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Generation and validation of a classification model to diagnose familial hypercholesterolaemia in adults

2023-10Journal article Restricted access
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dc.contributor.authorAlbuquerque, João
dc.contributor.authorMedeiros, Ana Margarida
dc.contributor.authorAlves, Ana Catarina
dc.contributor.authorJannes, Cinthia Elim
dc.contributor.authorMancina, Rosellina M.
dc.contributor.authorPavanello, Chiara
dc.contributor.authorChora, Joana Rita
dc.contributor.authorMombelli, Giuliana
dc.contributor.authorCalabresi, Laura
dc.contributor.authorPereira, Alexandre da Costa
dc.contributor.authorKrieger, José Eduardo
dc.contributor.authorRomeo, Stefano
dc.contributor.authorBourbon, Mafalda
dc.contributor.authorAntunes, Marília
dc.date.accessioned2023-11-15T12:05:00Z
dc.date.available2023-11-15T12:05:00Z
dc.date.issued2023-10
dc.description.abstractBackground and aims: The early diagnosis of familial hypercholesterolaemia is associated with a significant reduction in cardiovascular disease (CVD) risk. While the recent use of statistical and machine learning algorithms has shown promising results in comparison with traditional clinical criteria, when applied to screening of potential FH cases in large cohorts, most studies in this field are developed using a single cohort of patients, which may hamper the application of such algorithms to other populations. In the current study, a logistic regression (LR) based algorithm was developed combining observations from three different national FH cohorts, from Portugal, Brazil and Sweden. Independent samples from these cohorts were then used to test the model, as well as an external dataset from Italy. Methods: The area under the receiver operating characteristics (AUROC) and precision-recall (AUPRC) curves was used to assess the discriminatory ability among the different samples. Comparisons between the LR model and Dutch Lipid Clinic Network (DLCN) clinical criteria were performed by means of McNemar tests, and by the calculation of several operating characteristics. Results: AUROC and AUPRC values were generally higher for all testing sets when compared to the training set. Compared with DLCN criteria, a significantly higher number of correctly classified observations were identified for the Brazilian (p < 0.01), Swedish (p < 0.01), and Italian testing sets (p < 0.01). Higher accuracy (Acc), G mean and F1 score values were also observed for all testing sets. Conclusions: Compared to DLCN criteria, the LR model revealed improved ability to correctly classify observations, and was able to retain a similar number of FH cases, with less false positive retention. Generalization of the LR model was very good across all testing samples, suggesting it can be an effective screening tool if applied to different populations.pt_PT
dc.description.abstractHighlights: Early diagnosis of familial hypercholesterolemia is associated with a significant reduction in cardiovascular disease risk; The development of a multi-cohort classification model can allow for better generalization of results; Compared to traditional clinical criteria, accuracy was higher with the developed classification model; Furthermore, sensitivity is not compromised with this model.pt_PT
dc.description.sponsorshipThe current work was supported by the programme Norte2020 (operação NORTE-08-5369-FSE-000018) and by Fundação para a Ciência e Tecnologia (FCT), under the projects UID/MAT/00006/2019 and PTDC/SAU-SER/29180/2017.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationAtherosclerosis. 2023 Oct:383:117314. doi: 10.1016/j.atherosclerosis.2023.117314. Epub 2023 Sep 28.pt_PT
dc.identifier.doi10.1016/j.atherosclerosis.2023.117314pt_PT
dc.identifier.issn0021-9150
dc.identifier.urihttp://hdl.handle.net/10400.18/8767
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationCentre of Statistics and its Applications
dc.relationDyslipidaemia stratification : new screening tools for a cost effective approach
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0021915023052358?via%3Dihubpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectLogistic Regressionpt_PT
dc.subjectDutch Lipid Clinic Network Criteriapt_PT
dc.subjectValidationpt_PT
dc.subjectFamilial Hypercholesterolaemiapt_PT
dc.subjectDoenças Cardio e Cérebro-vascularespt_PT
dc.titleGeneration and validation of a classification model to diagnose familial hypercholesterolaemia in adultspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre of Statistics and its Applications
oaire.awardTitleDyslipidaemia stratification : new screening tools for a cost effective approach
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FMAT%2F00006%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FSAU-SER%2F29180%2F2017/PT
oaire.citation.startPage117314pt_PT
oaire.citation.titleAtherosclerosispt_PT
oaire.citation.volume383pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.embargofctAcesso de acordo com a política editorial da revistapt_PT
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isProjectOfPublicationd9faf814-0ee6-4214-92c8-4fd692956da5
relation.isProjectOfPublicationcde71551-1825-44a0-bc4d-e85c0e2955c8
relation.isProjectOfPublication.latestForDiscoverycde71551-1825-44a0-bc4d-e85c0e2955c8

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