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integra(ng Data for Analysis, Anonymiza(on, and SHaring
DBP2 Medica(on Surveillance Observa(on Study Frederic S. Resnic, MD, MS
Michael E. Matheny, MD, MS, MPH
Driving Biologic Project 2
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 2
• Brigham & Women’s Hospital and Lahey Health (MA) PI-‐Frederic Resnic Pinak Shah Susan Robbins Richard Cope
• Veterans Affairs MidSouth Healthcare Network (VISN 9) PI-‐Michael Matheny Lalit Nookala
Fern FitzHenry James Fly Svetlana Eden Jason Denton
• University of California – San Diego PI-‐Grace Kuo Paulina Paul Robert El-‐Kareh Seena Farzaneh
Agenda
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 3
1. BACKGROUND 2. METHODS
» Study SeTng » Data Collec(on » Case Finding » Sta(s(cal Methods
3. RESULTS » Outcomes » Limita(ons and Challenges
4. CONCLUSIONS
BACKGROUND
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 4
Medical Product Surveillance
• Historically, the FDA has employed a combina(on of mandatory and voluntary adverse event repor(ng (MedWatch / MAUDE) » No denominator (total exposure volume) data » Incomplete repor(ng » Repor(ng bias based on product-‐outcome visibility
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 5
Need for Medical Product Surveillance
• Vioxx (2004) » Cardiovascular Complica(ons
• Tequin (2006) » Hypoglycemia and Hyperglycemia
• Rosiglitazone (2007) » Associa(on with Myocardial Infarc(on and Cardiovascular Death
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 6
Ac(ve Surveillance Ini(a(ves
• In recent years, a number of ini(a(ves have been established to perform ac(ve surveillance by aggrega(ng and u(lizing: » administra(ve databases – limited clinical data » clinical registries – limited scope, dura(on » electronic health record databases
• Sen(nel Ini(a(ve in 2007 intended to connect enhanced claims data owners to support (mely surveillance
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 7
DBP2 Project Objec(ves • Post-‐marke(ng Surveillance of Novel Hematologic Medica(ons » Compare the safety of two new oral hematologic medica(ons to tradi(onal medica(ons » Dabigatran (Pradaxa) vs. Warfarin (Coumadin)
» Prasugrel (Effient) vs. Clopidogrel (Plavix)
» Three Use Cases: » Atrial Fibrilla(on -‐ Dabigatran » Venous Thromboembolism -‐ Dabigatran » Acute Coronary Syndrome with coronary Sten(ng -‐ Prasugrel
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 8
DBP2 Project Objec(ves
• Post-‐marke(ng Surveillance of Novel Hematologic Medica(ons
» Monitor Safety: Common bleeding complica(ons plus rare, life-‐threatening, events including: hemorrhagic stroke, TTP
» Monitor Efficacy: embolic stroke, repeat thromboembolism, cardiac procedures (coronary stent or CABG surgery), and death
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 9
§ Warfarin (Coumadin)
› Background: approved 1954; vit K antagonist interferes with produc(on of pro-‐coagulants proteins.
› Problems » Very narrow therapeu(c range » High cost of medica(on monitoring (INR tracking) » High rate of serious adverse events » Gene(c variability can greatly effect required dose » Increased gastrointes(nal bleeding
Source: RE-‐LY Study, RE-‐COVER Study
Use Case 1 & 2: An(-‐Thrombo(c Agents
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 10
§ Dabigatran (Pradaxa) › Background: approved 2010, direct thrombin inhibitor
› No Monitoring Required; No known gene(c variability in response
› Reduced rate of thromboembolic events in AF › Problems
» Twice daily dosing » Higher rate of non-‐compliance in trial
» Increased gastrointes(nal bleeding, hemorrhagic stroke
Source: RE-‐LY Study, RE-‐COVER Study
Use Case 1 & 2: An(-‐Thrombo(c Agents
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 11
Use Case 1: Dabigatran RCT
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Connolly et al. RE-‐LY Study, NEJM 2009
RE-‐LY Study: Risk of Stroke or Systemic Thromboembolism
§ Clopidogrel (Plavix) › Approved 1997; thienopyridine platelet inhibitor
› Problems » Modest an(-‐platelet effect
» Delayed onset of ac(on; requires conversion from pro-‐drug
» Considerable inter-‐pa(ent variability in drug response
» Iden(fiable gene(c efficacy variability
Use Case 3: An(-‐Platelet Agents
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 13
§ Prasugrel (Effient) › Approved 2009; thienopyridine › Trial data showing Improved cardiac outcomes compared with Plavix, though with increased risk of bleeding.
