(Stroke. 2000;31:2335.)
© 2000 American Heart Association, Inc.
Original Contributions |
Finding the Most Powerful Measures of the Effectiveness of Tissue Plasminogen Activator in the NINDS tPA Stroke Trial
From the Department of Neurology, University of Cincinnati (Ohio) (J.P.B.); Department of Biostatistics and Research Epidemiology, Henry Ford Health Sciences Center, Detroit, Mich (M.L.); Borgess Research Institute, Kalamazoo, Mich (R.K.); Department of Neurology, Wayne State University School of Medicine, Detroit, Mich (S.R.L.); University of California at San Diego Stroke Center (P.D.L.); Department of Neurology, University of Virginia Health System, Charlottesville (E.C.H.); Department of Neurology, Mayo Clinic Jacksonville (Fla) (T.G.B.); Department of Neurology, University of Texas, Houston (J.G.); Department of Biometry and Epidemiology, Medical University of South Carolina, Charleston (B.C.T.); Division of Stroke and Trauma, National Institute of Neurological Disorders and Stroke, Bethesda, Md (J.R.M.); and Department of Neurology, Emory University School of Medicine, Atlanta, Ga (M.F.).
Correspondence to Joseph Broderick, MD, University of Cincinnati, Department of Neurology, 231 Bethesda Ave, ML 0525, Cincinnati, OH 45267-0525.
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Abstract |
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 Top Abstract IntroductionSubjects and MethodsResultsDiscussionReferences |
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Background and Purpose—We sought to identify the most powerful binary measures of the treatment effect of tissue plasminogen activator (tPA) in the National Institute of Neurological Disorders and Stroke (NINDS) rTPA Stroke Trial.
Methods—Using the Classification and Regression Tree (CART) algorithm, we evaluated binary cut points and combination of binary cut points with the 4 clinical scales and head CT imaging measures in the NINDS tPA Stroke Trial at 4 times after treatment: 2 hours, 24 hours, 7 to 10 days, and 3 months. The first analysis focused on detecting evidence of "early activity" of tPA with the use of outcome measures derived from the 2-hour and 24-hour clinical and radiographic measures. The second analysis focused on longer-term outcome and "efficacy" and used outcome measures derived from 7- to 10-day and 3-month measures. After identifying the cut points with the ability to classify patients into the tPA and placebo groups using part I data from the trial, we then used data from part II of the trial to validate the results.
Results—Of the 5 most powerful outcome measures for early
activity of tPA, 4 involved the National Institutes of Health Stroke
Scale (NIHSS) score at 24 hours or changes in the NIHSS score from
baseline to 24 hours. The best overall single outcome measure was an
NIHSS score 
2 at 24 hours, which provided an odds ratio of 5.4 (95%
CI, 2.4 to 12.1) and a projected sample size of 58 per treatment
group assuming an 
of 0.05 (2-sided test) and a power of 80% using
part I data. The top 2 and 3 of the top 5 outcome measures for
detecting the longer-term efficacy of tPA also involved the NIHSS
score. A Rankin score of 0 or 1 at 3 months was the third most powerful
outcome measure. Outcome measures identified by CART from part I data
were not as sensitive in detecting the effectiveness of tPA when
applied to part II data.
Conclusions—Measures using the NIHSS and a Rankin score 1 were
the most sensitive discriminators of the effectiveness of tPA in the
NINDS tPA Stroke Trial compared with the other clinical and
radiological measures. The outcome measures identified in this
exploratory analysis (eg, NIHSS score 2 at 24 hours) would be
best used as an outcome measure in future phase II trials of
recanalization begun within the first 3 hours after
stroke onset, with inclusion and exclusion criteria similar to those in
the NINDS tPA Stroke Trial.
