Challenges and Solutions in OHDSI Network Studies

 
OHDSI Network Studies
Issues and possible solutions
 
Martijn Schuemie
 
Distributed Research Networks
 
2
 
Same across sites:
Common Data Model
R
 
Different:
Health care system
Data capture process
Source coding systems
ETL
Database platform
Possible issues when executing a network study
 
Technical issues
Edge cases
E.g. error because cohort is empty
Deviations from standard
E.g. error because dates stored as varchar
Bad testing
E.g. code won’t run at any site due to syntax errors
Local tech problems
E.g. weird issues with database configuration
Dependency problems
E.g. wrong version of FeatureExtraction installed
Possible issues when executing a network study
 
Source data issues
E.g. physician specialty not recorded but required in cohort definition
E.g. drug is not prescribed in country
Data size issues
E.g. not enough subjects to fit propensity model
ETL issues
E.g. observations derived from diagnoses are stored in
condition_occurrence
Mapping issues
E.g. source code not mapped to standard concept
Bias issues
E.g channeling due to site-specific doctors preferences / guidelines
Proposed solution 1: testing
 
Always test on multiple databases before sending out to OHDSI
network
 
Make sure to test the code you’ll be sending to others, not some
other version.
 
Yes, that means you’ll probably be running the same study on
your own data many times. (good thing: reproducible)
Proposed solution 2: Dependency documentation
Use OhdsiRTools::insertEnvironmentSnapshotInPackage
Dependency enforcement:
-
Packrat
-
?
Proposed solution 3: Achilles (Heel)
 
Run Achilles at every site
Require Achilles Heel errors to be resolved
Can detect deviations from standard and ETL issues
Share:
Heel report
Whole of Achilles?
Within study consortium or across all of OHDSI?
How to link version between Achilles and study?
Proposed solution 4: Cohort counts
Compute counts per cohort per database
No glaucoma due to specialty
requirement
Low stroke count due to
“inpatient visit” requirement
 
Proposed solution 5: Orphan code check
 
For example
Look for concepts with “gangrene” in name
Do they role up to “Gangrenous disorder”?
Do they appear in the data?
Requires source concept ids / values
J85.0
Gangrene and
necrosis of lung
4324261
Pulmonary
necrosis
Maps to
439928
Gangrenous
disorder
 
?
Proposed solution 6: Concept sets to source codes
Break down concept sets to implied source codes
Plot prevalence of source codes in data over time
Concept set
Standard concept
Source code
Drop in proportion when
switching from ICD-9 to 10
 
Proposed solution 7: Share study diagnostics
 
Propensity model + distribution
Covariate balance
Negative and positive control distribution
etc.
 
http://data.ohdsi.org/LegendBasicViewer/
 
Proposed solution 8: PheValuator
 
 
 
 
https://github.com/OHDSI/PheValuator
 
Proposed solution 9: Chart review
 
Time and resource intensive
Based on original charts or CDM data?
Formal or exploratory?
Summary
 
Network studies are vulnerable to
Technical issues
Source data issues
Data size issues
ETL issues
Mapping issues
Bias issues
Proposed solutions:
Testing at multiple sites
Dependency documentation
Achilles (Heel)
Cohort counts
Orphan code checks
Concept sets to source codes
Sharing study diagnostics
PheValuator
Chart review
 
 
Topics for next meeting
 
?
 
Next workgroup meeting
 
Eastern hemisphere: 
April 3
3pm Hong Kong / Taiwan
4pm South Korea
5:30pm Adelaide
9am Central European time
8am UK time
 
Western hemisphere: April 25
6pm Central European time
12pm New York
9am Los Angeles / Stanford
 
 
 
http://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:est-methods
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OHDSI Network Studies face challenges due to differences in health care systems and data capture processes across sites. Solutions include establishing a common Data Model and using the R programming language. Distributed Research Networks must coordinate efforts to ensure harmonization of data.

  • OHDSI
  • Network Studies
  • Data Model
  • Healthcare Systems
  • Coordination

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  1. OHDSI Network Studies Issues and possible solutions Martijn Schuemie

  2. Distributed Research Networks Data site A Same across sites: Common Data Model R Data site F Data site B Coordinating center Different: Health care system Data capture process Source coding systems ETL Database platform Data site E Data site C Data site D 2

  3. Possible issues when executing a network study Technical issues Edge cases E.g. error because cohort is empty Deviations from standard E.g. error because dates stored as varchar Bad testing E.g. code won t run at any site due to syntax errors Local tech problems E.g. weird issues with database configuration Dependency problems E.g. wrong version of FeatureExtraction installed

  4. Possible issues when executing a network study Source data issues E.g. physician specialty not recorded but required in cohort definition E.g. drug is not prescribed in country Data size issues E.g. not enough subjects to fit propensity model ETL issues E.g. observations derived from diagnoses are stored in condition_occurrence Mapping issues E.g. source code not mapped to standard concept Bias issues E.g channeling due to site-specific doctors preferences / guidelines

  5. Proposed solution 1: testing Always test on multiple databases before sending out to OHDSI network Make sure to test the code you ll be sending to others, not some other version. Yes, that means you ll probably be running the same study on your own data many times. (good thing: reproducible)

  6. Proposed solution 2: Dependency documentation Use OhdsiRTools::insertEnvironmentSnapshotInPackage Dependency enforcement: - Packrat - ?

  7. Proposed solution 3: Achilles (Heel) Run Achilles at every site Require Achilles Heel errors to be resolved Can detect deviations from standard and ETL issues Share: Heel report Whole of Achilles? Within study consortium or across all of OHDSI? How to link version between Achilles and study?

  8. Proposed solution 4: Cohort counts Compute counts per cohort per database Low stroke count due to inpatient visit requirement No glaucoma due to specialty requirement

  9. Proposed solution 5: Orphan code check For example Look for concepts with gangrene in name Do they role up to Gangrenous disorder ? Do they appear in the data? Requires source concept ids / values 439928 Gangrenous disorder ? J85.0 4324261 Pulmonary necrosis Gangrene and necrosis of lung Maps to

  10. Proposed solution 6: Concept sets to source codes Break down concept sets to implied source codes Plot prevalence of source codes in data over time switching from ICD-9 to 10 Drop in proportion when Concept set Standard concept Source code

  11. Proposed solution 7: Share study diagnostics Propensity model + distribution Covariate balance Negative and positive control distribution etc. http://data.ohdsi.org/LegendBasicViewer/

  12. Proposed solution 8: PheValuator https://github.com/OHDSI/PheValuator

  13. Proposed solution 9: Chart review Time and resource intensive Based on original charts or CDM data? Formal or exploratory?

  14. Summary Network studies are vulnerable to Technical issues Source data issues Data size issues ETL issues Mapping issues Bias issues Proposed solutions: Testing at multiple sites Dependency documentation Achilles (Heel) Cohort counts Orphan code checks Concept sets to source codes Sharing study diagnostics PheValuator Chart review

  15. Topics for next meeting ?

  16. Next workgroup meeting Eastern hemisphere: April 3 3pm Hong Kong / Taiwan 4pm South Korea 5:30pm Adelaide 9am Central European time 8am UK time Western hemisphere: April 25 6pm Central European time 12pm New York 9am Los Angeles / Stanford http://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:est-methods

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