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Intra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTHURIN, Nicolas
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorBOSCO-LEVY, Pauline
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorBLIN, Patrick
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorROUYER, Magali
dc.contributor.authorJOVE, Jeremy
dc.contributor.authorLAMARQUE, Stephanie
dc.contributor.authorLIGNOT, Severine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorLASSALLE, Regis
dc.contributor.authorABOUELFATH, Abdelilah
dc.contributor.authorBIGNON, Emmanuelle
dc.contributor.authorDIEZ, Pauline
dc.contributor.authorGROSS-GOUPIL, Marine
dc.contributor.authorSOULIE, Michel
dc.contributor.authorROUMIGUIE, Mathieu
dc.contributor.authorLE MOULEC, Sylvestre
dc.contributor.authorDEBOUVERIE, Marc
dc.contributor.authorBROCHET, Bruno
dc.contributor.authorGUILLEMIN, Francis
dc.contributor.authorLOUAPRE, Celine
dc.contributor.authorMAILLART, Elisabeth
dc.contributor.authorHEINZLEF, Olivier
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMOORE, Nicholas
dc.contributor.authorDROZ-PERROTEAU, Cecile
dc.date.accessioned2021-07-09T08:48:51Z
dc.date.available2021-07-09T08:48:51Z
dc.date.issued2021-05-01
dc.identifier.issn1471-2288en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/106486
dc.description.abstractEnBACKGROUND: Diagnosis performances of case-identifying algorithms developed in healthcare database are usually assessed by comparing identified cases with an external data source. When this is not feasible, intra-database validation can present an appropriate alternative. OBJECTIVES: To illustrate through two practical examples how to perform intra-database validations of case-identifying algorithms using reconstituted Electronic Health Records (rEHRs). METHODS: Patients with 1) multiple sclerosis (MS) relapses and 2) metastatic castration-resistant prostate cancer (mCRPC) were identified in the French nationwide healthcare database (SNDS) using two case-identifying algorithms. A validation study was then conducted to estimate diagnostic performances of these algorithms through the calculation of their positive predictive value (PPV) and negative predictive value (NPV). To that end, anonymized rEHRs were generated based on the overall information captured in the SNDS over time (e.g. procedure, hospital stays, drug dispensing, medical visits) for a random selection of patients identified as cases or non-cases according to the predefined algorithms. For each disease, an independent validation committee reviewed the rEHRs of 100 cases and 100 non-cases in order to adjudicate on the status of the selected patients (true case/ true non-case), blinded with respect to the result of the corresponding algorithm. RESULTS: Algorithm for relapses identification in MS showed a 95% PPV and 100% NPV. Algorithm for mCRPC identification showed a 97% PPV and 99% NPV. CONCLUSION: The use of rEHRs to conduct an intra-database validation appears to be a valuable tool to estimate the performances of a case-identifying algorithm and assess its validity, in the absence of alternative.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enValidation study
dc.subject.enCase-identifying algorithm
dc.subject.enClaims database
dc.subject.enReconstituted electronic health record
dc.subject.enMultiple sclerosis
dc.subject.enProstate Cancer
dc.subject.enPositive predictive value
dc.subject.enNegative predictive value
dc.title.enIntra-database validation of case-identifying algorithms using reconstituted electronic health records from healthcare claims data
dc.typeArticle de revueen_US
dc.identifier.doi10.1186/s12874-021-01285-yen_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed33933001en_US
bordeaux.journalBMC Medical Research Methodologyen_US
bordeaux.page95en_US
bordeaux.volume21en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamPharmacoEpi-Drugsen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
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