Key Words: clinical trials • models, predictive • outcome • stroke • tissue plasminogen activator
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Stroke is the third leading cause of death and the leading cause of adult disability, but recruitment of patients into clinical trials of acute ischemic stroke during the 1990s has been poor. The major reason for the lack of eligible patients is the narrow time window to treatment in almost all of the recent and ongoing clinical trials. Intravenous tissue plasminogen activator (tPA), the only therapy for acute stroke that is approved by the Food and Drug Administration, was proven to be effective in 2 studies in which all patients were treated within 3 hours and nearly half within 90 minutes.1 The combination of a limited number of eligible patients and many promising new drugs and devices for acute ischemic stroke indicates the need for the efficient design of future randomized phase II and phase III clinical trials.
Selection of the primary outcome measure or end point is among the most important considerations in the design of a clinical trial and depends not only on the disease under study but also on the expected mechanism and effect of a given therapy. Ideally, a study outcome measure should be easy to perform, reproducible, valid, clinically meaningful, and resistant to bias.2 It should also detect clinically relevant differences in the effectiveness of various therapies for a given disease with the smallest number of patients possible.
The National Institute of Neurological Disorders and Stroke (NINDS)
rtPA Stroke Trial, the basis for approval of tPA, used 4 different
clinical scales (primary outcome measures) as prespecified binary end
points.1 Additionally, volumetric measurement of infarct
by CT (secondary measure) was a prespecified end point. The present
exploratory study was designed to determine which binary end points, or
combination of end points, would consistently require the
fewest number of patients to detect a significantly beneficial effect
of tPA, assuming a power of 80% and an of 0.05, if such a study
were repeated. We hoped that such an analysis might provide
guidance concerning selection of outcome measures or end points for
future phase II and phase III studies of therapy for acute
ischemic stroke.
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Subjects and Methods |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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The NINDS rtPA Stroke Trial was composed of 2 separate parts with nearly identical study methodology but with different goals.1 The focus of part I was identifying early activity of tPA, and the prespecified end point of interest was improvement of
4 points from the baseline National Institutes of Health Stroke Scale
(NIHSS) to the 24-hour NIHSS or complete resolution (NIHSS score=0) at
24 hours (evidence of early activity of tPA). The goal of part II was
to determine whether tPA was associated with a significantly better
long-term functional and neurological outcome. The prespecified end
point for part II was a favorable outcome measured from 4 neurological
and functional scales dichotomized to clearly identify patients with
minimal or no neurological or functional deficit: Rankin Scale score of
0 or 1,3 NIHSS score of 0 or 1,4 Glasgow
Outcome Scale score of 1,5 or Barthel Index score of 95 to
100.6 A global statistic was used to test the effect of
tPA on the likelihood of a favorable outcome compared with the
placebo-treated group.7 In addition, detailed image
analyses of CT images obtained at baseline, 24 hours, 7 to 10
days, and 3 months enabled comparisons of the total lesion volume
between the 2 treatment groups.8 This exploratory analysis was designed to identify the most powerful binary end points with regard to detecting a treatment effect of tPA in the NINDS rtPA Stroke Trial. In other words, if we were starting the NINDS tPA Stroke Trial today, which end points or outcome measures would be the most likely to detect the activity or efficacy of tPA with the smallest sample size possible? To accomplish this, we wanted to consider all binary end points that could be constructed from the 4 clinical scales and imaging measures at 4 times after treatment: 2 hours, 24 hours, 7 to 10 days, and 3 months.
We conducted 2 separate analyses. The first analysis focused on detecting evidence of "early activity" of tPA and used outcome measures derived from 2- and 24-hour clinical and radiographic measures. The second analysis focused on longer-term outcome and "efficacy" and used outcome measures derived from 7- to 10-day and 3-month measures.
