PCORnet Coordinating Center Site Visit:
GPC
March 19, 2015
Call-in:1 (571) 317-3122 Access code: 863-993-413
Agenda
Welcome & Introductions
Vision and Goals for PCORnet
DSSNI Discussion
 Sentinel-PCORnet workgroup
 Data Characterization
 Complete Data
 Common Data Model-Updates and Maintenance
IRB and ADAPTABLE
Use Cases: Health Systems Demonstration Project & Obesity
Complete Data Demonstration
Open Session for Questions and Wrap Up
GPC Global Call
2
Welcome & Introductions
Vision and Goals for PCORnet
Rich Platt, Adrian Hernandez
PCORnet Opportunities and Challenges
Rich Platt
PCORnet: the National Patient-Centered Clinical
Research Network
PCORnet’s goal 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.
6
Guiding principle: Make research easier
Analysis ready data
Reusable analysis tools
Administrative simplicity
Simple, pragmatic studies integrated into routine care
Multiple Networks Sharing Infrastructure
Health
Plan 1
Health
Plan 4
Health
Plan 7
Hospital 1
Hospital 4
Outpatient
clinic 1
Patient
network 1
Health
Plan 2
Health
Plan 5
Health
Plan 8
Hospital 2
Hospital 5
Outpatient
clinic 2
Patient
network 2
Health
Plan 3
Health
Plan 6
Health
Plan 9
Hospital 3
Hospital 6
Outpatient
clinic 3
Patient
network 3
Each organization can participate in multiple networks
Each network controls its governance and coordination
Other networks can participate
Networks share infrastructure, data curation, analytics, lessons,
security, software development
Multiple Networks Sharing Infrastructure
Health
Plan 1
Health
Plan 4
Health
Plan 7
Hospital 1
Hospital 4
Outpatient
clinic 1
Patient
network 1
Health
Plan 2
Health
Plan 5
Health
Plan 8
Hospital 2
Hospital 5
Outpatient
clinic 2
Patient
network 2
Health
Plan 3
Health
Plan 6
Health
Plan 9
Hospital 3
Hospital 6
Outpatient
clinic 3
Patient
network 3
Each organization can participate in multiple networks
Each network controls its governance and coordination
Other networks can participate
Networks share infrastructure, data curation, analytics, lessons,
security, software development
PCORnet Common Data Model, Draft v2.0 Modifications
DEMOGRAPHIC
PATID
BIRTH_DATE
BIRTH_TIME
SEX
HISPANIC
RACE
BIOBANK_FLAG
Fundamental basis
ENROLLMENT
PATID
ENR_START_DATE
ENR_END_DATE
CHART
ENR_BASIS
DISPENSING
PATID
RX_DATE
NDC
RX_SUP
RX_AMT
Data captured from processes
associated with healthcare delivery
VITAL
PATID
ENCOUNTERID (optional)
MEASURE_DATE
MEASURE_TIME
VITAL_SOURCE
HT
WT
DIASTOLIC
SYSTOLIC
ORIGINAL_BMI
BP_POSITION
TOBACCO
TOBACCO_TYPE
CONDITION
PATID
ENCOUNTERID (optional)
REPORT_DATE
RESOLVE_DATE
CONDITION_STATUS
CONDITION
CONDITION_TYPE
CONDITION_SOURCE
PRO_CM
PATID
ENCOUNTERID (optional)
CM_ITEM
CM_LOINC
CM_DATE
CM_TIME
CM_RESPONSE
CM_METHOD
CM_MODE
CM_CAT
Data captured within multiple
contexts: healthcare delivery,
registry activity,
or directly from patients
ENCOUNTER
PATID
ENCOUNTERID
SITEID
ADMIT_DATE
ADMIT_TIME
DISCHARGE_DATE
DISCHARGE_TIME
PROVIDERID
FACILITY_LOCATION
ENC_TYPE
FACILITYID
DISCHARGE_DISPOSITION
DISCHARGE_STATUS
DRG
DRG_TYPE
ADMITTING_SOURCE
DIAGNOSIS
PATID
ENCOUNTERID
ENC_TYPE (replicated)
ADMIT_DATE (replicated)
PROVIDERID (replicated)
DX
DX_TYPE
DX_SOURCE
PDX
LAB_CM_RESULT
PATID
ENCOUNTERID (optional)
