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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-01-01
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| Series: | PLOS Global Public Health |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021536/?tool=EBI |
| _version_ | 1797700037610831872 |
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| author | Vindya Shalini Ranasinghe Manoji Pathirage Indika Bandara Gawarammana |
| author_facet | Vindya Shalini Ranasinghe Manoji Pathirage Indika Bandara Gawarammana |
| author_sort | Vindya Shalini Ranasinghe |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T04:17:20Z |
| format | Article |
| id | doaj.art-b8b89356dab64445b13be282c550ee5c |
| institution | Directory Open Access Journal |
| issn | 2767-3375 |
| language | English |
| last_indexed | 2024-03-12T04:17:20Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Global Public Health |
| spelling | doaj.art-b8b89356dab64445b13be282c550ee5c2023-09-03T10:35:35ZengPublic Library of Science (PLoS)PLOS Global Public Health2767-33752023-01-0132Predictors of in-hospital mortality in stroke patientsVindya Shalini RanasingheManoji PathirageIndika Bandara GawarammanaIn-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.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021536/?tool=EBI |
| spellingShingle | Vindya Shalini Ranasinghe Manoji Pathirage Indika Bandara Gawarammana Predictors of in-hospital mortality in stroke patients PLOS Global Public Health |
| 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021536/?tool=EBI |
| work_keys_str_mv | AT vindyashaliniranasinghe predictorsofinhospitalmortalityinstrokepatients AT manojipathirage predictorsofinhospitalmortalityinstrokepatients AT indikabandaragawarammana predictorsofinhospitalmortalityinstrokepatients |