The analyses were performed with the use of the Classification
and Regression Tree (CART) algorithm.9 CART methodology is
known as binary recursive partitioning using a
nonparametric approach. CART was used to construct
variable partitions to classify patients into either the treatment
or placebo group with a simple recursive tree structure. CART started
with outcomes of interest (eg, a given NIHSS score at 24 hours, changes
in the NIHSS score from baseline to 24 hours, and lesion volume at 24
hours). For each outcome (eg, NIHSSS score at 2 hours), CART explored
all the possible cutoff points (eg, values of 0, 1, and 42) and
picked the end point (the cutoff point) that provided the greatest
separation of the patients into the tPA-treated group and the
placebo-treated group. CART identified the end point with the greatest
ability to separate the 2 treatment groups from among all the binary
end points that were considered. This end point was then used to divide
patients into 2 groups (subgroups). From these 2 subgroups, CART
selected end points (the cutoff point) for every remaining binary
outcome of interest and identified an end point from among the other
end points with the greatest ability to divide the patients into
treatment groups. The process continued until no further end point
could be identified (see Figure 1
for
example). We then calculated the sample sizes for each end point or end
point combination on the basis of 80% power, a 2-sided test, and
of 0.05. We used the part I data from the NINDS rtPA Stroke Trial to
explore all the possible end points using the CART approach and used
the part II data to validate the results.
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We compared the sample size between the end points identified by this
exploratory analysis and the original primary end points for
parts I and II in the NINDS rtPA Stroke Trial. The sample size
calculation was based on a 
2 test of
proportions. Since power is a function of sample size, if we had fixed
sample size and computed power, holding constant the and
proportions of interest, the same end points would have been chosen. We
chose instead to fix power and compute sample size to provide
information that is more interpretable to the clinician.
To explore how entry criteria may affect the choice of end point for a
future trial based on the data from NINDS rtPA Stroke Trial, we
repeated the entire procedure including only patients with an NIHSS
score 10 at baseline. The rationale for selecting this cutoff is
because of a planned trial of
intravenous/intra-arterial tPA (the
Interventional Management of Stroke [IMS] Trial), in which
patients will have treatment started within 3 hours of onset. In this
trial, an NIHSS score 10 will be used to ensure a high likelihood of
a visible intra-arterial clot at
angiography.10
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Four of the 5 most powerful end points in detecting early activity of tPA involved the NIHSS (Table 1
). The
other end point was CT lesion volume 0.3 cm3 at
24 hours. The best overall single end point was NIHSS score 2 at 24
hours, which provided an odds ratio of 5.4 (95% CI, 2.4 to 12.1) and a
projected sample size of 58 per group. By comparison, the original
primary end point of part I of the NINDS rtPA Stroke Trial for early
activity of tPA was improvement 4 points from the baseline NIHSS to
the 24-hour NIHSS or complete resolution (NIHSS score=0) at 24 hours.
According to this end point, we would project a needed sample size
of 625 per group to reach 80% power using part I data and 573 using
part II data under similar assumptions.
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A combination of 2 measures provided only slightly lower projected
sample sizes in detecting early activity than use of a single measure
(Figure 1
). For example, NIHSS score 2 or change in NIHSS
score 8 between baseline and 2 hours was associated with an odds
ratio of 5.22 with a sample size of 40 per treatment group, assuming
the same power and level. The next best 2-measure combination in
detecting differences between the 2 treatment groups was NIHSS score
2 (good outcome) or NIHSS score at 24 hours 26 (bad outcome; sample
size, 47).
Table 2 demonstrates the results when the
best single end points for detecting early activity selected with part
I data are applied to part II data. The 3 end points with the smallest
projected sample size with the use of part I data were also
sensitive end points when applied to part II data, but part II data
indicate that the needed number of patients with the use of this end
point in a proposed study would be somewhat higher. These 3 end points
were NIHSS score 2 at 24 hours, change between baseline and 24-hour
NIHSS score 15 points, and NIHSS score 5 at 2 hours. The
projected sample sizes for these 3 end points as determined by part
II data ranged from 96 to 121 per patient group. Applying the top 3
combinations of 2 end points in part I of the trial to part II data
resulted in projected sample sizes of 113 to 134, which were not
smaller than projected sample sizes using single end points.
