DBP2))Medicaon)Surveillance)) Observaon)Study) Fred Resnic... · DBP2))Medicaon)Surveillance))...

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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

Transcript of DBP2))Medicaon)Surveillance)) Observaon)Study) Fred Resnic... · DBP2))Medicaon)Surveillance))...

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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      

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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  

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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  

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BACKGROUND  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   4  

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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  

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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  

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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  

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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  

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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  

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§ 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  

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§  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  

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Use  Case  1:    Dabigatran  RCT  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   12  

Connolly  et  al.    RE-­‐LY  Study,  NEJM  2009  

RE-­‐LY  Study:    Risk  of  Stroke  or  Systemic  Thromboembolism    

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§  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  

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§  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  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   14  

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Use  Case  3:  Prasugrel  RCT  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   15  

Wivio@  et  al.    TRITON  TIMI-­‐38,  NEJM  2007  

TRITON  Study:    Risk  of  Cardiac  Events  and  Bleeding  

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METHODS  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   16  

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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  

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• Study  SeTng  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   18  

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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  

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Case  Finding  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   20  

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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  

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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  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   22  

Use  case  pa(ent  iden(fica(on  steps  

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Detail  Step  Iden(fica(on  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   23  

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  

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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.    

 

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   24  

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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  

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Data  Collec(on  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   26  

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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  

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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  

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Standard  Vocabularies  

•  Includes  numerous  terminology  maps,  leveraging  the  NLM  effort  within  the  UMLS  and  other  transla(on  efforts  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   29  

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  

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OMOP  Common  Data  Model  Schema  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   30  

Source: OMOP CDM Specifications Version 4.0 April 2012

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OMOP  Source  to  Concept  Mapping  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   31  

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  

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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  

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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)  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   33  

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OCEANS:    Sta(s(cal  Methods  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   34  

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  

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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  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   35  

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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  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   36  

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.    

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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  

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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  

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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

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   39  

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RESULTS    

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   40  

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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  

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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.    

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Atrial  Fibrilla(on  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   43  

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

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Atrial  Fibrilla(on  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   44  

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

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Atrial  Fibrilla(on  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   45  

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

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Atrial  Fibrilla(on  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   46  

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

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Atrial  Fibrilla(on  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   47  

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

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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.    

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Venous  Thromboembolism  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   49  

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

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Venous  Thromboembolism  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   50  

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

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Venous  Thromboembolism  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   51  

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

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Venous  Thromboembolism  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   52  

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

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Venous  Thromboembolism  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   53  

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

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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.    

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Drug  Elu(ng  Stent  –  with  ACS  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   55  

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

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Drug  Elu(ng  Stent  –  with  ACS  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   56  

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

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Drug  Elu(ng  Stent  –  with  ACS  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   57  

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

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Drug  Elu(ng  Stent  –  with  ACS  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   58  

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

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Study  Challenges  and  Limita(ons  

 

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   59  

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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  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   60  

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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)  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   61  

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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.  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   62  

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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)  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   63  

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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)  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   64  

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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  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   65  

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Regulatory  Challenges  

•  So{ware  Deployment  (code  review)  •  Data  Sharing  Challenges  (aggregate  only)  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   66  

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CONCLUSIONS    

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   67  

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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.    

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   68  

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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  

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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  

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Thank  you!    

Ques(ons?  

Supported  by  the  NIH  Grant  U54  HL108460  to  the  University  of  California,  San  Diego   71  

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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  

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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

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•  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)  

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•  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)  

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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  

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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  

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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  

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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  


DART Tutorial Sec’on 4: How should observaons impact an ...Single observed variable, single unobserved variable. So far, have known observaon likelihood for single variable. Now,

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