RESUMEN
INTRODUCTION: The value of routine blood markers as prognostic indicators is increasingly established in acute ischaemic stroke. The relationship is less well defined in haemorrhagic stroke. In this study, we examined routine admission blood markers and applied a logistic regression model to predict outcome in haemorrhagic stroke. METHOD: A retrospective study was performed between September 2009-2011 in a general admission stroke unit in the UK. 1400 patients were admitted with stroke during this period, of which 117 were haemorrhagic. Admission systolic and diastolic blood pressure, venous blood samples and pre- and post-morbid (i.e. at discharge or death) modified Rankin scores were also recorded. Patients were controlled for age, sex, smoking status, hypertension status and co-morbidities (using Charleson Comorbidity Index scores). Logistic regression models were generated using SPSS. RESULTS: 113 patients were analysed (58 male/55 female). Lower admission blood glucose (p=0.009), lower total leukocyte count (p=0.001) and lower neutrophil count (p=0.021) were found to be significantly associated with survival vs. death. 90 patients with complete glucose, leukocyte count, sex (forced) and pre-morbid Rankin score (forced) data were entered into a logistic regression model. This predicted correct group membership (survived/deceased) in 72.2% of cases (83.9% survivors/52.9% deceased correctly predicted). In females with normal leukocyte count and glucose, survival was predicted with 68% accuracy. CONCLUSION: These results suggest that a logistic regression model using low admission glucose and low total leukocyte count may be markers of better prognosis in acute haemorrhagic stroke with a differential effect between sexes.