› Rapid onset of ac(on, no known gene(c variability
› Problems » A higher risk of bleeding in elderly, thin and pa(ents with prior stroke.
Wivio@ et al. TRITON TIMI-‐38, NEJM 2007
Use Case 3: An(-‐Platelet Agents
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Use Case 3: Prasugrel RCT
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Wivio@ et al. TRITON TIMI-‐38, NEJM 2007
TRITON Study: Risk of Cardiac Events and Bleeding
METHODS
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Data Acquisi(on and Analysis Strategy
1. Data Acquisi(on – local extrac(on from EHR
2. Case Iden(fica(on – selec(on of unique records based on high level event filters
3. Common Data Model (CDM) Transforma(on – implemented OMOP as standard across sites
4. Sta(s(cal Toolset – OMOP CDM to analy(c table, summary stats, compara(ve analysis using OCEANS toolkit
5. Implementa(on and Preliminary Analyses -‐ performed locally, evaluated, and shared through iDASH
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 17 10/2/13
• Study SeTng
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UCSD 4 Sites-‐VA Brigham and Women’s
Massachuseas General
Partners Healthcare
Beds 535 827 773 907 1680
Outpabent visits 597,962 1,760,786 761,687 896,150 1,657,837
Admissions 23,339 14,015 46,432 47,649 94,081
Emergency Visits 60,551 N/A 59,323 88,393 147,716
Inpabent Surgeries 8,900 N/A 19,199 19,206 38,405
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 19
Per Site Clinical Volume Summary
Case Finding
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Case Finding Challenges
• Iden(fy representa(ve and similar pa(ents at par(cipa(ng hospitals » VA Healthcare System – Longitudinal Care, integrated comprehensive EHR, comprehensive medica(on dispensing informa(on, coordinated and conserva(ve adop(on of new medica(ons.
» Academic Medical Centers (UCSD, MGH, BWH) – heterogeneous longitudinal care, limited drug dispensing informa(on, accelerated adop(on of new medica(ons.
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 21
Step 1 Iden(fy primary diagnosis during study period
Step 2 Connectedness determined by primary care or cardiology encounter in 30 days to 2 years prior
Step 3 Drop if pa(ent in hospice or pallia(ve care
Step 4 Remove pa(ents with same primary diagnosis in 2 years prior (Afib Only) Remove pa(ents on any of the four study drugs in prior 30 days Drop pa(ents with length of stay > 30 days
Step 5 Drop pa(ents if on one of two use case study drugs in the year prior
Step 6 Retain only those treated with one of two use case study drugs in 30 days post index and not treated with any of the other study drugs
Step 7 Remove pa(ents with death recorded in 30 days post index
Detail Step Iden(fica(on
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Use case pa(ent iden(fica(on steps
Detail Step Iden(fica(on
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Use case pa(ent iden(fica(on steps
Step 1 Iden(fy primary diagnosis during study period
Step 2 Connectedness determined by primary care or cardiology encounter in 30 days to 2 years prior
Step 3 Drop if pa(ent in hospice or pallia(ve care
Step 4 Remove pa(ents with same primary diagnosis in 2 years prior (Afib Only) Remove pa(ents on any of the four study drugs in prior 30 days Drop pa(ents with length of stay > 30 days
Step 5 Drop pa(ents if on one of two use case study drugs in the year prior
Step 6 Retain only those treated with one of two use case study drugs in 30 days post index and not treated with any of the other study drugs
Step 7 Remove pa(ents with death recorded in 30 days post index
Case Finding Challenges
• “Connectedness”-‐ Feature to iden(fy pa(ents with sustained interac(on with healthcare system in order maximize likelihood of capturing subsequent clinical events.
