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dc.rights.licenseopenen_US
dc.contributor.authorLAHBIB, Hana
dc.contributor.authorMANDEREAU-BRUNO, Laurence
dc.contributor.authorGORIA, Sarah
dc.contributor.authorWAGNER, Verene
dc.contributor.authorTORRES, Marion J
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorFEART, Catherine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHELMER, Catherine
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPERES, Karine
dc.contributor.authorCARCAILLON-BENTATA, Laure
dc.date.accessioned2025-05-20T12:20:40Z
dc.date.available2025-05-20T12:20:40Z
dc.date.issued2025-04-02
dc.identifier.issn2045-2322en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/206666
dc.description.abstractEnThis study aimed to build a predictive model to identify frailty in the French national health data system (SNDS) so as to create a new tool to monitor and anticipate the disability burden associated with population ageing. We developed the model using the 2012 wave of the French Health, Healthcare, and Insurance Survey (ESPS) linked to the SNDS (n = 2,829). This survey used Fried's frailty phenotype as the gold standard. We compared two statistical approaches - logistic regressions (stepwise and LASSO selection) and random forest - to predict frailty probability based on different SNDS healthcare claims. We indirectly validated the model by comparing (1) the predicted frailty prevalence in the overall French population in the SNDS with the expected prevalence and (2) the predictive ability of the model for 6-year mortality with that of Fried's frailty phenotype. Logistic regression with LASSO selection was retained as the best method to predict frailty. After stratification for age, we obtained three models for individuals aged 55-64, 65-74, and ≥ 75 years (AUC = 0.61, 0.76, and 0.80 respectively). Applying these models to the SNDS, frailty prevalence was comparable to expected prevalence in all sex and age groups: overall prevalence = 12.9% (95%CI: 12.9-12.9) in the SNDS versus 12.0% (95%CI: 10.8-13.2) in the ESPS. Predicted frailty probabilities in the SNDS showed a similar strong association with 6-year mortality (HR(frail_probability)=2.6, 95%CI: 1.9-3.5) compared with Fried's phenotype (HR(frail_phenotype)=2.9, 95%CI: 2.1-3.8). Our predictive models are thus useful for estimating frailty probability in the SNDS.
dc.language.isoENen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subject.enFrench national health data system
dc.subject.enAlgorithm
dc.subject.enPredictive model
dc.subject.enFrailty
dc.title.enDevelopment and indirect validation of a model predicting frailty in the French healthcare claims database
dc.title.alternativeSci Repen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1038/s41598-025-95629-zen_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed40175586en_US
bordeaux.journalScientific Reportsen_US
bordeaux.page11344en_US
bordeaux.volume15en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamACTIVE_BPHen_US
bordeaux.teamLEHA_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-05075462
hal.version1
hal.date.transferred2025-05-20T12:21:33Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Scientific%20Reports&rft.date=2025-04-02&rft.volume=15&rft.issue=1&rft.spage=11344&rft.epage=11344&rft.eissn=2045-2322&rft.issn=2045-2322&rft.au=LAHBIB,%20Hana&MANDEREAU-BRUNO,%20Laurence&GORIA,%20Sarah&WAGNER,%20Verene&TORRES,%20Marion%20J&rft.genre=article


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