BACKGROUND AND PURPOSE: Many patients with ischemic stroke eligible for recombinant tissue plasminogen activator (rt-PA) are not treated in part because of the risks and benefits perceived by treating physicians. Therefore, we aimed to develop a Stroke-Thrombolytic Predictive Instrument (TPI) to aid physicians considering thrombolysis for stroke. METHODS: Using data from 5 major randomized clinical trials (n=2184) testing rt-PA in the 0- to 6-hour window, we developed logistic regression equations using clinical variables as potential predictors of a good outcome (modified Rankin Scale score < or =1) and of a catastrophic outcome (modified Rankin Scale score > or =5), with and without rt-PA. The models were internally validated using bootstrap re-sampling. RESULTS: To predict good outcome, in addition to rt-PA treatment, 7 variables significantly affected prognosis and/or the treatment-effect of rt-PA: age, diabetes, stroke severity, sex, previous stroke, systolic blood pressure, and time from symptom onset. To predict catastrophic outcome, only age, stroke severity, and serum glucose were significant; rt-PA treatment was not. For patients treated within 3 hours, the median predicted probability of a good outcome with rt-PA was 42.9% (interquartile range [IQR]=18.6% to 64.7%) versus 25.3% (IQR=9.8% to 46.2%) without rt-PA; the median predicted absolute benefit was 12.5% (IQR=5.1% to 21.0%). The median probability for a catastrophic outcome, with or without, rt-PA was 15.2% (IQR=8.0% to 31.2%). The area under the receiver-operator characteristic curve was 0.788 for the model predicting good outcome and 0.775 for the model predicting bad outcome. CONCLUSIONS: The Stroke-TPI predicts good and bad functional outcomes with and without thrombolysis. Incorporated into a usable tool, it may assist in decision-making.
BACKGROUND AND PURPOSE: Many patients with ischemic stroke eligible for recombinant tissue plasminogen activator (rt-PA) are not treated in part because of the risks and benefits perceived by treating physicians. Therefore, we aimed to develop a Stroke-Thrombolytic Predictive Instrument (TPI) to aid physicians considering thrombolysis for stroke. METHODS: Using data from 5 major randomized clinical trials (n=2184) testing rt-PA in the 0- to 6-hour window, we developed logistic regression equations using clinical variables as potential predictors of a good outcome (modified Rankin Scale score < or =1) and of a catastrophic outcome (modified Rankin Scale score > or =5), with and without rt-PA. The models were internally validated using bootstrap re-sampling. RESULTS: To predict good outcome, in addition to rt-PA treatment, 7 variables significantly affected prognosis and/or the treatment-effect of rt-PA: age, diabetes, stroke severity, sex, previous stroke, systolic blood pressure, and time from symptom onset. To predict catastrophic outcome, only age, stroke severity, and serum glucose were significant; rt-PA treatment was not. For patients treated within 3 hours, the median predicted probability of a good outcome with rt-PA was 42.9% (interquartile range [IQR]=18.6% to 64.7%) versus 25.3% (IQR=9.8% to 46.2%) without rt-PA; the median predicted absolute benefit was 12.5% (IQR=5.1% to 21.0%). The median probability for a catastrophic outcome, with or without, rt-PA was 15.2% (IQR=8.0% to 31.2%). The area under the receiver-operator characteristic curve was 0.788 for the model predicting good outcome and 0.775 for the model predicting bad outcome. CONCLUSIONS: The Stroke-TPI predicts good and bad functional outcomes with and without thrombolysis. Incorporated into a usable tool, it may assist in decision-making.
Authors: David Asuzu; Karin Nyström; Joseph Schindler; Charles Wira; David Greer; Janet Halliday; Kevin N Sheth Journal: Neurocrit Care Date: 2015-10 Impact factor: 3.210
Authors: David M Kent; Robin Ruthazer; Carole Decker; Philip G Jones; Jeffrey L Saver; Erich Bluhmki; John A Spertus Journal: Neurology Date: 2015-08-19 Impact factor: 9.910
Authors: Branko N Huisa; Andrew B Stemer; Michael G Walker; Karen Rapp; Brett C Meyer; Justin A Zivin Journal: Int J Stroke Date: 2012-02-02 Impact factor: 5.266