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Table 3 represents the best end
points with regard to detecting differences in longer-term outcome or
efficacy. The 2 most sensitive end points involved the NIHSSS
(projected sample size, 70 to 73). A Rankin score of 0 or 1 at 3
months was the third most sensitive end point (projected sample
size, 91). A combination of 2 measures did not appreciably change the
needed sample size. For example, the best combination was a change in
NIHSS score from baseline to 7 to 10 days of 24 or lesion volume at 3
months of 0.1 cm3 (odds ratio, 3.72; sample
size, 62).
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By comparison, the original primary end point for efficacy in part II
of the NINDS tPA Stroke Trial was a composite end point of Rankin Scale
score of 0 or 1, Glasgow Coma Scale score of 1, Barthel Index score of
95 to 100, and NIHSS score of 0 or 1. Using the composite end points,
we would project a needed sample size of 122 per group based on
part I data and 232 per group based on part II data, using similar
assumptions concerning and power.
Table 4 illustrates results when the best
single end points for detecting longer-term efficacy with part I data
are applied to part II data. In general, the projected sample sizes
are higher. Applying the combination of measures to part II data gave
similar projected sample sizes compared with projected sample
sizes with the use of single measures alone.
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Table 5 demonstrates the substantial
change in the best outcome measures when the analysis is
limited to patients with NIHSS score 10 at baseline. NIHSS measures
still consistently perform best, but the most discriminating
NIHSS measures in this subgroup are different than those in which the
entire sample of patients is used. Of the best 5 end points as
determined from part I data, only 2 measures were among the best as
determined by part II data. These 2 measures were change in NIHSS score
15 (or return to NIHSS score of 0) from baseline to 24 hours and
NIHSS score 2 at 24 hours. Using either of these 2 measures, one
would expect to need 100 to 200 patients per treatment group for a
phase II study of safety and early drug activity.
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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We have found that the NIHSS score is an excellent outcome measure for use in phase II studies, in which the primary goal is to screen potential stroke therapies for early activity and safety as well as long-term outcome. Measures using the NIHSS were the most sensitive and consistent discriminators of the effectiveness of tPA in both parts of the NINDS rtPA Stroke Trial compared with the other clinical and radiological measures. In addition, use of an NIHSS score
2 at 24
hours was a more powerful measure of a treatment effect of tPA in this
group of patients than the composite global end point at 3 months, the
primary outcome measure of efficacy in part II of the NINDS rtPA Stroke
Trial.1 7 A Rankin score of 0 or 1 at 3 months and at 7 to
10 days and a Barthel Index score of 95 at 7 to 10 days were other
sensitive measures of longer-term efficacy in addition to NIHSS-based
end points. The power of the NIHSS outcome measure to detect a treatment effect has several possible explanations. First, the NIHSS has been shown to be reproducible, valid, and an excellent predictor of long-term outcome.4 11 12 13 The 42-point NIHSS is more sensitive to change than global scales such as the modified Rankin, which has only 5 levels of change. Finally, great care was taken in the NINDS rtPA Stroke Trial to ensure that all treating investigators could perform the NIHSS reliably and correctly before the trial was started and as the trial progressed.1 13 Such attention to performance of the NIHSS reduces variability and makes change in the score more likely to be related to real changes in the patient’s condition.
The most powerful measure of the effectiveness of tPA during the first 3 months after stroke was a given level or amount of change in the NIHSS during the first 24 hours, or even the first 2 hours, after start of treatment. This finding supports the clinical observation that dramatic improvement in neurological function during the first hours after treatment may be an excellent marker of a treatment effect.14 In the original NINDS tPA Pilot Trial, some patients improved so dramatically during the first several hours that they were termed "on-the-table responders" by the investigators. Our analysis appears to confirm this clinical impression of an early treatment effect in some patients.