» primary care or cardiology encounter in 30 days to 2 years prior.
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Cohort Inclusion/Exclusion Counts
Step Atrial Fibrillabon VA UCSD PHS
Venous Thrombemb. VA UCSD PHS
ACS with DES VA UCSD PHS
1 6,872 6,845 16,427 6,999 3,268 10,168 690 177 1,257
2 3,593 13,210 5,693 3,192 10,168 621 79 871
3 3,593 13,210 5,658 3,192 10,168 569 79 871
4 1,900 8,352 4,849 2,937 9,697 493 77 790
5 1,849 7,172 4,733 2,774 8,519 358 69 592
6 327 1,192 902 504 1,478 355 0 589
7 842 324 1,172 867 504 1,458 351 0 586
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 25
Data Collec(on
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Distributed Analysis
• Running a sta(s(cal analysis across mul(ple health care systems required the implementa(on of a common data model (CDM)
• CDM’s use standard terminologies to transcode heterogeneous structured data elements in source EHRs to a common standard terminology
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 27
Concept Mapping
• Observa(onal Medical Outcomes Partnership (OMOP): developed by FNIH star(ng in 2008 with a two-‐year pilot program in a consor(um of Government, Industry, and Academia
• OMOP Source to Concept Mappings and data schema have undergone significant revisions over the years
• Not complete for most use cases, requires some data valida(on and augmenta(on
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 28
Standard Vocabularies
• Includes numerous terminology maps, leveraging the NLM effort within the UMLS and other transla(on efforts
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Vocabulary ID SNOMED-‐CT ICD-‐9-‐CM
ICD-‐9-‐Procedure CPT
HCPCS LOINC NDF-‐RT RxNor NDC Read
FDB Indicabon Mulblex
VA Product VA Class
OMOP Common Data Model Schema
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Source: OMOP CDM Specifications Version 4.0 April 2012
OMOP Source to Concept Mapping
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Source Vocabulary ID
Source Code Source Code Descrip(on
Concept ID
Concept Vocabulary ID
2 360.43 Hemophthalmos, except current injury 375539 1
2 430 Subarachnoid hemorrhage 432923 1
2 431 Intracerebral hemorrhage 376713 1
2 432 Other and unspecified intracranial hemorrhage 4108355 1
2 432 Nontraumabc extradural hemorrhage 436430 1
2 432.1 Subdural hemorrhage 4318408 1
2 432.9 Unspecified intracranial hemorrhage 439847 1
2 568.81 Hemoperitoneum (nontraumabc) 194690 1
2 853.01 Other and unspecified intracranial hemorrhage following injury, without menbon of open intracranial wound, with no loss of consciousness 440869 1
2 853.02 Other and unspecified intracranial hemorrhage following injury, without menbon of open intracranial wound, with brief [less than one hour] loss of consciousness 438596 1
2 853.03 Other and unspecified intracranial hemorrhage following injury, without menbon of open intracranial wound, with moderate [1-‐24 hours] loss of consciousness 436841 1
2 853.04
Other and unspecified intracranial hemorrhage following injury, without menbon of open intracranial wound, with prolonged [more than 24 hours] loss of consciousness and return to pre-‐exisbng conscious level 436842 1
Source: OMOP Source_to_Concept_Map Version 4.0 April 2012
Observa(onal Cohort Event Analysis & No(fica(on
System (OCEANS):
A Distributed Automated Sta(s(cal Analysis Engine
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 32
OCEANS Design Specifica(ons
• Programming Framework Flexibility (C# & Java)
• Scalability (Big Data)
• Expandability (Sta(s(cal Method Modularity)
• Mul(ple Data Source Compa(bility (Flat File / OMOP)
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OCEANS: Sta(s(cal Methods
