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dc.contributor.authorLea-Pereira, María Carmen
dc.contributor.authorAmaya Pascasio, Laura
dc.contributor.authorMartínez Sánchez, Patricia 
dc.contributor.authorRodríguez Salvador, María del Mar 
dc.contributor.authorGalván-Espinosa, José
dc.contributor.authorTéllez-Ramírez, Luis
dc.contributor.authorReche Lorite, Fernando 
dc.contributor.authorSánchez, María-José
dc.contributor.authorGarcía-Torrecillas, Juan Manuel
dc.date.accessioned2022-03-10T18:05:56Z
dc.date.available2022-03-10T18:05:56Z
dc.date.issued2022-03-08
dc.identifier.issn1660-4601
dc.identifier.urihttp://hdl.handle.net/10835/13431
dc.description.abstractBackground: Stroke is the second cause of mortality worldwide and the first in women. The aim of this study is to develop a predictive model to estimate the risk of mortality in the admission of patients who have not received reperfusion treatment. Methods: A retrospective cohort study was conducted of a clinical–administrative database, reflecting all cases of non-reperfused ischaemic stroke admitted to Spanish hospitals during the period 2008–2012. A predictive model based on logistic regression was developed on a training cohort and later validated by the “hold-out” method. Complementary machine learning techniques were also explored. Results: The resulting model had the following nine variables, all readily obtainable during initial care. Age (OR 1.069), female sex (OR 1.202), readmission (OR 2.008), hypertension (OR 0.726), diabetes (OR 1.105), atrial fibrillation (OR 1.537), dyslipidaemia (0.638), heart failure (OR 1.518) and neurological symptoms suggestive of posterior fossa involvement (OR 2.639). The predictability was moderate (AUC 0.742, 95% CI: 0.737–0.747), with good visual calibration; Pearson’s chi-square test revealed non-significant calibration. An easily consulted risk score was prepared. Conclusions: It is possible to create a predictive model of mortality for patients with ischaemic stroke from which important advances can be made towards optimising the quality and efficiency of care. The model results are available within a few minutes of admission and would provide a valuable complementary resource for the neurologist.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectpredictive modeles_ES
dc.subjectrisk scorees_ES
dc.subjectmortalityes_ES
dc.subjectstrokees_ES
dc.subjectvascular neurologyes_ES
dc.titlePredictive Model and Mortality Risk Score during Admission for Ischaemic Stroke with Conservative Treatmentes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/1660-4601/19/6/3182es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/ijerph19063182


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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