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Developing a Standards-based Signal Detection and Validation Framework of Immune-related Adverse Events using OHDSI Comm

This study aims to develop a standardized framework for detecting and validating immune-related adverse events (irAEs) using the OHDSI Common Data Model. The framework incorporates real-world data mining, text mining, and validation to improve the detection of unique irAEs associated with immunotherapy for cancer treatment.

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Developing a Standards-based Signal Detection and Validation Framework of Immune-related Adverse Events using OHDSI Comm

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  1. Developing A Standards-based Signal Detection and Validation Framework of Immune-related Adverse Events Using the OHDSI Common Data Model Yue Yu, PhD, Kathryn J. Ruddy, MD, MPH, Shintaro Tsuji, PhD, Na Hong, PhD, Nilay Shah, PhD, Guoqian Jiang, MD, PhD MayoClinic

  2. Background • Immunotherapyhas recently been found to be useful in the treatment of certain cancers. • However, immunotherapy for cancer has been associated with unique immune-related adverse events (irAEs). PictureSource:https://www.nobelprize.org/

  3. Background irAEsdetection • Real-worlddata; • 6monoclonal antibodies(mAbs);

  4. Background • Lack of standardization makes it hard to interpret the real-worldsafety data.

  5. Background • OHDSI CommonDataModel(CDM) • Transforming data contained within different databases into: • a common format (data model)and • a common representation(terminologies,vocabularies, coding schemes); PictureSource:https://www.ohdsi.org

  6. Objective • 1.BuildingastandardizedadversedrugreactiondetectiondataplatformbasedonOHDSICDM; • 2.Developingacomprehensive(real-worlddatamining+textmining+validation)irAEsdetectionframework.

  7. Materials 1)Datastandardization&irAEsdatamining: • FDAAdverseEventReportingSystem(FAERS): • a database that contains adverse event reports, medication error reports and product quality complaints resulting in adverse events that were submitted to FDA.  • Widelyusedinpost-marketing safety surveillancebyFDAandresearchcommunities. • ADEpedia-on-OHDSI platform: • Trytointegratea spontaneous reporting system data(suchasFAERS)andlongitudinal observational databases (suchas Electronic Health Records,EHRs) • An extraction, transformation and loading(ETL)tooltocovertFAERSdatabaseintotheOHDSICDMformat. • https://github.com/adepedia/adepedia-on-ohdsi

  8. Materials • 2) irAEstext-mining: • Drug Labels: • 6mAbsdruglabelsinStructured Product Labeling (SPL)formatdownloadfromDailyMed. • Publication: • 679 PubMed abstractsregardingwithmAbs-irAEs. • cTAKES: • discovers clinical-named entities and clinical events using a dictionary lookup algorithm and a subset of the UMLS. • Common Terminology Criteria for Adverse Events (CTCAE)terminologywasusedasdictionarytoextractirAEsconceptsfromtext.

  9. Methods:1) FAERSData Standardization Data Cleaning and Drug Name Mapping Structure Mappings between FAERS and OHDSI CDM Data ETL Implementation Reference:Banda JM, etal. A curated and standardized adverse drug event resource to accelerate drug safety research. Sci Data, 2016. 3: p. 160026.

  10. Methods:1) FAERSData Standardization • Databasestructuremappingintablelevel.

  11. Methods:2) irAEs Detection • WedevelopedairAEsdetectionframeworkwhichcontains3modules: • 1)irAEsdatamining Module; • 2)Text-mining Module; • 3)Validation Module;

  12. irAEsdatamining Module • Datasource:OHDSICDMbasedFAERS; • Algorithm： ReportingOddsRatio(ROR)； • Positivesignal:When the case number ≥ 3and the RORlower limit of 95% CI > 1, the signal is considered as a positive irAEs signal.

  13. Text-mining Module • Text-miningprocess:

  14. Validation Module

  15. Result 1) ETL result • Evaluationofinformation loss duringETLprocesswillshowinanotherpaper. Basic Statistics of OHDSI CDM Tables after ETL Process.

  16. Result • 2) irAEsdatamining result based on FAERS

  17. Result • 2) irAEs detection result • 174labeledsignals • 101unlabeledpublishedsignals • 43newsignals

  18. Discussion &Conclusion Conclusion • 1)Weutilized standard OHDSI CDM to represent the FAERS data. • 2)Wedevelopedastandards-based signal detection and validation frameworktodetectirAEssignals. • Discussion • 1)MoreEHRdataandanalysistoolswillbeintegratedintoourADEpedia-on-OHDSI platforminfuture. • 2)Forthetext-miningperformance,weconducttheevaluation ofCTCAEfor capturing irAEsinanotherpaper. • 3)Infuturestudy,wewillalsoperform afurtherhuman expert review to check whether thosenewly identified signalsare real new signals or just synonyms/subtypes.

  19. Thank you! Email me at: Yu.Yue1@mayo.edu Jiang.Guoqian@mayo.edu

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