pcornet coordinating center site visit: gpc march 19, 2015 call-in:1 (571) 317-3122 access code:...
TRANSCRIPT
PCORnet Coordinating Center Site Visit: GPC
March 19, 2015
Call-in:1 (571) 317-3122 Access code: 863-993-413
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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
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.
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Guiding principle: Make research easier
Analysis ready data
Reusable analysis tools
Administrative simplicity
Simple, pragmatic studies integrated into routine care
Outpatient clinic 2
Outpatient clinic 3
Each organization can participate in multiple networksEach network controls its governance and coordinationOther networks can participateNetworks share infrastructure, data curation, analytics, lessons, security, software development
Health Plan 2
Health Plan 1
Health Plan 5
Health Plan 4
Health Plan 7
Hospital 1
Health Plan 3
Health Plan 6
Health Plan 8
Hospital 3Health Plan 9
Hospital 2
Hospital 4
Hospital 6
Hospital 5
Outpatient clinic 1
Patient network 1
Patient network 3
Patient network 2
Multiple Networks Sharing Infrastructure
Outpatient clinic 2
Outpatient clinic 3
Health Plan 2
Health Plan 1
Health Plan 5
Health Plan 4
Health Plan 7
Hospital 1
Health Plan 3
Health Plan 6
Health Plan 8
Hospital 3Health Plan 9
Hospital 2
Hospital 4
Hospital 6
Hospital 5
Outpatient clinic 1
Patient network 1
Patient network 3
Patient network 2
Multiple Networks Sharing Infrastructure
Each organization can participate in multiple networksEach network controls its governance and coordinationOther networks can participateNetworks share infrastructure, data curation, analytics, lessons, security, software development
Data captured from healthcare delivery, direct encounter basis
Data captured from processes associated with healthcare delivery
Data captured within multiple contexts: healthcare delivery,
registry activity, or directly from patients
Fundamental basis
PATIDBIRTH_DATEBIRTH_TIMESEXHISPANICRACEBIOBANK_FLAG
DEMOGRAPHIC
PATIDENR_START_DATEENR_END_DATECHARTENR_BASIS
ENROLLMENT
PATIDENCOUNTERIDSITEIDADMIT_DATEADMIT_TIMEDISCHARGE_DATEDISCHARGE_TIMEPROVIDERIDFACILITY_LOCATIONENC_TYPEFACILITYIDDISCHARGE_DISPOSITIONDISCHARGE_STATUSDRGDRG_TYPEADMITTING_SOURCE
ENCOUNTER
PATIDENCOUNTERID (optional)MEASURE_DATEMEASURE_TIMEVITAL_SOURCEHTWTDIASTOLICSYSTOLICORIGINAL_BMIBP_POSITIONTOBACCOTOBACCO_TYPE
VITAL
PATIDENCOUNTERIDENC_TYPE (replicated)ADMIT_DATE (replicated)PROVIDERID (replicated)DXDX_TYPEDX_SOURCEPDX
DIAGNOSIS
PATIDENCOUNTERIDENC_TYPE (replicated)ADMIT_DATE (replicated)PROVIDERID (replicated)PX_DATEPXPX_TYPE
PROCEDURE
PATIDRX_DATENDCRX_SUPRX_AMT
DISPENSING
PATIDENCOUNTERID (optional)LAB_NAMESPECIMEN_SOURCELAB_LOINCSTATRESULT_LOCLAB_PXLAB_PX_TYPELAB_ORDER_DATESPECIMEN_DATESPECIMEN_TIMERESULT_DATERESULT_TIMERESULT_QUALRESULT_NUMRESULT_MODIFIERRESULT_UNITNORM_RANGE_LOWMODIFIER_LOWNORM_RANGE_HIGHMODIFIER_HIGHABN_IND
LAB_CM_RESULT
PATIDENCOUNTERID (optional)REPORT_DATERESOLVE_DATECONDITION_STATUSCONDITIONCONDITION_TYPECONDITION_SOURCE
CONDITION
PATIDENCOUNTERID (optional)CM_ITEMCM_LOINCCM_DATECM_TIMECM_RESPONSECM_METHODCM_MODECM_CAT
PRO_CM
vv
New to v2.0New to v2.0
PCORnet Common Data Model, Draft v2.0 Modifications
CDRN 1
PCORnet DRN Coordinating Center
PCORnet Secure Network Portal
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Demographics Utilization
Etc
Review & Run Query
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Review & Return Results
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CDRN 11
Demographics Utilization
Etc
Review & Run Query
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Review & Return Results
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1. User creates and submits query (a computer program)
2. Individual CDRNs/PPRNs retrieve query
3. CDRNs/PPRNs review and run query against their local data
4. CDRNs/PPRNs review results
5. CDRNs/PPRNs return results via secure network
6. Results are aggregated
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Toh Arch Intern Med.2012;172:1582-1589.
