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Decision-making support systems on extended hospital length of stay

2023-02-08Artigo científico Acesso aberto
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dc.contributor.authorXavier, Joana
dc.contributor.authorSeringa, Joana
dc.contributor.authorPinto, Fausto José
dc.contributor.authorMagalhães, Teresa
dc.contributor.institutionEscola Nacional de Saúde Pública (ENSP)
dc.contributor.institutionComprehensive Health Research Centre (CHRC) - Pólo ENSP
dc.contributor.institutionCentro de Investigação em Saúde Pública (CISP/PHRC)
dc.contributor.pblFrontiers Media
dc.date.accessioned2023-04-18T22:20:53Z
dc.date.available2023-04-18T22:20:53Z
dc.date.issued2023-02-08
dc.descriptionFunding Information: This study was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020). Publisher Copyright: Copyright © 2023 Xavier, Seringa, Pinto and Magalhães.
dc.description.abstractBackground: Cardiovascular diseases are still a significant cause of death and hospitalization. In 2019, circulatory diseases were responsible for 29.9% of deaths in Portugal. These diseases have a significant impact on the hospital length of stay. Length of stay predictive models is an efficient way to aid decision-making in health. This study aimed to validate a predictive model on the extended length of stay in patients with acute myocardial infarction at the time of admission. Methods: An analysis was conducted to test and recalibrate a previously developed model in the prediction of prolonged length of stay, for a new set of population. The study was conducted based on administrative and laboratory data of patients admitted for acute myocardial infarction events from a public hospital in Portugal from 2013 to 2015. Results: Comparable performance measures were observed upon the validation and recalibration of the predictive model of extended length of stay. Comorbidities such as shock, diabetes with complications, dysrhythmia, pulmonary edema, and respiratory infections were the common variables found between the previous model and the validated and recalibrated model for acute myocardial infarction. Conclusion: Predictive models for the extended length of stay can be applied in clinical practice since they are recalibrated and modeled to the relevant population characteristics.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent195940
dc.identifier.doi10.3389/fmed.2023.907310
dc.identifier.issn2296-858X
dc.identifier.otherPURE: 58571478
dc.identifier.otherPURE UUID: ab46e241-1df2-4ec9-bbbb-5415e6ed37ba
dc.identifier.otherScopus: 85148585440
dc.identifier.otherWOS: 000934728200001
dc.identifier.otherPubMed: 36844231
dc.identifier.otherPubMedCentral: PMC9946108
dc.identifier.otherORCID: /0000-0003-3794-1659/work/133365089
dc.identifier.urihttp://hdl.handle.net/10362/151912
dc.identifier.urlhttps://www.scopus.com/pages/publications/85148585440
dc.language.isoeng
dc.peerreviewedyes
dc.subjectacute myocardial infarction
dc.subjectcardiovascular diseases
dc.subjectdecision-making
dc.subjectlength of stay
dc.subjectpredictive models
dc.subjectGeneral Medicine
dc.subjectSDG 3 - Good Health and Well-being
dc.titleDecision-making support systems on extended hospital length of stayen
dc.title.subtitlevalidation and recalibration of a model for patients with AMIen
dc.typejournal article
degois.publication.titleFrontiers in medicine
degois.publication.volume10
dspace.entity.typePublication
rcaap.rightsopenAccess

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