The CART method examines only possible binary cut points and does not
evaluate other nonbinary end points (eg, comparing the median NIHSS in
2 treatment groups at 24 hours). Binary end points work best when
outcome measures are not distributed normally but are skewed or
clustered. The distribution of measures of outcome after stroke, using
treatment measures such as the Barthel Index, Rankin Scale, and NIHSS,
is quite skewed. Patient scores on these scales tend to cluster at the
very good and bad ends of the given scale, with relatively fewer
patients in the middle of the scale. Figure 2 demonstrates just such a distribution,
using the Barthel Index at 3 months in tPA-treated and placebo patients
from the NINDS tPA Stroke Trial. The distribution of
radiographic end points such as CT volume of brain
infarction is also skewed. Thus, the CART method should be a useful
tool to explore clinical and radiographic end points in
acute stroke studies. The J- or U-shaped distribution of scale scores
such as the Barthel Index, or the skewed distribution of volumes of
infarction as measured by CT, emphasizes that investigators must
consider carefully the distribution of scale scores from similar
previous trials, the desired clinically relevant outcomes, and the
expected effect of a therapy when choosing study end points.
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Global outcome measures using multiple appropriate end points have been shown to increase the power of a study when individual end points are correlated in the same direction, compared with using only one of the individual end points.7 Such an approach was used successfully for part II of the NINDS trial. Retrospectively, the global outcome method was applied to the European Cooperative Acute Stroke Study (ECASS) I and gave a positive result.15 However, the global outcome approach is only as good as the individual outcome measures. The more powerful the individual end points, the better the global outcome approach should be. Using the validated results of an exploratory CART analysis, one could improve the selection of individual end points for a global outcome measure.
The NIHSS criterion identified in this exploratory analysis
(eg, NIHSS score 2 at 24 hours) would be appropriate as an end point
in a proposed phase II trial of recanalization
begun within the first 3 hours after stroke onset with inclusion and
exclusion criteria similar to those in the NINDS trial. Different end
points may be more appropriate in other proposed trials depending on
the entry criteria, the time to treatment, and the suspected action of
the drug. For example, when we included only patients with NIHSS score
10, the projected best end points were different than those in
which the whole patient sample was used.
Comparison of end points in the ECASS II16 and NINDS rtPA Stroke Trial1 illustrates how selection of an end point depends on the time to treatment and expected action of the drug. In the NINDS rtPA Stroke Trial, in which patients were treated within 3 hours, a Rankin score of 0 or 1 at 3 months was the third most sensitive end point with regard to long-term efficacy. Using this end point would require only 91 patients per treatment group to detect a benefit for tPA in part I of the tPA Stroke Trial. A Rankin score of 0 to 2 at 3 months was a much less sensitive end point for detecting a difference and would require 212 patients per treatment group.
By contrast, the ECASS II study treated patients within 6 hours of onset and used a Rankin score of 0 or 1 as the primary study outcome.16 When this end point was used, the ECASS II study was negative. When the ECASS II study was analyzed post hoc with an end point of Rankin score of 0 to 2, a benefit for tPA-treated patients was demonstrated. The Prolyse in Acute Cerebral Thromboembolism (PROACT) II trial, an intra-arterial study that also treated patients within 6 hours, also found a Rankin score of 0 to 2 a more sensitive measure of the benefit of prourokinase than a Rankin score of 0 to 1.17 The different sensitivities of different cut points for the Rankin Scale make biological sense. Thrombolytic therapy administered soon after onset of ischemia, as in the NINDS tPA Stroke Trial, would be more likely to open the occluded artery more quickly and to salvage brain tissue than therapy given at a later time. Such patients treated sooner would be more likely to return to normal or near normal, as measured by a Rankin score of 0 or 1. Patients who receive thrombolytic therapy after 3 hours of symptom onset would be less likely to have sparing of ischemic brain but could still accrue some benefit. Such patients may be less likely to return to normal than if treated with intravenous tPA within 3 hours but may have a shift in their disability from severe to mild or moderate or a Rankin score of 0 to 2. For a future study of a recanalization therapy begun within 3 hours of onset, a Rankin score of 0 or 1 would likely be a better 3-month end point than a Rankin score of 0 to 2. For a study of neuroprotection given at a later time window, another cut point may be better.