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Stabsbc .NET Version
Java Version
Data Diagnosbcs Descripbve Stabsbcs X X Mulb-‐collinearity diagnosbcs X Risk Adjustment Methods Linear Regression X Logisbc Regression X X Propensity Score Matching X X Sequenbal Comparabve Effecbveness Analybcs Risk Adjusted Sequenbal Probability Rabo Tesbng X X Maximized Sequenbal Probability Rabo Tests X Regression-‐Adjusted Proporbonal Difference Analysis X X Bayesian Logisbc Regression X X
Ac(ve Surveillance Framework: OCEANS
• Reference (Unexposed) Group Selec(on » Retrospec(ve – Generate Unexposed Risk Model » Prospec(ve Concurrent – Propensity Score Matching
• Sta(s(cal Method Selec(on » Time Based Grouping (Week, Month, etc.) versus Sequen(al
» Aler(ng Threshold – All Odds Ra(os > 1.0 or >2.0 (or 1.5, etc.)
» Incorpora(on of Type I and Type II measurement error » Adjustment for Repeated Measurements
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Prospec(ve Propor(onal Difference
• Prospec(ve concurrent analysis adjusts best for pa(ent case mix and changes in clinical prac(ce over (me
• Propensity Score Matching balances measured confounding by excluding unmatched cases
• Sample size can be limited by the smaller group • Strong contraindica(ons for use of exposure can create poor
matching with loss of generalizability and sample size • Can use a large number of covariates in model
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Rosenbaum P, et al. Biometrika. 1983;70:41-‐55. Brookhart MA, et al. Am J Epidemiol. 2006;163(12):1149-‐1156. Aus(n, PC. Biometrical J. 2009;51:171-‐184. Newcombe RG. StaLsLcs in Medicine. 1998;17(8):873-‐90.
Propensity Score Matched PD Example
Exposure: Vascular Closure Devices, Reference: Manual Compression, Outcome: Retroperitoneal Hemorrhage
Source: Matheny et al. AMIA Annu Symp Proc. 2007;518-‐522. Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 37
Risk Adjusted SPRT
• Variant of Sta(s(cal Process Control
• Formal framework for incorpora(ng ά and β error as well as repeated measurements
• Specify odds ra(o of event rate eleva(on detec(on desired
• Risk adjustment using risk model from retrospec(ve data incorporated into the cumula(ve log likelihood ra(o calcula(ons
Source: Spiegelhalter, et al. International Journal of Quality Healthcare 2003;15:7-13 Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 38
Risk Adjusted SPRT Example
Source: Matheny et al. American Heart Journal. 2008;155:114-20
Exposure: Single Operator, Reference: All National/Local Operators, Outcome: Inpatient Mortality
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RESULTS
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Pa(ent Characteris(cs (Prelim Extract)
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 41 10/2/13
UCSD % VA %
Partners Healthcare
%
Total
Total %
Study Pabents 626 2,060 2,792 5,415
New Drug (%) 15 31.2% 18 5.3% 22 2.5% 55 4.4%
Age (mean ± SD) 71.7 ± 12.6
70.5 ± 10.1 70.3 ± 13.0 ~ 70
Female (%) 21 43.8% 8 2.4% 361 41.2% 390 30.9%
Diabetes (%) N/A* 11 3.3% 27 3.1% >38 >3.0%
Hypertension (%) 0 41 12.2% 0 41 3.2%
Hyperlipid (%) N/A* 35 10.4% 135 15.3% >170 13.5%
Hx of CVA (%) N/A* 9 2.7% 0 >9 .01%
Hix of Cancer (%) 0 20 5.9% 0 20 15.8%
Common Patient Characteristics – AF Use Case
* Not available in preliminary data extract
An(-‐Thrombo(c Use Cases
Dabigatran versus Warfarin:
1. New Onset Atrial Fibrilla(on 2. New Venous Thromboembolism (DVT or PE)
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 42
Prasugrel versus Clopidogrel: 3. Acute Coronary Syndrome (Unstable Angina
or Non-‐ST eleva(on myocardial infarc(on) with Drug Elu(ng Stent use.
