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Prediction of Long-term Outcome in the Early Hours Following Acute Ischemic Stroke

Prediction of Long-term Outcome in the Early Hours Following Acute Ischemic Stroke Abstract Objective: To develop a model for predicting outcome in the first few hours after the onset of an ischemic stroke on the basis of the clinical findings obtained during a rapid bedside examination. Design: Clinical records were retrieved from the data bank of a randomized multicenter trial. The resulting case series was split into two subgroups that served as a "training set" and a "test set." Logistic regression was applied to the training set to select the prognostic predictors among baseline clinical findings. The performances of the model based on independent prognostic predictors were then validated in the test set. Setting: Eleven primary care institutions (either hospitals or university clinics) participating in the Italian Acute Stroke Study on the efficacy of hemodilution and monosialoganglioside in acute ischemic stroke. Patients: Consecutive noncomatose patients (N=300) observed within the first 6 hours after the onset of a first supratentorial ischemic stroke. Main Outcome Measure: Death or disablement 4 months after the index stroke. Disablement was defined as a score of 3 or higher on the Rankin Scale. Results: Age and CNS score defined six risk groups with a predicted 4-month poor outcome rate ranging from 10% (patients aged 70 years or younger and with an initial CNS score of 7 or higher) to 89% (patients older than 70 years and with a CNS score of 4.5 or lower). When a risk of poor outcome of 60% was taken as a cutoff, the accuracy of the prediction was 78%±6% in the training set and 72%±9% in the test set. Conclusion: Long-term outcome can be predicted in the first few hours following an acute ischemic stroke by means of a simple model based on age and CNS score. References 1. Fieschi C, Argentino C, Lenzi GL, et al. Therapeutic window for pharmacological treatment in acute focal cerebral ischemia . Ann N Y Acad Sci . 1988;522:662-666.Crossref 2. Foulkes MA, Wolf PA, Price TR, Mohr JP, Hier DB. The Stroke Data Bank: design, method and baseline characteristics . Stroke . 1988;19:547-554.Crossref 3. Bogousslavsky J, Van Melle G, Regli F. The Lausanne Stroke Registry: analysis of the first 1000 consecutive patients with first stroke . Stroke . 1988;19: 1083-1092.Crossref 4. Wade DT, Langton Hewer R. Why admit stroke patients to hospital? Lancet . 1983;1:807-809.Crossref 5. Alberts MJ, Bertels C, Dawson DV. An analysis of time of presentation after stroke . JAMA . 1990;263:65-68.Crossref 6. Barsan WG, Brott TG, Olinger CP, Adams HP, Clarke Haley E, Levy DE. Identification at entry of the patient with acute cerebral infarction . Ann Emerg Med . 1988;17:1192-1195.Crossref 7. Argentino C, Sacchetti ML, Toni D, et al. GM1 ganglioside therapy in acute ischemic stroke . Stroke . 1989;20:1143-1149.Crossref 8. Côté R, Hachinski VC, Shurvell BL, Norris YW, Wolfson C. The Canadian Neurological Scale: a preliminary study in acute stroke . Stroke . 1986;17:731-737.Crossref 9. Rankin J. Cerebrovascular accidents in patients over the age of 60, II: prognosis . Scott Med J . 1957;2:200-215. 10. Silver FL, Norris JW, Lewis AJ, Hachinski VC. Early mortality following stroke: a prospective review . Stroke . 1984;15:492-496.Crossref 11. Infante-Rivard C, Villeneuve JP, Esnaola S. A framework for evaluating and conducting prognostic studies: an application to cirrhosis of the liver . J Clin Epidemiol . 1989;42:791-805.Crossref 12. SAS Institute Inc. SAS/STAT User's Guide . Cary, NC: SAS Institute Inc; 1988;6. 13. Dixon WJ, ed. BMDP Statistical Software Manual . Berkeley, Calif: University of California Press; 1990. 14. Hosmer DW, Lemeshow S. Goodness-of-fit tests for the multiple logistic regression model . Commun Stat . 1980;A10:1043-1069.Crossref 15. Breslow NE, Day NE. Statistical Methods in Cancer Research, Vol 1: The Analysis of Case-Control Studies . Lyon, France: International Agency for Research on Cancer Scientific Publications: 1980. No. 32, section 4.4. 16. Chambers BR, Norris JW, Shurvell BL, Hachinski VC. Prognosis of acute stroke . Neurology . 1987;37:221-225.Crossref 17. Levy DE, Scherer PB, Lapinski RH, Singer BH, Pulsinelli WA, Plum F. Predicting recovery from acute ischemic stroke using multiple clinical variables . In: Plum F, Pulsinelli WA, eds. Cerebrovascular Diseases . New York, NY: Raven Press; 1985:69-75. 18. Candelise L, Pinardi G, Morabito A, and the Italian Acute Stroke Study Group. Mortality in acute stroke with atrial fibrillation . Stroke . 1991;22:169-174.Crossref 19. Côté R, Battista RN, Wolfson C, Boucher J, Adam J, Hachinski VC. The Canadian Neurological Scale: validation and reliability assessment . Neurology . 1989; 39:638-643.Crossref http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Neurology American Medical Association

Prediction of Long-term Outcome in the Early Hours Following Acute Ischemic Stroke

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References (23)

