what ever most important to patient
Minimizing risk of causing harm
More medication, even if suggested by care guidelines, may cause harm

26. How Can We Address Polypharmacy?
Trials to date used pre-set criteria
Beers, START/STOPP, Med. Appropriateness Index
Result in medications being started as well as stopped
Improves “appropriateness”, do not decrease count
Do not achieve separation of treatment arms
This approach does not attempt to prioritize medications by
Magnitude of benefit (for risk modification)
Patient preferences (for symptom modification)

27. Cardiovascular Disease Prevention a Major Driver of Polypharmacy
Every male 45+ years of age is “suppose” to be on at least one cardio-preventative medication (aspirin).

28. Myocardial Infarction in HIV+/-
Premature aging? N
# of events
Mean age
HIV-
56,456
286
55.3
HIV+
27,988
231
0.0 years crude
difference
Adjusted mean difference in age:
-0.04 (-0.62, 0.54) years
No difference in age at diagnosis by HIV status
Greater risk? 10
HIV HIV+
10.00
IR per 1,000 py
95% CI
aIRR
HIV-
1.31
(1.17, 1.47)
1.00
HIV+
2.18
(1.92, 2.48)
1.81
(1.49, 2.20)
8.00
6.00
4.00
2.00
An 81% increase in the rate in
HIV+ compared to HIV-
0.00
Linear regression models to estimate the mean difference in age at diagnosis and Poisson regression models to estimate incidence
rate ratios (aIRR) were adjusted for age, race, sex, body mass index, alcohol use, cigarette smoking, hepatitis C infection, anemia,
diabetes, hyperlipidemia, lipid-lowering medications, hypertension, anti-hypertension medications, and statin use.

29. Non-ART Medications by Age
Age-group (Yrs)
Medication
Total n (%)
<50
50-64
>65 P1
Antihypertensives
(not ACE inhibitors)
831 (9.8)
323 (5.6)
367 (16.4)
141 (31.3)
<0.001
(ACE inhibitors)
935 (11.1)
355 (6.2)
432 (19.4)
148 (32.9)
Lipid-lowering agents
1071 (12.7)
356 (6.2)
527 (23.6)
188 (41.8)
Oral antidiabetics
179 (2.1)
51 (0.9)
87 (3.9)
41 (9.1)
Insulin
116 (1.4)
40 (0.7)
50 (2.2)
26 (5.8)
Antiplatelet drugs
488 (5.8)
121 (2.1)
237 (10.6)
130 (28.9)
Antidepressants
846 (10.0)
560 (9.7)
251 (11.2)
35 (7.8)
0.659
1 Test for trend across age-groups
Abbreviations: ART, antiretroviral therapy; ACE, Angiotensin converting enzyme
Swiss
Cohort
Study
H I V
Hasse B. et al. Morbidity and Aging in HIV-Infected Persons: The Swiss HIV Cohort Study CID :

30. Common Prescription Medication Classes HIV+/-
Edelman EJ et al. IDSA [oral], San Francisco, California, October 2-6, 2013.

31. Are Cardiovascular Medications as Effective Among HIV+?
Fewer HIV+ on cardio-protective medications
Among those started on diabetes and lipid medications, fewer achieve goals
Mechanism may be more predominantly inflammation than among uninfected

32. Can Cardiovascular Medications Harm?
Anti hypertensives: Increased risk of falls—especially among those with borderline indications
Lipid lowering agents: liver and muscle toxicity
Drug-drug interactions with ARVs
Confusion leading to non adherenece to ARVs
Adds to polypharmacy

33. Adverse Drug Events Associated with Common Medications (General Population)
Hamilton H. et al. Potentially Inapprorpiate Medications Defined by STOPP Criteria and the Risk of Adverse Drug Events in Older Hospitalized Patients Arch Intern Med 2011;171(11):

34. Age, Polypharmacy and Risk of Drug Interactions with ARVs (U of Toronto)
Pts in clinic since 2010, on ARVs as of 2012
Drug interactions at recent visit prior to Jan
Chart review with medications verified after visit
Prescribed
Over the counter and CAM
71% were 50+ years
Mean medication count was 9 (7, <50 yrs)
71% had 1+ potential drug-drug interaction (55%, <50 yrs.)
Cardiovascular medications a driver of polypharmacy
Tseng A. et al. Association of Age with Polypharmacy and Risk of Drug Interactions with Antiretroviral Medications in HIV Positive Patients. Annals of Pharmacy 2013:47 (11);

35. Equipoise? HIV+ individuals on ART are both
At increased risk from polypharmacy
At increased risk of cardiovascular disease
Not clear whether additional cardio-protective medications will do more benefit than harm
Evidence suggests answer depends upon:
Life expectancy
Other risk factors for cardiovascular disease
Susceptibility to harm from additional medication
Current medication count
Physiologic frailty

36. Objective
Test whether a personalized strategy of prioritizing and reducing medications results in fewer hospitalizations and deaths without excess cardiovascular events, compared to guideline driven cardio-protective care among HIV infected individuals

37. Intersection of Three Goals
Decreasing Polypharmacy
Preference Based Personalized Care
Balanced Approach to Cardiovascular Risk