Used data for 3.9 million new users of anti-hypertensives in 18 organizationsPropensity score matched stratified analysisNo person-level data was sharedFive months and $250,000 required for programming and analysis – compared to 1-2 years and $2 million without analysis-ready distributed dataset
Reusable Program: Propensity Score Matched Cohort Study
Specify:Population (age/sex/etc), time periodExposuresOutcomes 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 criteriaExclusion criteriaCovariatesPropensity score matching options Comorbidity, utilization, high dimensional propensity
score Matching ratio Caliper size
Angioedema: Table 1. Unmatched Cohort
3.9 million new users
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf
Diabetes 21% vs 10%Heart failure 2% vs 4%Ischemic heart disease 5% vs 13%
Propensity Scores Before Match
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdfDP3
Angioedema: Table 2. Matched Cohort
2.6 million new users
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf
Diabetes 10% vs 10%Heart failure 3% vs 3%Ischemic heart disease 8% vs 8%
Propensity Scores After Match
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdfDP3
Angioedema: Table 3. Results
ACEI vs β-blocker 1:1 matched analysis:• HR = 3.1
(95% CI, 2.9-3.4)
www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Known-Positives-ACEI-Angioedema.pdf
Toh et al findings: • HR = 3.0
(95% CI, 2.8-3.3)
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
Randomization & ASA dose assignment
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.
Clinician reviews and decides on participation
Consent Form Contacts:Local contact info for any site issuesLocal contact info for withdrawal from trialContact info for questions about the trialContact info for reporting adverse events
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
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Mini-Sentinel/PCORnet Data Linkage Workgroup
Rich Platt
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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
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Scenarios for potential collaboration
Scenario 0
Characterize within network and outside network utilization for each CDRN by providing CDRN NPI/TIN information to Mini-Sentinel Data Partners with which they have meaningful overlap.
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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.
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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.
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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.
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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 4BCreate 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.
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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.
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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
PCORnet Data Characterization Overview
Jeff Brown and Lesley Curtis
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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
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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
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Data Characterization Cycle
Create DataMart and
ETL ADD
DSSNI sends DC queries
DC query output to DSSNI
DSSNI review; summary to
PCORI
DSSNI and CDRN site discuss DC
results
DSSNI approves
Datamart for querying
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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)
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Data Characterization Queries
PCORnet CDM 1.0
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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
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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
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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
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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)
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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)
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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
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Query Results Processing
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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)
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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
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Questions?
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
Why check after every refresh?
Green: records from prior refreshRed: 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.
Why check after every refresh?
Green: records from prior refreshRed: 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.
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)
MS QA: example
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 year-
month) 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 year-
month) 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
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"
Standardizing clinical lab data
Blank%% A1C% A1c% NGSP% OF TOTAL% TOTAL HGB% of Hgb% of total%A1C%AIC
%Hb
%HbA1c%NGSP%T.Hgb%THbHbA1c%MG/DLNULLPERCENTPercentg/dLmmol/mol
Observed Result Units for Hba1c
Standardizing clinical lab data
Standardizing clinical lab data
0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 4.2 4.5 4.8 5.1 5.4 5.7 6.0 6.3 6.6 6.9 7.2 7.5 7.8 8.1 8.4 8.7 9.0 9.3 9.6 9.90%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
Result Value
Perc
ent o
f Tot
al IN
R Re
sults
Percent of INR Results by Data Partner
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
Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE)Trial
PCORnet’s First Pragmatic Clinical Trial
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Case ScenarioSaul 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.
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Leading causes of death worldwide
Ischemic heart diseases
Cerebrovascular diseases
Lower respiratory infections
Diarrheal diseases
Perinatal conditions
COPD
Trachea, bronchus, lung cancers
1990 2020Ischemic heart diseases
Cerebrovascular diseases
Lower respiratory infections
—Murray CJL, Lopez AD. The Global Burden of Disease. 1996.
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Platelets are critical in acute cardiovascular events
Quiescent plaqueProcess
Plaque formation
InflammationMultiple factors? Infection
Plaque rupture? MacrophagesMetalloproteinases
ThrombosisPlatelet ActivationThrombin
Marker
CholesterolLDL
C-reactive proteinAdhesion moleculesInterleukin 6, TNFa,sCD-40 ligand
MDA modified LDL
D-dimerFibrinogenTroponin
Vulnerable plaque
MacrophagesFoam Cells
Collagen platelet activation
TF Clotting cascade
Lipid core
Metalloproteinases
Inflammation
Platelet-thrombin micro-emboliPlaque rupture
Aspirin: A “wonder” drugProven 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
Gastrointestinal bleeding
Likely dose-dependent relationship
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
81 mg36%
162 mg3%
325 mg61%
Other0.01%
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 all-cause 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)
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To develop and refine the infrastructure for PCORnet to conduct multiple comparative effectiveness trials in the future
Study DesignPatients 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 eConsentTreatment assignment will be provided directly to patient
ASA 81 mg QD ASA 325 mg QD
Electronic F/U Q 4 monthsSupplemented 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
*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
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
Randomization & ASA dose assignment
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.
Clinician reviews and decides on participation
Consent Form Contacts:Local contact info for any site issuesLocal contact info for withdrawal from trialContact info for questions about the trialContact info for reporting adverse events
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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 activationAugust 2015
First patient randomizedAugust 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
Use Cases: Health Systems Demonstration Project & Obesity Complete Data Demonstration
Rich Platt & Adrian Hernandez
Open Session for Questions and Wrap Up
All