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.

BackgroundCerebral salt wasting syndrome (CSWS) and Syndrome of Inappropriate Anti Diuretic Hormone secretion (SIADH) are the most common aetiological factors for developing hyponatremia following stroke. The differentiation of these two entities is crucial as the treatment options are completely different. Hence the knowledge on predictors of CSWS is important to make a more accurate diagnosis of CSWS. MethodsTwo hundred and fourty six patients with confirmed stroke were prospectively observed throughout the hospital stay in a tertiary referral center in Sri Lanka to identify the possible predictors of CSWS. Hyponatremia was defined as serum Na+ level less than 131mmo/l. Serum osmolality, urine osmolality, urinary Na+, serum cortisol and thyroid function tests were performed on all the hyponatremic patients. Differentiation of the CSWS and SIADH was based on physical examination findings and laboratory parameters. ResultsThe incidence of hyponatremia in our study population was 19.1% (95% Confidence Interval 14.39-24.58). The majority of patients (24, 51%) were attributed to CSWS. SIADH group comprised of 17 (36.2%) patients and 6 (12.7%) patients had other undetermined causes. There was a significant statistical difference between the aetiologies of hyponatremia and laboratory investigations like urinary Na+, urinary osmolality and serum osmolality. Demographic characteristics, comorbidities, imaging findings and clinical parameters like systolic blood pressure, diastolic blood pressure, on admission GCS were considered in the multivariable logistic regression model and the overall model was not significant. Conclusion The incidence of CSWS is higher than the incidence of SIADH. The demographic characteristics, comorbidities, imaging and clinical parameters like blood pressure, on admission GCS could not predict the occurrence of CSWS

Background: 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. Methods: This was a prospective observational 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. Serum electrolyte test was performed in all stroke patients and hyponatremia was defined as serum Na+ less than 131mmol/l. 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. Results: 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. Conclusion: On admission GCS and haemorrhagic stroke are independent predictors of in-hospital mortality. Patients with on admission GCS <10 have a moderate predictive ability in predicting the 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.