38. Randomized Trial Proposal: Simplify

39. ARMS
SIMPLIFY
SAME or INTENSIFY
Screening/ Consent
Medication Reconciliation (phone/in person) generates a complete list of medications (OTC, herbal, VA and non-VA prescribed). Also determine eligibility for CVD prevention (ASA, antihypertensive, DM treatment, or lipid lowering agents). Patient asked to come in an hour early for next scheduled clinic appointment.
Manualized Intervention (Day of Visit Prior to MD Encounter)
Personalized discussion of:
Individualized risk from: polypharmacy & CVD
Adherence to each medication
Perceived side effects, and preference for taking/not taking
Perceived benefits from each medication
Initial automated prioritization of eligible CVD meds and current medications to treat/prevent disease (tailored to demographics, comorbidities, VACS Index, and level of polypharmacy)
Alternatives to symptom/pain medications discussed (e.g., diet, sleep hygiene, exercise, etc)
Generic discussion of:
Risk of CVD in HIV
Automated guideline (asa, htn, lipids, dm)
Guideline based behavioral advice (diet, smoking, exercise)

40. ARMS (cont’d)
SIMPLIFY
SAME or INTENSIFY
Joint prioritization of meds with patient in light of discussion, aim to cut total med count by 2+ over the three sessions (if medications added need to cut more); Give patient written recommendations to give to provider (also forward electronically)
Recommend 2+ new CVD medications or intensification over the three sessions, if not already on all eligible; Give patient written recommendations to give to provider (also forward electronically)
At MD Visit
Provider reviews recommendations and approves/denies orders
4 & 8 week Visits
Abbreviated review of prior steps additional medications may be stopped
Abbreviated review of prior steps, CVD meds may be added or intensified
years of observation

41. How is This Possible? Preventive Care Markov Model adapted to
HIV and organ system disease using VACS Index
Account for patient preferences
VA Electronic Medical Record System
Initial list of active medications
Fill/refill adherence
Decision support facilitated preference elicitations
Taksler GB et al. Personalized Estimates of benefit from Preventive Care Guidelines: A Proof of Concept. Ann Intern Med 2013;159:

42. VA Cooperative Studies: Examples
Rheumatoid Arthritis Comparison of Active Therapies (RACAT, published NEJM 2013): enrolled 353 participants at 16 VA facilities, 12 Rheumatoid Arthritis Investigational Network sites, and 8 Canadian medical centers who met criteria for active rheumatoid arthritis and had been receiving methotrexate at stable doses and randomly assigned a triple regimen of disease-modifying drugs (methotrexate, sulfasalazine, and hydroxychloroquine) or methotrexate and etanercept (a biologic agent). Triple therapy was non inferior to etanercept.
Prostate Cancer Intervention versus Observation Trials (PIVOT, published NEJM 2012, referenced 105 times): screened 13,022 men with prostate cancer and found 5,023 eligible, 731 underwent randomization to radical prostatectomy or observation and were followed a median of 10 years at 44 VA facilities and 8 NCI sites. Radical prostatectomy did not reduce all-cause or prostate cancer mortality compared with observation. Adverse events occurred within 30 days after surgery in 21.4% of men randomized to surgery. This study changed clinical practice guidelines.
Clinical Outcomes utilizing Revascularization and Aggressive Drug Evaluation (COURAGE, published NEJM 2007, referenced 1,179 times): screened 35,539 patients and randomized 2287 patients at 50 centers (42% at US VA facilities, 17% at US non VA facilities, and 41% at Canadian facilities). Patients with stable coronary artery disease randomized to undergo PCI and medical therapy or medical therapy alone. PCI did not reduce risk of death, myocardial infarction, or other major cardiovascular events. This study was prominently cited in the Federal Council on Comparative Effectiveness Report].

43. How Much Will This Cost? Sample size 1330, ~111 from each of 12 sites
3 years of recruitment, 5 year total study time
Total estimated costs: $19.9 million
About average for VA Cooperative Studies Trial

44. Acknowledgements Consortium PI : AC Justice*
Scientific Collaborator (NIAAA): K Bryant
Affiliated PIs: S Braithwaite, K Crothers*, R Dubrow *, DA Fiellin*, M Freiberg*, V LoRe*
Participating VA Medical Centers: Atlanta (D. Rimland*, V Marconi), Baltimore (M Sajadi, R Titanji), Bronx (S Brown, Y Ponomarenko), Dallas (R Bedimo), 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, K Kraemer, M Freiberg, E Hoffman), and Washington DC (C Gibert, R Peck)
Core and Workgroup Chairs: C Brandt, J Edelman, N Gandhi, J Lim, K McGinnis, KA Oursler, C Parikh, J Tate, E Wang, J Womack
Staff: H Bathulapalli, T Bohan, J Ciarleglio, A Consorte, P Cunningham, L Erickson, C Frank, K Gordon, J Huston, F Kidwai-Khan, G Koerbel, F Levin, L Piscitelli, C Rogina, S Shahrir, M Skanderson
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
Cross Cohort Collaborators: Richard Moore (NA-ACCORD), Jonathan Sterne (ART-CC), Brian Agan (DoD)
Major Funding by: National Institutes of Health: AHRQ (R01-HS018372), NIAAA (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799, U24-AA022001, U24 AA022007), NHLBI (R01-HL095136; R01-HL090342) , NIAID (U01-A ), NIMH (P30-MH062294), NIDA (R01DA035616), NCI (R01 CA173754) and the Veterans Health Administration Office of Research and Development (VA REA , VA IRR Merit Award) and Office of Academic Affiliations (Medical Informatics Fellowship)
*Indicates individual is also the Chair of a Core or Workgroup

45. Acknowledgements Continued
COMpAAAS/Veterans Aging Cohort Study, a CHAART Cooperative Agreement, supported by the National Institutes of Health: National Institute on Alcohol Abuse and Alcoholism (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799) and in kind by the US Department of Veterans Affairs.  In addition to grant support from NIAAA, we gratefully acknowledge the scientific contributions of Dr. Kendall Bryant, our scientific collaborator.
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