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Predicting survival for 1 year among different subtypes of stroke. Results from the Perth Community Stroke Study
C S Anderson1, K D Jamrozik, R J Broadhurst
1Department of Medicine, Flinders University of South Australia, Bedford Park.
Insights
Simple clinical factors like coma, incontinence, cardiac failure, severe paresis, and atrial fibrillation predict 1-year stroke survival. These predictors help identify high-risk patients for targeted stroke management.
Area of Science:
- Neurology
- Epidemiology
- Public Health
Background:
- Limited research exists on factors predicting long-term stroke survival with defined pathology.
- The relative importance of various stroke risk factors, including sociodemographic and premorbid variables, requires detailed description.
Purpose of the Study:
- To evaluate factors influencing or predicting long-term survival after stroke in an unselected patient series.
- To define the relative importance of stroke risk factors, including sociodemographic and premorbid variables.
Main Methods:
- A cohort of 492 patients with stroke was studied in Perth, Western Australia (1989-1990).
- Objective evidence of stroke pathology was obtained in 86% of cases.
- All deaths during a 1-year follow-up period were reviewed.
Main Results:
- One-year case fatality varied significantly by stroke subtype, ranging from 6% for boundary zone infarction to 46% for primary intracerebral hemorrhage.
- A predictive model identified five independent predictors of 1-year mortality: coma, urinary incontinence, cardiac failure, severe paresis, and atrial fibrillation.
- The model demonstrated high sensitivity (90-94%) and negative predictive value (92-95%) for predicting death.
Conclusions:
- Stroke case fatality, timing, and causes of death differ across pathological subtypes.
- Simple clinical measures at onset, reflecting neurological deficit severity and cardiac status, independently predict 1-year mortality.
- These predictors can aid in directing patient management strategies.
Background And Purpose:
Few studies have evaluated the factors influencing or predicting long-term survival after stroke in an unselected series of patients in whom the underlying cerebrovascular pathology is clearly defined. Moreover, the relative importance of risk factors for stroke, including sociodemographic and premorbid variables, has not been described in detail.
Methods:
The study cohort consisted of 492 patients with stroke who were registered with a population-based study of acute cerebrovascular disease undertaken in Perth, Western Australia, during an 18-month period in 1989 and 1990. Objective evidence of the pathological basis of the stroke was obtained in 86% of cases, and all deaths among patients during a follow-up of 1 year were reviewed.
Results:
One hundred twenty patients (24%) died within 28 days of the onset of stroke. Among the different subtypes of stroke, the 1-year case fatality (mean, 38%) varied from 6% and 16% for boundary zone infarction and lacunar infarction, respectively, to 42% and 46% for subarachnoid hemorrhage and primary intracerebral hemorrhage, respectively. Using Cox proportional-hazards analysis, a predictive model was developed on 321 patients with acute stroke (test sample). The best model contained five baseline variables that were independent predictors of death within 1 year: coma (relative risk [RR], 3.0; 95% confidence interval [CI], 1.1 to 8.4), urinary incontinence (RR, 3.9; 95% CI, 1.4 to 10.6), cardiac failure (RR, 6.5; 95% CI, 2.8 to 15.1), severe paresis (RR, 4.9; 95% CI, 1.6 to 15.5), and atrial fibrillation (RR, 2.0; 95% CI, 1.1 to 3.5). The sensitivity, specificity, and negative predictive value of this model for predicting death were 90%, 83%, and 95%, respectively. When applied to a second randomly selected validation sample of 171 events, sensitivity was 94%, specificity 62%, and negative predictive value 92%, indicating stability of the model.
Conclusions:
Although the case fatality, timing, and cause of death vary considerably among the different pathological subtypes of stroke, simple clinical measures that reflect the severity of the neurological deficit and associated cardiac disease at onset independently predict death by 1 year and may help to direct management.