Mostrar el registro sencillo del ítem

Predicting Progression to Advanced Age-Related Macular Degeneration from Clinical, Genetic, and Lifestyle Factors Using Machine Learning

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorAJANA, Soufiane
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCOUGNARD-GREGOIRE, Audrey
dc.contributor.authorCOLIJN, J. M.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMERLE, Benedicte
dc.contributor.authorVERZIJDEN, T.
dc.contributor.authorDE JONG, Ptvm
dc.contributor.authorHOFMAN, A.
dc.contributor.authorVINGERLING, J. R.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHEJBLUM, Boris
ORCID: 0000-0003-0646-452X
IDREF: 189970316
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorKOROBELNIK, Jean-Francois
ORCID: 0000-0002-4438-9535
IDREF: 028739272
dc.contributor.authorMEESTER-SMOOR, M. A.
dc.contributor.authorUEFFING, M.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorJACQMIN-GADDA, Helene
dc.contributor.authorKLAVER, C. C. W.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDELCOURT, Cecile
ORCID: 0000-0002-2099-0481
IDREF: 035105291
dc.contributor.authorCONSORTIUM, Eye-Risk
dc.date.accessioned2021-03-22T10:22:07Z
dc.date.available2021-03-22T10:22:07Z
dc.date.issued2020-09-02
dc.identifier.issn1549-4713 (Electronic) 0161-6420 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/26758
dc.description.abstractEnOBJECTIVE: Current prediction models for advanced age-related macular degeneration (AMD) are based on a restrictive set of risk factors. The objective of this study was to develop a comprehensive prediction model, applying a machine learning algorithm allowing selection of the most predictive risk factors automatically. DESIGN: Two population-based cohort studies PARTICIPANTS: The Rotterdam Study I (RS-I, training set) included 3838 participants aged 55 years or more, with a median follow-up period of 10.8 years and 108 incident cases of advanced AMD. The ALIENOR study (test set) included 362 participants aged 73 years or more, with a median follow-up period of 6.5 years and 33 incident cases of advanced AMD. METHODS: The prediction model used the bootstrap lasso for survival analysis to select the best predictors of incident advanced AMD in the training set. Predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). MAIN OUTCOME MEASURES: incident advanced AMD (atrophic and/or neovascular), based on standardized interpretation of retinal photographs. RESULTS: The prediction model retained i) age, ii) a combination of phenotypic predictors (based on the presence of intermediate drusen, hyper-pigmentation in one or both eyes and age-related eye disease study (AREDS) simplified score), iii) a summary genetic risk score based on 49 single nucleotide polymorphisms, iv) smoking, v) diet quality, vi) education, and vii) pulse pressure. The cross-validated AUC estimation in RS-I was 0.92 [0.88-0.97] at 5 years, 0.92 [0.90-0.95] at 10 years and 0.91 [0.88-0.94] at 15 years. In ALIENOR, the AUC reached 0.92 at 5 years [0.87-0.98]. In terms of calibration, the model tended to underestimate the cumulative incidence of advanced AMD for the high-risk groups, especially in ALIENOR. CONCLUSIONS: This prediction model reached high discrimination abilities, paving the way towards making precision medicine for AMD patients a reality in the near future.
dc.language.isoENen_US
dc.title.enPredicting Progression to Advanced Age-Related Macular Degeneration from Clinical, Genetic, and Lifestyle Factors Using Machine Learning
dc.title.alternativeOphthalmologyen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.ophtha.2020.08.031en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed32890546en_US
bordeaux.journalOphthalmology: Journal of The American Academy of Ophthalmologyen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.teamSISTM_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.exportfalse
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Ophthalmology:%20Journal%20of%20The%20American%20Academy%20of%20Ophthalmology&rft.date=2020-09-02&rft.eissn=1549-4713%20(Electronic)%200161-6420%20(Linking)&rft.issn=1549-4713%20(Electronic)%200161-6420%20(Linking)&rft.au=AJANA,%20Soufiane&COUGNARD-GREGOIRE,%20Audrey&COLIJN,%20J.%20M.&MERLE,%20Benedicte&VERZIJDEN,%20T.&rft.genre=article


Archivos en el ítem

ArchivosTamañoFormatoVer

No hay archivos asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem