Background: In Denmark 15%-20% of stroke victims die within the first year. Simple and valid tools are needed to assess patients' risk of dying. The aim of this study was to identify potential predictors of 1-year mortality in stroke victims and construct a simple and valid prediction model.
Methods: Data were collected retrospectively from a cohort of 1031 stroke victims admitted over a period of 18 months at Nordsjællands Hospital, Denmark. Follow-up was 1 year after symptom onset. Multiple logistic regression analysis with backwards selection was used to identify predictors and construction of a prediction model. The model was validated using cross validation with 10,000 repeated random splits of the dataset. Area under the receiver operating characteristic curve (AUC) and Brier score were used as measures of validity.
Results: Within the first year 186 patients died (18.0%) and 4 (0.4%) were lost to follow-up. Age (OR 1.08), gender (OR 2.19), stroke severity (OR 1.03), Early Warning Score (OR 1.17), Performance Status (ECOG) (OR 1.94), Body Mass Index (OR 0.91), the Charlton's Comorbidity Index (OR 1.17), and urinary problems (OR 2.55) were found to be independent predictors of 1-year mortality. A model including age, stroke severity, Early Warning Score, and Performance Status was found to be valid (AUC 86.5 %, Brier Score 9.03).
Conclusions: A model including only 4 clinical variables available shortly after admission was able to predict the 1-year mortality risk of patients with acute ischemic and haemorrhagic stroke.
Keywords: Stroke; cross validation; mortality; prediction models.
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