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dc.contributor.authorDrazdienė, Nijolė
dc.contributor.authorBagdonienė, Rasa
dc.contributor.authorSirtautienė, Rasa
dc.contributor.authorVezbergienė, Nijolė
dc.contributor.authorSliesoraitienė, Viktorija
dc.contributor.authorSliesoraitytė, Ieva
dc.date.accessioned2023-09-18T20:28:35Z
dc.date.available2023-09-18T20:28:35Z
dc.date.issued2006
dc.identifier.issn1392-0138
dc.identifier.other(BIS)VUB02-000025391
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/150119
dc.description.abstractBackground: Retinopathy of prematurity (ROP) might be prevented by a timely diagnosis and appropriate treatment applied according to the risk factors. The goal of our research was to combine the optimal clinical and epidemiological indicators for ROP risk detection. Materials and methods: A retrospective observational research was carried out at Clinic of Neonatology of Vilnius University Children’s Hospital. The research combined examination of epidemiological and clinical characteristics of premature neonates born in 2005 and analysis of ROP protocols. Multifactorial risk environment for ROP pathological process development was elucidated. Results: The infants’ age at ROP onset was four to six weeks. Their mean gestational age was 28.1 ± 0.9 weeks, the mean birth weight being 1250 ± 214 g. Infants at the greatest risk of ROP weighed 1500 g or less at birth (p < 0.001), and their gestational age was 30 weeks or less (p < 0.05). An inverse relationship was found between the incidence and severity of ROP and birth weight and gestational age (r1 = -0.8; r2 = -0.7; p < 0.01). Low Apgar scale rates were positively associated with ROP stage and accordingly with a high ROP risk level (r3 = 0.9; p < 0.001). Delivery and pregnancy failure increase the ROP pattern as following infections and bleeding during delivery are the leading pathological status elevating ROP stage. Oxygen therapy had been applied in all ROP stages. Concomitant neonate pathologies range within a moderate to high risk level for ROP development (p < 0.001). Conclusions: The tool based on artificial neural networks has the potentiality of identifying the risk level for ROP development with high sensitivity considering positive and negative predictive values.eng
dc.format.extentp. 141-146
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyCurrent Abstracts
dc.relation.isreferencedbyTOC Premier
dc.relation.isreferencedbyCurrent Abstracts (EBSCO)
dc.relation.isreferencedbyIndex Copernicus
dc.rightsLaisvai prieinamas internete
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:6172459/datastreams/ATTACHMENT_6172473/content
dc.source.urihttps://talpykla.elaba.lt/elaba-fedora/objects/elaba:6172459/datastreams/MAIN/content
dc.titleMultifactorial risk environment for retinopathy of prematurity
dc.title.alternativeDaugiafaktorinė aplinkos rizika neišnešiotų naujagimių retinopatijai
dc.typeStraipsnis kitoje DB / Article in other DB
dcterms.references32
dc.type.pubtypeS3 - Straipsnis kitoje DB / Article in other DB
dc.contributor.institutionVilniaus universitetas
dc.contributor.institutionVilniaus universiteto ligoninės Santariškių klinikos
dc.contributor.institutionVilniaus Gedimino technikos universitetas Vilniaus universiteto ligoninės Santariškių klinikos
dc.contributor.institutionKauno medicinos universitetas
dc.contributor.facultyMechanikos fakultetas / Faculty of Mechanics
dc.subject.researchfieldM 001 - Medicina / Medicine
dc.subject.ltNeišnešioti naujagimiai
dc.subject.ltretinopatija
dc.subject.ltVaikų oftalmologija
dc.subject.enRetinopathy of prematurity
dc.subject.enRisk factors
dc.subject.enArtificial neural networks
dcterms.sourcetitleActa medica Lituanica
dc.description.issueno. 3
dc.description.volumevol. 13
dc.identifier.doiVUB02-000025391
dc.identifier.doiELB01-000006103
dc.identifier.doiLT-eLABa-0001:J.04~2006~ISSN_1392-0138.V_13.N_3.PG_141-146
dc.identifier.doiLBT02-000020872
dc.identifier.elaba6172459


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