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Predictors of in-hospital mortality in stroke patients

In-hospital mortality is a good indicator to assess the efficacy of stroke care. Identifying the predictors of in-hospital mortality is important to advance the stroke outcome and plan the future strategies of stroke management. This was a prospective cohort study conducted at a tertiary referral ce...

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Autores principales: Ranasinghe, Vindya Shalini, Pathirage, Manoji, Gawarammana, Indika Bandara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021536/
https://www.ncbi.nlm.nih.gov/pubmed/36962904
http://dx.doi.org/10.1371/journal.pgph.0001278
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author Ranasinghe, Vindya Shalini
Pathirage, Manoji
Gawarammana, Indika Bandara
author_facet Ranasinghe, Vindya Shalini
Pathirage, Manoji
Gawarammana, Indika Bandara
author_sort Ranasinghe, Vindya Shalini
collection PubMed
description In-hospital mortality is a good indicator to assess the efficacy of stroke care. Identifying the predictors of in-hospital mortality is important to advance the stroke outcome and plan the future strategies of stroke management. This was a prospective cohort study conducted at a tertiary referral center in Sri Lanka to identify the possible predictors of in-hospital mortality. The study included 246 confirmed stroke patients. The diagnosis of stroke was established on the clinical history, examination and neuroimaging. The differentiation of stroke in to haemorrhagic type and ischaemic type was based on the results of computed tomography. In all patients, demographic data, comorbidities, clinical signs (pulse rate, respiratory rate, systolic blood pressure, diastolic blood pressure, on admission Glasgow Coma Scale (GCS) score) and imaging findings were recorded. All patients were followed up throughout their hospital course and the in-hospital mortality was recorded. In hospital mortality was defined as the deaths which occurred due to stroke after 24 hours of hospital admission. The incidence of in-hospital mortality was 11.7% (95% confidence interval: 8–16.4). The mean day of in-hospital deaths to occur was 5.9 days (SD ± 3.8 Min 2 Max 20). According to multivariate logistic regression analysis on admission GCS score (Odds Ratio (OR)-0.71) and haemorrhagic stroke type (OR-5.12) predict the in-hospital mortality. The area under the curve of receiver operating curve drawn for the on admission GCS score was 0.78 with a sensitivity of 96.31% and specificity of 41.38% for a patient presented with the GCS score of <10. On admission GCS and haemorrhagic stroke are independent predictors of in-hospital mortality. Thus, a special attention should be given to the patients with low GCS score and haemorrhagic strokes for reducing rates of in-hospital mortality.
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spelling pubmed-100215362023-03-17 Predictors of in-hospital mortality in stroke patients Ranasinghe, Vindya Shalini Pathirage, Manoji Gawarammana, Indika Bandara PLOS Glob Public Health Research Article In-hospital mortality is a good indicator to assess the efficacy of stroke care. Identifying the predictors of in-hospital mortality is important to advance the stroke outcome and plan the future strategies of stroke management. This was a prospective cohort study conducted at a tertiary referral center in Sri Lanka to identify the possible predictors of in-hospital mortality. The study included 246 confirmed stroke patients. The diagnosis of stroke was established on the clinical history, examination and neuroimaging. The differentiation of stroke in to haemorrhagic type and ischaemic type was based on the results of computed tomography. In all patients, demographic data, comorbidities, clinical signs (pulse rate, respiratory rate, systolic blood pressure, diastolic blood pressure, on admission Glasgow Coma Scale (GCS) score) and imaging findings were recorded. All patients were followed up throughout their hospital course and the in-hospital mortality was recorded. In hospital mortality was defined as the deaths which occurred due to stroke after 24 hours of hospital admission. The incidence of in-hospital mortality was 11.7% (95% confidence interval: 8–16.4). The mean day of in-hospital deaths to occur was 5.9 days (SD ± 3.8 Min 2 Max 20). According to multivariate logistic regression analysis on admission GCS score (Odds Ratio (OR)-0.71) and haemorrhagic stroke type (OR-5.12) predict the in-hospital mortality. The area under the curve of receiver operating curve drawn for the on admission GCS score was 0.78 with a sensitivity of 96.31% and specificity of 41.38% for a patient presented with the GCS score of <10. On admission GCS and haemorrhagic stroke are independent predictors of in-hospital mortality. Thus, a special attention should be given to the patients with low GCS score and haemorrhagic strokes for reducing rates of in-hospital mortality. Public Library of Science 2023-02-08 /pmc/articles/PMC10021536/ /pubmed/36962904 http://dx.doi.org/10.1371/journal.pgph.0001278 Text en © 2023 Ranasinghe et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ranasinghe, Vindya Shalini
Pathirage, Manoji
Gawarammana, Indika Bandara
Predictors of in-hospital mortality in stroke patients
title Predictors of in-hospital mortality in stroke patients
title_full Predictors of in-hospital mortality in stroke patients
title_fullStr Predictors of in-hospital mortality in stroke patients
title_full_unstemmed Predictors of in-hospital mortality in stroke patients
title_short Predictors of in-hospital mortality in stroke patients
title_sort predictors of in-hospital mortality in stroke patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021536/
https://www.ncbi.nlm.nih.gov/pubmed/36962904
http://dx.doi.org/10.1371/journal.pgph.0001278
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