Our study also demonstrates the potential problems of the use of exploratory post hoc analyses from small to moderate studies to determine end points for subsequent trials without subsequently validating the results prospectively.18 19 20 A definitively positive trial like the NINDS trial, as determined by both primary and secondary end points, is the ideal way to explore the sensitivities of various end points. Many of the end points that were quite sensitive to the early activity of tPA and long-term effectiveness in part I patients were less sensitive with the use of part II data. Ideally, one should choose an end point that is consistently sensitive in detecting a treatment effect in multiple studies, such as in both part I and part II of the NINDS tPA Stroke Trial. The reasons underlying the different sensitivities of various end points in part I and part II of the trial may relate in part to overall differences in baseline characteristics between the patients in part I or part II that have been shown to be related to long-term outcome (eg, baseline NIHSS score or age) or to random error. We plan to explore this issue further in the NINDS data set.
Some investigators have suggested that radiological end points, particularly changes in MR diffusion/perfusion, may be used as surrogate end points in acute stroke studies and require fewer patients to demonstrate the activity of a given therapy.21 22 23 In our study high-quality analysis of the volume of brain infarction as measured by CT was not as sensitive to a treatment effect as the clinical scale measures. To equal the sensitivities of the NIHSS within the 3-hour time window, MRI measures would need to be able to detect differences between treatment groups with fewer than 60 to 70 patients per group. Certainly MRI will never surpass neurological scales such as the NIHSS in terms of speed, ease of use, lack of missing data, and cost. In addition, the time to obtain MRI, even with echo planar imaging, will delay the time to treatment for at least the near future. Whether end points using diffusion or diffusion/perfusion MRI can be a more sensitive measure of treatment effect in a clinical trial than the NIHSS during the first 24 hours is a hypothesis that needs to be tested.
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Acknowledgments |
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This study was supported by National Institutes of Health contracts NO1-NS-02382, NO1-NS-02374, NO1-NS-02377, NO1-NS-02379, NO1-NS-02373, NO1-NS-02378, NO1-NS-02376, and NO1-NS-02380.
Received March 30, 2000; revision received July 18, 2000; accepted July 18, 2000.
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References |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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 R. Mikulik, M. Ribo, M. D. Hill, J. C. Grotta, M. Malkoff, C. Molina, M. Rubiera, R. Delgado-Mederos, J. Alvarez-Sabin, A. V. Alexandrov, et al. Accuracy of Serial National Institutes of Health Stroke Scale Scores to Identify Artery Status in Acute Ischemic Stroke Circulation, May 22, 2007; 115(20): 2660 - 2665. [Abstract] [Full Text] [PDF]
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M. Saqqur, K. Uchino, A. M. Demchuk, C. A. Molina, Z. Garami, S. Calleja, N. Akhtar, F. O. Orouk, A. Salam, A. Shuaib, et al. Site of Arterial Occlusion Identified by Transcranial Doppler Predicts the Response to Intravenous Thrombolysis for Stroke Stroke, March 1, 2007; 38(3): 948 - 954. [Abstract] [Full Text] [PDF]
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J. L. Banks and C. A. Marotta Outcomes Validity and Reliability of the Modified Rankin Scale: Implications for Stroke Clinical Trials: A Literature Review and Synthesis Stroke, March 1, 2007; 38(3): 1091 - 1096. [Abstract] [Full Text] [PDF]