Atrial Fibrilla(on
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Exposure: Dabigatran vs. warfarin Outcome: Death
Partners 0 0 0 0 0 0 0 12 30 76 126 236 298 360VA 0 0 0 0 0 0 0 0 0 0 0 4 4 8UCSD 0 0 0 0 0 0 0 4 20 32 32 32 32 32
Atrial Fibrilla(on
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Exposure: Dabigatran vs. warfarin Outcome: Cerebrovascular Event
Partners 0 0 0 0 0 0 0 12 30 76 126 236 298 360VA 0 0 0 0 0 0 0 0 0 0 0 4 4 8UCSD 0 0 0 0 0 0 0 4 20 32 32 32 32 32
Atrial Fibrilla(on
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Exposure: Dabigatran vs. warfarin Outcome: Thromboembolic Event
Partners 0 0 0 0 0 0 0 12 30 76 126 236 298 360VA 0 0 0 0 0 0 0 0 0 0 0 4 4 8UCSD 0 0 0 0 0 0 0 4 20 32 32 32 32 32
Atrial Fibrilla(on
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Exposure: Dabigatran vs. warfarin Outcome: Major Bleed
Partners 0 0 0 0 0 0 0 12 30 76 126 236 298 360VA 0 0 0 0 0 0 0 0 0 0 0 4 4 8UCSD 0 0 0 0 0 0 0 4 20 32 32 32 32 32
Atrial Fibrilla(on
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Exposure: Dabigatran vs. warfarin Outcome: Minor Bleed
Partners 0 0 0 0 0 0 0 12 30 76 126 236 298 360VA 0 0 0 0 0 0 0 0 0 0 0 4 4 8UCSD 0 0 0 0 0 0 0 4 20 32 32 32 32 32
An(-‐Thrombo(c Use Cases
Dabigatran versus Warfarin:
1. New Onset Atrial Fibrilla(on 2. New Venous Thromboembolism (DVT or PE)
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 48
Prasugrel versus Clopidogrel: 3. Acute Coronary Syndrome (Unstable Angina
or Non-‐ST eleva(on myocardial infarc(on) with Drug Elu(ng Stent use.
Venous Thromboembolism
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Exposure: Dabigatran vs. warfarin Outcome: Death
Partners 0 0 0 0 0 0 0 0 12 20 28 34 42 46VA 0 0 0 0 0 0 0 0 0 0 0 2 2 2UCSD 0 0 0 0 0 0 0 6 8 10 10 10 10 10
Venous Thromboembolism
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Exposure: Dabigatran vs. warfarin Outcome: Cerebrovascular Event
Partners 0 0 0 0 0 0 0 0 12 20 28 34 42 46VA 0 0 0 0 0 0 0 0 0 0 0 2 2 2UCSD 0 0 0 0 0 0 0 6 8 10 10 10 10 10
Venous Thromboembolism
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Exposure: Dabigatran vs. warfarin Outcome: Thromboembolic Event
Partners 0 0 0 0 0 0 0 0 12 20 28 34 42 46VA 0 0 0 0 0 0 0 0 0 0 0 2 2 2UCSD 0 0 0 0 0 0 0 6 8 10 10 10 10 10
Venous Thromboembolism
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Exposure: Dabigatran vs. warfarin Outcome: Major Bleed
Partners 0 0 0 0 0 0 0 0 12 20 28 34 42 46VA 0 0 0 0 0 0 0 0 0 0 0 2 2 2UCSD 0 0 0 0 0 0 0 6 8 10 10 10 10 10
Venous Thromboembolism
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Exposure: Dabigatran vs. warfarin Outcome: Minor Bleed
Partners 0 0 0 0 0 0 0 0 12 20 28 34 42 46VA 0 0 0 0 0 0 0 0 0 0 0 2 2 2UCSD 0 0 0 0 0 0 0 6 8 10 10 10 10 10
An(-‐Thrombo(c Use Cases
Dabigatran versus Warfarin:
1. New Onset Atrial Fibrilla(on 2. New Venous Thromboembolism (DVT or PE)
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 54
Prasugrel versus Clopidogrel: 3. Acute Coronary Syndrome (Unstable Angina
or Non-‐ST eleva(on myocardial infarc(on) with Drug Elu(ng Stent use.