LAB_NAME
SPECIMEN_SOURCE
LAB_LOINC
STAT
RESULT_LOC
LAB_PX
LAB_PX_TYPE
LAB_ORDER_DATE
SPECIMEN_DATE
vv
SPECIMEN_TIME
RESULT_DATE
RESULT_TIME
RESULT_QUAL
RESULT_NUM
RESULT_MODIFIER
RESULT_UNIT
NORM_RANGE_LOW
MODIFIER_LOW
NORM_RANGE_HIGH
MODIFIER_HIGH
ABN_IND
PROCEDURE
PATID
ENCOUNTERID
ENC_TYPE (replicated)
ADMIT_DATE (replicated)
PROVIDERID (replicated)
PX_DATE
PX
PX_TYPE
Data captured from healthcare delivery, direct encounter basis
New
New to
to v2.0
v2.0
PCORnet DRN Coordinating Center
1
6
1. User creates and
submits query (a
computer program)
PCORnet Secure Network Portal
CDRN 1
Review &
Run Query
2
3
Review &
Return Results
Demographics
Utilization
Etc
4
3
Review &
Return Results
Demographics
Utilization
Etc
3. CDRNs/PPRNs
review and run query
against their local data
4. CDRNs/PPRNs
review results
CDRN 11
Review &
Run Query
5
2. Individual
CDRNs/PPRNs
retrieve query
4
5. CDRNs/PPRNs
return results via
secure network
6. Results are
aggregated
11
Used data for 3.9 million new users of antihypertensives in 18 organizations
Propensity score matched stratified analysis
No person-level data was shared
Five months and $250,000 required for programming
and analysis – compared to 1-2 years and $2 million
without analysis-ready distributed dataset
Toh Arch Intern Med.2012;172:1582-1589.
Reusable Program: Propensity Score
Matched Cohort Study
Specify:
Population (age/sex/etc), time period
Exposures
Outcomes
 ICD-9-CM code 995.1 in any position during outpatient,
inpatient, or emergency department encounter
 Washout period (days before first dispensing): 183
days
Inclusion criteria
Exclusion criteria
Covariates
Propensity score matching options
 Comorbidity, utilization, high dimensional propensity
score
 Matching ratio
 Caliper size
Angioedema: Table 1. Unmatched Cohort
3.9 million new users
Diabetes
21% vs 10%
Heart failure
2% vs 4%
Ischemic heart disease 5% vs 13%
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf
Propensity Scores Before Match
DP3
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf
Angioedema: Table 2. Matched Cohort
2.6 million new users
Diabetes
10% vs 10%
Heart failure
3% vs 3%
Ischemic heart disease 8% vs 8%
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf
Propensity Scores After Match
DP3
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf
Angioedema: Table 3. Results
ACEI vs βblocker 1:1
matched
analysis:
• HR = 3.1
(95% CI, 2.93.4)
Toh et al
findings:
• HR = 3.0
(95% CI, 2.83.3)
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf
Trial Logistics:
Taking Advantage
of PCORnet Infrastructure
19
Screening, Enrollment & Data Flow
20
Computable
phenotype
History of CAD
• Past MI
OR
• Past cath showing
significant CAD
OR
• Revascularization
(PCI/CABG)
At least one
of the following:
• age > 65 years
• Creatinine > 1.5
• Diabetes,
• Known 3 vessel coronary artery
disease
• Current cerebrovascular
disease and/or peripheral artery
disease
• Known ejection fraction <50%,
• Current smoker
Getting consent
21
Getting Informed Consent
Clinician reviews and
decides on participation
Email to potential patient with trial
introduction and link to consent
Letter to potential pt. with trial intro and
paper consent for non-Internet accessible
pt.