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M. Saqqur, C. A. Molina, A. Salam, M. Siddiqui, M. Ribo, K. Uchino, S. Calleja, Z. Garami, K. Khan, N. Akhtar, et al. Clinical Deterioration After Intravenous Recombinant Tissue Plasminogen Activator Treatment: A Multicenter Transcranial Doppler Study Stroke, January 1, 2007; 38(1): 69 - 74. [Abstract] [Full Text] [PDF]
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 D. Kim, R. Jahan, S. Starkman, A. Abolian, C.S. Kidwell, F. Vinuela, G.R. Duckwiler, B. Ovbiagele, P.M. Vespa, S. Selco, et al. Endovascular Mechanical Clot Retrieval in a Broad Ischemic Stroke Cohort AJNR Am. J. Neuroradiol., November 1, 2006; 27(10): 2048 - 2052. [Abstract] [Full Text] [PDF]
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P. Khatri and J. Broderick Measuring Treatment Effect in Acute Stroke Trials Stroke, September 1, 2006; 37(9): 2204 - 2204. [Full Text] [PDF]
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A. M. Demchuk, M. D. Hill, P. A. Barber, B. Silver, S. C. Patel, S. R. Levine, and for the NINDS rtPA Stroke Study Group, NIH Importance of Early Ischemic Computed Tomography Changes Using ASPECTS in NINDS rtPA Stroke Study Stroke, October 1, 2005; 36(10): 2110 - 2115. [Abstract] [Full Text] [PDF]
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A. Davalos, M. Blanco, S. Pedraza, R. Leira, M. Castellanos, J. M. Pumar, Y. Silva, J. Serena, and J. Castillo The clinical-DWI mismatch: A new diagnostic approach to the brain tissue at risk of infarction Neurology, June 22, 2004; 62(12): 2187 - 2192. [Abstract] [Full Text] [PDF]
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F. B. Young, K. R. Lees, and C. J. Weir Strengthening Acute Stroke Trials Through Optimal Use of Disability End Points Stroke, November 1, 2003; 34(11): 2676 - 2680. [Abstract] [Full Text] [PDF]
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R. T. Higashida and A. J. Furlan Trial Design and Reporting Standards for Intra-Arterial Cerebral Thrombolysis for Acute Ischemic Stroke Stroke, August 1, 2003; 34 (8): e109 - e137. [Abstract] [Full Text] [PDF]
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L. A. Labiche, F. Al-Senani, A. W. Wojner, J. C. Grotta, M. Malkoff, and A. V. Alexandrov Is the Benefit of Early Recanalization Sustained at 3 Months?: A Prospective Cohort Study Stroke, March 1, 2003; 34(3): 695 - 698. [Abstract] [Full Text] [PDF]
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J.T. L. Wilson, A. Hareendran, M. Grant, T. Baird, U. G.R. Schulz, K. W. Muir, and I. Bone Improving the Assessment of Outcomes in Stroke: Use of a Structured Interview to Assign Grades on the Modified Rankin Scale Stroke, September 1, 2002; 33(9): 2243 - 2246. [Abstract] [Full Text] [PDF]
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D. J. Gladstone, S. E. Black, and A. M. Hakim Toward Wisdom From Failure: Lessons From Neuroprotective Stroke Trials and New Therapeutic Directions Stroke, August 1, 2002; 33(8): 2123 - 2136. [Abstract] [Full Text] [PDF]
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J. N. Fink, M. H. Selim, S. Kumar, B. Silver, I. Linfante, L. R. Caplan, and G. Schlaug Is the Association of National Institutes of Health Stroke Scale Scores and Acute Magnetic Resonance Imaging Stroke Volume Equal for Patients With Right- and Left-Hemisphere Ischemic Stroke? Stroke, April 1, 2002; 33(4): 954 - 958. [Abstract] [Full Text] [PDF]
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Recommendations for Clinical Trial Evaluation of Acute Stroke Therapies Stroke, July 1, 2001; 32(7): 1598 - 1606. [Abstract] [Full Text] [PDF]
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