Drug Elu(ng Stent – with ACS
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Exposure: Prasugrel vs. clopidogrel Outcome: Death
Partners 0 0 4 14 16 28 36 46 62 74 90 106 120 124VA 0 0 0 0 0 0 0 2 2 2 6 8 12 18UCSD 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Drug Elu(ng Stent – with ACS
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Exposure: Prasugrel vs. clopidogrel Outcome: Cerebrovascular Event
Partners 0 0 4 14 16 28 36 46 62 74 90 106 120 124VA 0 0 0 0 0 0 0 2 2 2 6 8 12 18UCSD 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Drug Elu(ng Stent – with ACS
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Exposure: Prasugrel vs. clopidogrel Outcome: Major Bleed
Partners 0 0 4 14 16 28 36 46 62 74 90 106 120 124VA 0 0 0 0 0 0 0 2 2 2 6 8 12 18UCSD 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Drug Elu(ng Stent – with ACS
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Exposure: Prasugrel vs. clopidogrel Outcome: Minor Bleed
Partners 0 0 4 14 16 28 36 46 62 74 90 106 120 124VA 0 0 0 0 0 0 0 2 2 2 6 8 12 18UCSD 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Study Challenges and Limita(ons
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Major Limita(ons
1. Disparate source data and representa(on within ins(tu(onal EHR systems
2. Variable par(cipant IRB policies, challenging synchroniza(on of study (me periods
3. Mapping to CDM (OMOP) challenging, and resource intensive
4. Slow and variable uptake of novel medica(ons at par(cipa(ng centers limi(ng sample size
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Data Acquisi(on Challenges • Pa(ent Popula(ons differ across par(cipa(ng ins(tu(ons:
» >70% VA pa(ents con(nued care at VA versus 40-‐50% at non-‐governmental systems
» VA 95% male versus 68% non-‐governmental » VA with more co-‐morbidi(es and MD visits (Agha, 2000) » Drug dispensing captured only in VA dataset
• Data capture and representa(on within EHR vary across ins(tu(on type: » Diagnosis codes for billing vs. problem lists » Poten(al for under-‐ascertainment for VA vs. Private Health care systems. more diagnosis codes in civilian bills (Borowsky, 1999)
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Site Specific Regulatory Challenges
• IRB governance and policies vary significantly among par(cipa(ng ins(tu(ons » UCSD permits only request for retrospec(ve clinical data » Variable limits on submission amendments » Variable limits on frequency of data requests (and source data refresh schedules)
• Par(cipa(ng sites have variable restric(ons on scope, breadth and volume of data available for research » Restric(ons at Partners on maximum records retrieved necessitated combining mul(ple extract slices for each cohort
» UCSD administra(ve billing coding data managed by different organiza(on; challenging to integrate into data warehouse.