Consent Form Contacts:
Local contact info for any site issues
Local contact info for withdrawal from trial
Contact info for questions about the trial
Contact info for reporting adverse events
Randomization & ASA dose assignment
PCORnet’s opportunities
Be a national/regional resource to answer questions important to
patients, clinicians, and delivery system leaders (be a foundation
of the Learning Health System)
 Researchers embedded in clinical environments, who are able to
 Engage patients, providers, health plan leaders, and
 Use both EHR and claims data when needed
Data
 Develop validated data domains/elements for national use
 Use, publish standard analytic tools for the CDM
Trials
 Develop efficient methods for pragmatic, multi-center trials
Methods
 Create novel analytical tools, e.g., privacy preserving regression
Our Challenges
Funders will increasingly expect multi-site studies to be better,
faster, and cheaper than our investigators are accustomed to
 Using data that comes directly from delivery systems and
from patients
We need to develop trust and systems to allow PCORnet-wide
coordination to guide external investigators and funders,
manage projects, and implement efficiencies
DSSNI Discussion
Rich Platt, DSSNI Team: Jeff Brown, Lesley Curtis, and Jessica
Sturtevant
Mini-Sentinel/PCORnet Data Linkage
Workgroup
Rich Platt
26
About the Workgroup
Workgroup Lead: Rich Platt
Project Period: January – August 2015
Meetings held first and third Fridays of the month at 9am ET
 Next meeting: April 3, 2015
Deliverable: White paper
 Lead author: Kevin Haynes
 Address governance and technical aspects of several data
linkage scenarios
 Provide guidance for implementation of select scenarios
27
Scenarios for potential collaboration
Scenario 0
Characterize within network and outside network utilization for
each CDRN by providing CDRN NPI/TIN information to MiniSentinel Data Partners with which they have meaningful
overlap.
28
Scenarios for potential collaboration
Scenario 1
Create a list of overlapping populations between networks and
health plans.
 Options include a master list of all individuals in common
between two networks, or lists could be created as needed.
29
Scenarios for potential collaboration
Scenario 2
Identify outcomes of interest in Mini-Sentinel Data Partners’ data for specific
cohorts of people in the PCORnet networks.
 Example with informed consent: A Mini-Sentinel data partner
identifies hospitalizations for acute myocardial infarction or
GI bleeding among PCORnet network's patients who are
participating in a randomized trial of different aspirin doses.
 Example without informed consent: A PCORnet Clinical Data
Research Network shares INR values for Mini-Sentinel
health plan individuals exposed to different anticoagulants.
30
Scenarios for potential collaboration
Scenario 3
Create a merged dataset tailored to address a single question.
 Examples: the PCORnet randomized trial on aspirin dosing
and the observational studies based on the PCORnet weight
cohorts
• Identify outcomes of different kinds of bariatric surgery.
– CDRNs and health plans create a merged longitudinal
dataset for their shared patient-members who had
bariatric surgery at a CDRN site. The dataset would
include all of the data from both organizations that are
in the Common Data Model, for the entire period
during which the individual was a member of the
health plan. Both organizations that contribute data to
the merged dataset would need to approve each use
of the data.
31
Scenarios for potential collaboration
Scenario 4
Create a multipurpose merged dataset capable of rapidly answering many
questions
 Example: CDRNs and Mini-Sentinel Data Partners create a
merged longitudinal dataset for their shared patient-members. The
dataset would include all of the data from both organizations that
are in the Common Data Model. The data set would be used for
rapid querying using modular programs. Both organizations that
contribute data to the merged dataset would need to approve each
use of the data.
Scenario 4B
Create a merged dataset for a cohort of individuals with a condition or
treatment of interest, e.g., diabetes, heart failure, hip arthroplasty. This
dataset would be suitable for addressing a variety of questions about
the population or condition of interest.
32
Scenarios for potential collaboration
Scenario 5
Transfer all the data about a PPRN participant from a Sentinel
Data Partner to the PPRN – with the individual’s
request/authorization. Data would be in Common Data Model
format.