Publisher
American Medical Association
Copyright
Copyright © 1995 American Medical Association. All Rights Reserved.
ISSN
0003-9942
eISSN
1538-3687
DOI
10.1001/archneur.1995.00540270038017
Publisher site
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Abstract

Abstract Objective: To develop a model for predicting outcome in the first few hours after the onset of an ischemic stroke on the basis of the clinical findings obtained during a rapid bedside examination. Design: Clinical records were retrieved from the data bank of a randomized multicenter trial. The resulting case series was split into two subgroups that served as a "training set" and a "test set." Logistic regression was applied to the training set to select the prognostic predictors among baseline clinical findings. The performances of the model based on independent prognostic predictors were then validated in the test set. Setting: Eleven primary care institutions (either hospitals or university clinics) participating in the Italian Acute Stroke Study on the efficacy of hemodilution and monosialoganglioside in acute ischemic stroke. Patients: Consecutive noncomatose patients (N=300) observed within the first 6 hours after the onset of a first supratentorial ischemic stroke. Main Outcome Measure: Death or disablement 4 months after the index stroke. Disablement was defined as a score of 3 or higher on the Rankin Scale. Results: Age and CNS score defined six risk groups with a predicted 4-month poor outcome rate ranging from 10% (patients aged 70 years or younger and with an initial CNS score of 7 or higher) to 89% (patients older than 70 years and with a CNS score of 4.5 or lower). When a risk of poor outcome of 60% was taken as a cutoff, the accuracy of the prediction was 78%±6% in the training set and 72%±9% in the test set. Conclusion: Long-term outcome can be predicted in the first few hours following an acute ischemic stroke by means of a simple model based on age and CNS score. References 1. Fieschi C, Argentino C, Lenzi GL, et al. Therapeutic window for pharmacological treatment in acute focal cerebral ischemia . Ann N Y Acad Sci . 1988;522:662-666.Crossref 2. Foulkes MA, Wolf PA, Price TR, Mohr JP, Hier DB. The Stroke Data Bank: design, method and baseline characteristics . Stroke . 1988;19:547-554.Crossref 3. Bogousslavsky J, Van Melle G, Regli F. The Lausanne Stroke Registry: analysis of the first 1000 consecutive patients with first stroke . Stroke . 1988;19: 1083-1092.Crossref 4. Wade DT, Langton Hewer R. Why admit stroke patients to hospital? Lancet . 1983;1:807-809.Crossref 5. Alberts MJ, Bertels C, Dawson DV. An analysis of time of presentation after stroke . JAMA . 1990;263:65-68.Crossref 6. Barsan WG, Brott TG, Olinger CP, Adams HP, Clarke Haley E, Levy DE. Identification at entry of the patient with acute cerebral infarction . Ann Emerg Med . 1988;17:1192-1195.Crossref 7. Argentino C, Sacchetti ML, Toni D, et al. GM1 ganglioside therapy in acute ischemic stroke . Stroke . 1989;20:1143-1149.Crossref 8. Côté R, Hachinski VC, Shurvell BL, Norris YW, Wolfson C. The Canadian Neurological Scale: a preliminary study in acute stroke . Stroke . 1986;17:731-737.Crossref 9. Rankin J. Cerebrovascular accidents in patients over the age of 60, II: prognosis . Scott Med J . 1957;2:200-215. 10. Silver FL, Norris JW, Lewis AJ, Hachinski VC. Early mortality following stroke: a prospective review . Stroke . 1984;15:492-496.Crossref 11. Infante-Rivard C, Villeneuve JP, Esnaola S. A framework for evaluating and conducting prognostic studies: an application to cirrhosis of the liver . J Clin Epidemiol . 1989;42:791-805.Crossref 12. SAS Institute Inc. SAS/STAT User's Guide . Cary, NC: SAS Institute Inc; 1988;6. 13. Dixon WJ, ed. BMDP Statistical Software Manual . Berkeley, Calif: University of California Press; 1990. 14. Hosmer DW, Lemeshow S. Goodness-of-fit tests for the multiple logistic regression model . Commun Stat . 1980;A10:1043-1069.Crossref 15. Breslow NE, Day NE. Statistical Methods in Cancer Research, Vol 1: The Analysis of Case-Control Studies . Lyon, France: International Agency for Research on Cancer Scientific Publications: 1980. No. 32, section 4.4. 16. Chambers BR, Norris JW, Shurvell BL, Hachinski VC. Prognosis of acute stroke . Neurology . 1987;37:221-225.Crossref 17. Levy DE, Scherer PB, Lapinski RH, Singer BH, Pulsinelli WA, Plum F. Predicting recovery from acute ischemic stroke using multiple clinical variables . In: Plum F, Pulsinelli WA, eds. Cerebrovascular Diseases . New York, NY: Raven Press; 1985:69-75. 18. Candelise L, Pinardi G, Morabito A, and the Italian Acute Stroke Study Group. Mortality in acute stroke with atrial fibrillation . Stroke . 1991;22:169-174.Crossref 19. Côté R, Battista RN, Wolfson C, Boucher J, Adam J, Hachinski VC. The Canadian Neurological Scale: validation and reliability assessment . Neurology . 1989; 39:638-643.Crossref

Journal

Archives of Neurology – American Medical Association

Published: Mar 1, 1995

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