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OMOP Transforma(on Challenges
• Significant commitment of (me and resources » VA experience -‐ 6 to 8 person months for OMOP coding. » Confirms experience of early adopters -‐-‐ “On average, conver(ng a database to the OMOP required the equivalent of four full-‐(me employees for 6 months” (Overhage, 2012)
» Recommend budget for ini(al and ongoing, split processing
• OMOP is evolving, incomplete, and cumbersome to implement » Not all ICD-‐9 diagnosis codes mapped to SNOMED » “Era” tables -‐ not used, complex to populate » Validity check programs difficult to run (OSCAR and GROUCH)
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Data Transforma(on Challenges
• Missing source codes for conversion to OMOP: » Drugs – UCSD and Partners did not have mapping to standard terminology
» Laboratory – UCSD had no mapping to standard terminology. VA had LOINC missing in >10% of Troponin and INR results
» Clinic stop codes not mapped to standard terminology for specialty clinics (at all sites).
• Confirms findings of recent study which found substan(al
varia(on in mapped codes (55.8-‐69.2%) (Defalco, 2013)
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Sample Size Limita(ons
• Low numbers of exposures in the new agents due to deliberate clinical uptake and ins(tu(onal policies.
» Novel medica(ons are expensive (10-‐20x cost per dose)
» Delay due to ins(tu(onal policy, insurance coverage, P&T commizee review (biggest impact at VAHS)
• Varia(on in data access and study periods – » VA and Partners: case finding 1/1/09 thru 6/30/12 with events followed through 12/30/12
» UCSD started 3/1/thru 6/30/12 with events followed through 12/30/12
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Regulatory Challenges
• So{ware Deployment (code review) • Data Sharing Challenges (aggregate only)
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CONCLUSIONS
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Clinical Conclusions
• In pa(ents with new-‐onset Atrial Fibrilla(on, Dabigatran was associated with: » Reduced rates of thromboembolic events » Trends towards reduc(on of major and minor bleeding
• In pa(ents with Venous Thrombembolism, there were no significant differences between Dabigatran and Warfarin treated pa(ents. » Early increased bleeding risks abated over (me.
• In pa(ents with ACS-‐DES, there were no significant differences between Prasugrel and Clopidogrel treated pa(ents.
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Conclusions
• Distributed surveillance is necessary in order to monitor infrequently used medica(ons with low event rates.
• Transforma(on to a common data model is required for effec(ve distributed analysis across ins(tu(ons
• Automated, distributed medica(on surveillance is feasible and technical components can be shared among disparate health care organiza(ons.
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 69
Acknowledgements
Nashville • Arijit Basu • Jason Denton • Svetlana Eden • Fern Fitzhenry • Michael Matheny • Lalit Nookala • Theodore Speroff • James Fly
Boston • Richard Cope • Frederic S. Resnic • Susan Robbins • Pinak Shah
San Diego • Aziz Boxwala • Rob El-Kareh • Grace Kuo • Ken Nunes • Kiltesh Patel • Paulina Paul • Lucila Ohno-Machado
Grant Funding • NIH AHRQ R-‐01-‐HS-‐019913 (Ohno-‐ Machado) • NIH NHLBI U-‐54-‐HL-‐108460 (Ohno-‐Machado) • VA HSR&D CDA-‐2 2008-‐020 (Matheny) • NIH NLM R-‐01-‐LM-‐0814204 (Resnic) • FDA SOL-‐08-‐00837A (Resnic)
• ….and many others!!
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 70
Thank you!
Ques(ons?