33
Timeline
Deliverable
Due Date
First draft of Scenario 0 ready for workgroup discussion
March 27, 2015
First drafts of 1, 2, 3, 4, 4B, and 5 ready for discussion at two week intervals
April 10, 2015 – June 19, 2015
Second drafts ready for review within one month of initial discussion
May 11, 2015 – July 20, 2015
Final scenario report completed 3 weeks after second draft is released
June 1, 2015 – August 10, 2015
Final white paper submitted to PCORI and FDA
August 24, 2015
34
PCORnet Data Characterization Overview
Jeff Brown and Lesley Curtis
Data Characterization: Purpose and Process
Ensure consistency with PCORnet Common Data Model
(CDM)
Identify major data gaps or issues for discussion
ETL Annotated Dictionaries describe how each data table
was created and identifies local issues
Data characterization queries will include basic checks of
each data domain and data element in the PCORnet CDM
ETL ADDs and data characterization query output will be
reviewed by the DSSNI Team and PCORI
36
Data Characterization: Tools and Resources
ETL Annotated Data Dictionary – description of
mapping and data transformation from source data
Functional Specifications – summary of the data
characterization approach and process
Technical Specifications – detailed descriptions of
the data characterization design and output, to inform
SQL code development
Work Plans – outline data characterization query and
output
37
Data Characterization Cycle
Create
DataMart and
ETL ADD
DSSNI
approves
Datamart
for querying
DSSNI sends
DC queries
DSSNI and
CDRN site
discuss DC
results
DC query
output to
DSSNI
DSSNI
review;
summary to
PCORI
38
Data Characterization Query Distribution
Native SQL programs distributed via the DRN Query Tool
 File distribution query type
 Query and workplan sent to DataMart Administrator via the Query Tool
 DataMart Administrator receives query and work plan with DataMart
Client
 DataMart Administrator runs SQL code locally against their DataMart
and review the output
 Results are approved and uploaded to Query Tool via the DataMart
Client
 DSSNI downloads results for review and tracking
Queries produce aggregate data only (no patient-level data)
39
Data Characterization Queries
PCORnet CDM 1.0
40
Data Characterization: Demographics
Count of unique PATIDs
Frequency of records by
 SEX
 RACE
 HISPANIC
 AGE_GROUP(calculated as of data characterization date)
Summary statistics for age in years
41
Data Characterization: Enrollment
Counts of unique PATIDs and records (enrollment periods)
Distribution of records by
 ENR_START
 ENR_END
Frequency of records
 By enrollment months per PATID
 By enrollment years per PATID
 By year and month of enrollment
 By ENR_BASIS
42
Data Characterization: Encounter
Counts of unique PATIDs and ENCOUNTERID
Frequency of records by




ENC_TYPE
ADMIT_DATE year
ADMIT_DATE year month
ADMITTING_SOURCE
Distribution and frequency of records by







ADMITTING_SOURCE and ENC_TYPE
DISCHARGE_DATE by year
DISCHARGE_DATE by year month
DISCHARGE_DISPOSITION
DISCHARGE_DISPOSITION and ENC_TYPE
DISCHARGE_STATUS
DISCHARGE_STATUS and ENC_TYPE
43
Data Characterization: Diagnosis
Counts of unique PATIDs and ENCOUNTERID
Distribution and frequency of records by
 ADMIT_DATE by year
 ADMIT_DATE by year month
 Principal discharge diagnosis (PDX)
 DX_SOURCE
 DX_SOURCE and DX_TYPE
Distribution of records by Diagnosis Code (DX)
44
Data Characterization: Procedure
Counts of unique PATIDs and ENCOUNTERID
Distribution and frequency of records by
 ADMIT_DATE by year
 ADMIT_DATE by year month
 PX_TYPE
Distribution of records by PX and ENC_TYPE
Distribution of records by Procedure Code (PX)
45
Data Characterization: Vital
Counts of unique PATIDs and ENCOUNTERID Distribution and
frequency of records by
 MEASURE_DATE by year
 MEASURE_DATE by year month
 VITAL_SOURCE
 HEIGHT
 WEIGHT
 DIASTOLIC
 SYSTOLIC
 BMI
46
Query Results Processing
47
Data Characterization Timeline
DSSNI Liaisons will work with networks to determine Data
Characterization schedules/timelines
Prioritization
 1 DataMart per Network complete cycle by end of May
 ADAPTABLE sites/DataMarts
 Obesity Observational Studies
CDM v1.0 DC queries ready for distribution by early April (SQL
Server, Postgres, Oracle)
48
Initial Metrics for PCORI Reporting
Days from distribution to response
Number of unique patients in each table
Trend in unique encounters by encounter type
Month-year of first and last encounter
Number of diagnoses per encounter by encounter type (eg, diagnoses
per ambulatory visit)
Number of procedures per encounter by encounter type (eg, procedures
per inpatient visit)
Frequency of missingness and “out of range” values for critical data
elements (eg, % of missing values for DOB)
 DOB
 Sex
 Race
 Dates
 Encounter type
49
Questions?
