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Neonatal cholestasis : development of a diagnostic decision algorithm from multivariate predictive models

2021Journal article Restricted access
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dc.contributor.authorSantos Silva, Ermelinda
dc.contributor.authorMoreira Silva, Helena
dc.contributor.authorCatarino, Cristina
dc.contributor.authorDias, Cláudia Camila
dc.contributor.authorSantos Silva, Alice
dc.contributor.authorLopes, Ana Isabel
dc.date.accessioned2021-01-08T14:02:38Z
dc.date.available2021-01-08T14:02:38Z
dc.date.issued2021
dc.description© Springer-Verlag GmbH Germany, part of Springer Nature 2021pt_PT
dc.description.abstractDespite the recent advances involving molecular studies, the neonatal cholestasis (NC) diagnosis still relays on the expertise of medical teams. Our aim was to develop models of etiological diagnosis and unfavourable prognosis which may support a rationale diagnostic approach. We retrospectively analysed 154 patients born between January 1985 and October 2019. The cohort was divided into two main groups: (A) transient cholestasis and (B) other diagnosis (with subgroups) and also in two groups of outcomes: (I) unfavourable and (II) favourable. Multivariate logistic regression analysis identified the lower gestational age as the only variable independently associated with an increased risk of transient cholestasis and signs and/or symptoms of sepsis with infectious or metabolic diseases. Gamma-glutamyl transferase serum levels > 300 IU/L had a positive predictive value for both diagnosis of biliary atresia and for alpha-1-antitrypsin deficiency (A1ATD) and for unfavourable prognosis. A model of diagnosis for A1ATD (n = 34) showed an area under the ROC curve = 0.843 [confidence interval (CI): 0.773-0.912].Conclusion: This study identified some predictors of diagnosis and prognosis which helped to build a diagnostic decision algorithm. The unusually large subgroup of patients with A1ATD in this cohort emphasizes its predictive diagnostic model. What Is Known • The etiological diagnosis of neonatal cholestasis (NC) requires a step-by-step guided approach, and diagnostic models have been developed only for biliary atresia. • Current algorithms neither address the epidemiology changes nor the application of the new molecular diagnostic tools. What Is New • This study provides diagnostic predictive models for patients with A1ATD, metabolic/infectious diseases, and transient cholestasis, and two models of unfavourable prognosis for NC. • A diagnostic decision algorithm is proposed based on this study, authors expertise and the literature.pt_PT
dc.description.sponsorshipThis work was supported by a PhD scholarship from the Department of Education, Training, and Research of the Centro Hospitalar Universitário do Porto, and by Applied Molecular Biosciences Unit (UCIBIO), which is financed by national funds from FCT/MCTES (UID/MULTI/04378/2019).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationEuropean Journal of Pediatrics (2021)pt_PT
dc.identifier.doi10.1007/s00431-020-03886-zpt_PT
dc.identifier.eissn1432-1076
dc.identifier.issn0340-6199
dc.identifier.urihttp://hdl.handle.net/10451/45711
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relation.publisherversionhttps://www.springer.com/journal/431/pt_PT
dc.subjectAlpha-1-antitrypsin deficiencypt_PT
dc.subjectBiliary atresiapt_PT
dc.subjectDiagnostic decision algorithmpt_PT
dc.subjectMultivariate prediction modelspt_PT
dc.subjectNeonatal cholestasispt_PT
dc.subjectTransient neonatal cholestasispt_PT
dc.titleNeonatal cholestasis : development of a diagnostic decision algorithm from multivariate predictive modelspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FMulti%2F04378%2F2013/PT
oaire.citation.titleEuropean Journal of Pediatricspt_PT
oaire.fundingStream5876
person.familyNameRamalho Santos Silva
person.givenNameErmelinda
person.identifier.ciencia-id7010-0876-5066
person.identifier.orcid0000-0002-0987-341X
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscovery9ed3e912-7db6-4857-9156-4dec588dd653
relation.isProjectOfPublicationaaa5b3a4-66df-4968-966c-2a7b18d63b67
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