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dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorSPINI, Andrea
dc.contributor.authorROSELLINI, Pietro
dc.contributor.authorBELLAN, Cristiana
dc.contributor.authorFURIESI, Folco
dc.contributor.authorGIORGI, Silvano
dc.contributor.authorDONNINI, Sandra
dc.contributor.authorGINI, Rosa
dc.contributor.authorZICHE, Marina
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorSALVO, Francesco
dc.contributor.authorROBERTO, Giuseppe
dc.date.accessioned2022-10-19T10:00:38Z
dc.date.available2022-10-19T10:00:38Z
dc.date.issued2022-06-08
dc.identifier.issn1932-6203 (Electronic) 1932-6203 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/170072
dc.description.abstractEnPURPOSE: To develop and validate a case-finding algorithm for the identification of Non-Small Cell Lung Cancer (NSCLC) cases in a region-wide Italian pathology registry (PR). MATERIALS AND METHODS: Data collected between 2009 and 2017 in the PR and the Pharmacy Database of the University Hospital of Siena and the PR of Tuscany region were used. A NSCLC-identification algorithm based on free-text keywords and SNOMED morphology and topography codes was designed and tested on data from Siena: indication for drug use (i.e. NSCLC) was the reference standard for sensitivity (SE); positive predictive value (PPV) was estimated through manual review. Algorithm modifications were then tested to improve algorithm performance: PPV was calculated against validated dataset from PR of Siena; a range of SE [min-max] was estimated in PR of Tuscany using analytical formulae that assumed NSCLC incidence equal either to 80% or 90% of overall lung cancer incidence recorded in Tuscany. The algorithm modification with the best performance was chosen as the final version of the algorithm. A random sample of 200 cases was extracted from the PR of Tuscany for manual review. RESULTS: The first version of the algorithm showed a PPV of 74.7% and SE of 79% in PR of Siena. The final version of the algorithm had a SE in PR of Tuscany that grew with calendar time (2009 = [24.7%-28%]; 2017 = [57.9%-65.1%]) and a PPV of 93%. CONCLUSIONS: The final NSCLC-finding algorithm showed with very high PPV. SE was in line with the expected contribution of PR to overall cases captured in the regional Cancer Registry, with a trend of increase over calendar time. Given the promising algorithm validity and the wide use of SNOMED terminology in electronic pathology records, the proposed algorithm is expected to be easily adapted to other electronic databases for (pharmaco)epidemiology purposes.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.title.enDevelopment and validation of a case-finding algorithm for the identification of non-small cell lung cancers in a region-wide Italian pathology registry
dc.typeArticle de revueen_US
dc.identifier.doi10.1371/journal.pone.0269232en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed35675338en_US
bordeaux.journalPLoS ONEen_US
bordeaux.volume17en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue6en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamAHEAD_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03820839
hal.version1
hal.date.transferred2022-10-19T10:00:41Z
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
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