50
MS Data QA Overview
Jeff Brown
Why check after every refresh?
Underlying data sources are dynamic
 Verify compliance with CDM
 Identify changes in Data Partners’ data sources or
transformation processes
 Identify problems and/or differences in Data
Partners’ data transformation methods

info@mini-sentinel.org
52
Why check after every refresh?
Green: records from prior refresh
Red: record from new refresh
under review
Problem:
Enrollment data from 2010 was
archived between refreshes and
not included in latest refresh.
Outcome:
Data Partner was asked to
recreate the MS refresh including
2010 data.
info@mini-sentinel.org
53
Why check after every refresh?
Green: records from prior refresh
Red: record from new refresh
under review
Problem:
Loss of 2010 also observed in the
Diagnosis table.
Outcome:
The Data Partner was asked to
recreate the MS refresh including
2010 data.
info@mini-sentinel.org
54
Data QA & Characterization

Four “levels”
• Level 1: checks basic compliance with CDM (completeness,
validity, accuracy)
• Level 2: checks cross-variable and cross-table integrity (integrity)
• Level 3: characterizes trends within and across data partners
(consistency)
• Level 4: characterizes implausible (and illogical) data and
variation in data capture and care practices (plausibility,
convergence)

Standardized error check codes
Err code: Table, Level, Variable Number, and Check Number
– Err Code “DEM1.3.2” denotes the second level 1 check
performed on the variable SEX in the Demographic table
–

QA Data Model (now queryable)
info@mini-sentinel.org
55
MS QA: example
Completeness:
•
ADate variable has missing values
Validity:
•
ADate variable is not SAS date value of numeric data type
•
ADate variable is not of length 4
•
DDate variable is not SAS date value of numeric data type
•
DDate variable is not of length 4
Accuracy:
•
ADate is after DDate (for IP and IS only)
•
ADate and DDate variables have values before DP_MinDate
Integrity:
•
DDate variable is missing for EncType value "IP"
•
DDate variable is populated for records with EncType values
other than "IP" or "IS"
info@mini-sentinel.org
Consistency:
•
Problem with distribution of ADate (i.e. total number of records per year)
within the ETL
•
Problem with distribution of ADate (i.e. total number of records per yearmonth) within the ETL
•
Significant change in number of records per ADate (year) across ETLs
•
Significant change in number of records per ADate (year-month) across ETLs
•
Problem with distribution of ADate (overall) within the ETL
•
Problem with distribution of ADate (overall) across ETLs
•
Problem with distribution of DDate (i.e. total number of records per year)
within the ETL
•
Problem with distribution of DDate (i.e. total number of records per yearmonth) within the ETL
•
Significant change in number of records per DDate (year) across ETLs
•
Significant change in number of records per DDate (year-month) across ETLs
•
Problem with distribution of DDate (overall) within the ETL
•
Problem with distribution of DDate (overall) across ETLs
•
Problem with distribution of DDate variable by EncType per year
•
Problem with distribution of DDate variable by EncType per year-month
•