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 71
Maximized SPRT
• Variant of Sta(s(cal Process Control
• Formal framework for incorpora(ng ά and β error as well as repeated measurements
• Composite hypothesis for event detec(on where any odds ra(o > 1.0
• Poisson version uses risk model from retrospec(ve data for risk adjustment
• Case-‐Control version uses propensity score matching for prospec(ve concurrent analysis
Source: Li, Kulldorff. Statist. Med. 2010;29:284-295 Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 72
Poisson Maximized SPRT
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 73
Source: Greene et al. Pharmacoepidemiol Drug Saf. 2011 Jun;20(6);583-90
• Use Case Risk Factors and outcomes covariates were iden(fied in site data sets. Missing data impacted final analysis date range VA dates 1/1/2009-‐8/30/2010
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 74
Missing Data (VA)
• Disparity of common date range does not allow for mul(-‐site analysis. UCSD dates 1/1/2012 – 6/30/2013
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 75
Missing Data (UCSD)
Outcomes
Supported by the NIH Grant U54 HL108460 to the University of California, San Diego 76
Death Cardiac Event Myocardial infarc(on Unstable Angina Cerebrovascular Event Cerebral thrombosis Cerebral emobism Transient Cerebral ischemia PostOp Stroke Repeat Revasculariza(on CABG PCI Thromboembolic Event Deep Vein Thrombosis Acute Pulmonary Embolism Major Bleed Intracranial hemmorhage/Bleed Hemogrobin drop >= 5 g/dl Hematacrit drop >= 15% Blood Transfusion > 5 units w/in 48 hrs Minor Bleed Control of Epistaxis Coagula(on/Hemmorhage Diagnosis GI Bleed Other Misc Bleed
All Outcomes > 30 days from administra(on of new drug
Drug Elu(ng Stent
UCSD % VA % Partners % Total %
Pa(ent Count 351 352 703 Age 65.0 +/-‐ 9.1 62.917 Female 7 2% 92 0.26 99 0.14 Chonic Lung Disease 10 0.03 6 0.02 16 0.02 PAD 19 0.05 7 0.02 26 0.04 Prior CABG 8 0.02 0 0.00 8 0.01 Prior Cancer 30 0.09 17 0.05 47 0.07 Prior CHF 14 0.04 19 0.05 33 0.05 Prior DVT 3 0.01 2 0.01 5 0.01 Prior MI 0 0.00 13 0.04 13 0.02 Prior PE 2 0.01 0 0.00 2 0.00 Prior Smoking 29 0.08 17 0.05 46 0.07 Prior Stroke 30 0.09 18 0.05 48 0.07
Atrial Fibrillabon
UCSD % VA % Partners % Total %
Pa(ent Count 319 842 1453 2614
Age 70.1+/-‐13.1 70.1+/-‐9.7 61.4 +/-‐17.0
Female 132 0.41 11 0.01 608 0.42 751 0.29 Chonic Lung Disease 26 0.08 34 0.04 134 0.09 194 0.07 PAD 2 0.01 44 0.05 63 0.04 109 0.04 Prior CABG 0 0 1 0.00 0 0.00 1 0.00 Prior Cancer 39 0.12 50 0.06 215 0.15 304 0.12 Prior CHF 12 0.04 73 0.09 165 0.11 250 0.10 Prior DVT 5 0.02 7 0.01 83 0.06 95 0.04 Prior MI 0 0 0 0.00 65 0.04 65 0.02 Prior PE 2 0.01 5 0.01 41 0.03 48 0.02 Prior Smoking 6 0.02 41 0.05 76 0.05 123 0.05 Prior Stroke 24 0.08 57 0.07 147 0.10 228 0.09
Venous Thromboembolism UCSD % VA % Partners % Total %
Pa(ent Count 307 867 924 2098
Age 59.4795+/-‐15.468
66.4675 +/-‐ 12.5391
Female 143 0.47 37 0.04 693 0.75 873 0.42 Chonic Lung Disease 27 0.09 60 0.07 0 87 0.04 PAD 5 0.02 64 0.07 0 69 0.03 Prior CABG 0 0 6 0.01 0 6 0.00 Prior Cancer 65 0.21 95 0.11 0 160 0.08 Prior CHF 8 0.03 61 0.07 0 69 0.03 Prior DVT 0 0 20 0.02 0 20 0.01 Prior MI 3 0.01 0 0.00 0 3 0.00 Prior PE 0 0 7 0.01 0 7 0.00 Prior Smoking 16 0.05 68 0.08 0 84 0.04 Prior Stroke 22 0.07 79 0.09 0 101 0.05
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