Problem with distribution of length of stay (DDate-ADate + 1) by EncType
•
Problem with distribution of length of stay (DDate-ADate + 1) by EncType per
year
56
Standardizing clinical lab data
Observed Result Units for Hba1c
Blank
%Hb
%
%HbA1c
% A1C
%NGSP
% A1c
%T.Hgb
% NGSP
%THb
% OF TOTAL
HbA1c%
% TOTAL HGB
MG/DL
% of Hgb
NULL
% of total
PERCENT
%A1C
Percent
%AIC
g/dL
mmol/mol
info@mini-sentinel.org
57
Standardizing clinical lab data
info@mini-sentinel.org
58
Standardizing clinical lab data
Percent of INR Results by Data Partner
20%
18%
Percent of Total INR Results
16%
14%
12%
10%
8%
6%
4%
2%
9.9
9.6
9.3
9.0
8.7
8.4
8.1
7.8
7.5
7.2
6.9
6.6
6.3
6.0
5.7
5.4
5.1
4.8
4.5
4.2
3.9
3.6
3.3
3.0
2.7
2.4
2.1
1.8
1.5
1.2
0.9
0.6
0.3
0.0
0%
Result Value
info@mini-sentinel.org
59
Quality Assurance Statistics
# of QA output files to review: 264
 # of error codes to evaluate: 1,493
 # of analysts who review each refresh: 2
 Average number of analyst hours per data refresh: 16
 # of Data Partners: 18
 # of refreshes/QA reviews per year: ~55
 % of refreshes that undergo QA review: 100%
 Pages of documentation: >100
 SAS code: available online

http://www.mini-sentinel.org/data_activities/distributed_db_and_data/details.aspx?ID=131
info@mini-sentinel.org
65
Aspirin Dosing: A Patient-Centric Trial
Assessing Benefits and Long-term
Effectiveness (ADAPTABLE)
Trial
PCORnet’s First Pragmatic Clinical Trial
Case Scenario
Saul had chest pain while working and was taken to the emergency
room where he learned he was having a heart attack.
Saul’s doctors told him that plaque was building up in his arteries.
Upon discharge from the hospital Saul was advised to take 325mg of
aspirin each day.
Saul compared notes with another friend who said his doctor has him on
a baby aspirin because it causes less bleeding and bruising.
Saul is confused about what dose he should take. He does a lot of work
outdoors and carpentry. He is worried about bleeding while working but
doesn’t want another heart attack either.
Saul now wonders what he should do.
67
Leading causes of death worldwide
1990 2020
Ischemic heart diseases
1
1
Ischemic heart diseases
Cerebrovascular diseases
2
2
Cerebrovascular diseases
Lower respiratory infections
3
3
COPD
Diarrheal diseases
4
4
Lower respiratory infections
Perinatal conditions
5
5
Trachea, bronchus,
lung cancers
6
11
10
16
—Murray CJL, Lopez AD.
The Global Burden of Disease. 1996.
Platelets are critical in acute cardiovascular
events
Quiescent plaque
Lipid core
Process
Marker
Plaque formation
Cholesterol
LDL
Inflammation
Multiple factors
? Infection
C-reactive protein
Adhesion molecules
Interleukin 6, TNFa,
sCD-40 ligand
Vulnerable plaque
TF  Clotting cascade
Inflammation
Collagen  platelet
activation
Foam Cells
Macrophages Metalloproteinases
Plaque rupture
Platelet-thrombin micro-emboli
Plaque rupture
? Macrophages
Metalloproteinases
Thrombosis
Platelet Activation
Thrombin
MDA modified LDL
D-dimer
Fibrinogen
Troponin
Aspirin: A “wonder” drug
Proven clinical benefit in reducing ischemic
vascular events
Cost effective
Benefit with combination antiplatelet therapies
But there are issues:
 Emerging evidence for dose modifiers
(ASA resistance, genetics, P2Y12 inhibitors)
 Equal efficacy across patients?
 Intolerance
Most effective dose uncertain
Risks of aspirin therapy
Intracranial hemorrhage
0.04% per year
Sanjay Gupta; CNN
Risks of aspirin therapy
Intracranial hemorrhage
Likely dose-dependent relationship
Gastrointestinal bleeding
75mg
2.3 OR
150mg
3.2 OR
300mg
3.9 OR
Risk of death from
GI bleed 0.5–10%
Distribution of aspirin dosing at discharge
Other
0.01%
81 mg
36%
325 mg
61%
162 mg
3%
Main Objectives of ADAPTABLE Trial
To compare the effectiveness
and safety of two doses of aspirin
(81 mg and 325 mg) in high-risk
patients with coronary artery
disease.
 Primary Effectiveness
Endpoint: Composite of allcause mortality, nonfatal MI,
nonfatal stroke
 Primary Safety Endpoint:
Major bleeding complications
To compare the effects of aspirin
in subgroups of patients:
 Women vs men
 Older vs younger
 Racial and ethnic minorities
 Diabetics
 Chronic kidney disease (CKD)
To develop and refine the infrastructure for PCORnet to conduct
multiple comparative effectiveness trials in the future
74
Study Design
Patients with known Coronary Artery Disease (MI, or CAD or Revasc) + >1 “Enrichment Factor”*
Identified through EHR/Direct pt. consenting in clinics and hospitals through CDRNs/PPRNs
(PPRN pts. would need to connect through a CDRN to participate)
Pts. contacted electronically with trial information and eConsent
Treatment assignment will be provided directly to patient
ASA 81 mg QD
*Enrichment Factors
 Age > 65 years,
 Creatinine > 1.5,
 Diabetes,
 Known 3 vessel
coronary artery disease,
 Current cerebrovascular
disease and/or
peripheral artery
disease,
 known ejection fraction
<50%
 Current smoker
ASA 325 mg QD
Electronic F/U Q 4 months
Supplemented with EHR/CDM/Claims Data
Duration: Enrollment over 24 months; maximum f/u of 30 months
Primary Endpoint: Composite of all-cause mortality, nonfatal MI,
nonfatal stroke
Primary Safety Endpoint: Major bleeding complications
Trial Logistics:
Taking Advantage
of PCORnet Infrastructure
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Screening, Enrollment & Data Flow
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Computable
phenotype
History of CAD
• Past MI
OR
• Past cath showing
significant CAD
OR
• Revascularization
(PCI/CABG)
At least one
of the following:
• age > 65 years
• Creatinine > 1.5
• Diabetes,
• Known 3 vessel coronary artery
disease
• Current cerebrovascular
disease and/or peripheral artery
disease
• Known ejection fraction <50%,
• Current smoker
Getting consent
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Getting Informed Consent
Clinician reviews and
decides on participation
Email to potential patient with trial
introduction and link to consent
Letter to potential pt. with trial intro and
paper consent for non-Internet accessible
pt.
Consent Form Contacts:
Local contact info for any site issues
Local contact info for withdrawal from trial
Contact info for questions about the trial
Contact info for reporting adverse events
Randomization & ASA dose assignment
Timeline for ADAPTABLE Trial
Application deadline:
February 13, 2015
Merit review:
March 2015
Award announced:
April 2015
Earliest project start:
April 2015
ADAPTABLE SC approval
May 2015
DSMB approval
June 2015
First system/site activation
August 2015
First patient randomized
August 2015
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ADAPTABLE Mindset & Community
This is a novel project employing novel methods
It is being built by a large group of dedicated networks
and people leveraging different experiences, skills and
expertise dedicated to the Mission
If funded:
 Modifications will be needed per the Review
Committee and DMC
 The Steering Committee will need to modify details
based on additional review especially on pragmatism
We need pioneers, willing to work together to solve the
challenge to create a reusable infrastructure
ADAPTABLE needs to be adaptable
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Next steps….
Let’s make a plan for each of the following areas:
Clinician engagement
Recruitment plan
Patient engagement
eConsent/IRB
Follow-up
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Use Cases: Health Systems Demonstration
Project & Obesity Complete Data
Demonstration
Rich Platt & Adrian Hernandez
Open Session for Questions and Wrap Up
All