Entry Criteria and Baseline Characteristics Predict
Outcome in Acute Stroke Trials
Ken Uchino, MD; Dean Billheimer, PhD; Steven C. Cramer, MD
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Background and Purpose—We sought to study the range of entry criteria and baseline characteristics in acute stroke trials
and to understand their effects on patient outcomes.
Methods—Randomized, placebo-controlled therapeutic trials in patients with acute ischemic stroke were identified. Entry
criteria, baseline clinical characteristics, and outcome were extracted for the placebo group of each trial. The relationship
between key variables was then determined.
Results—Across 90 placebo groups identified, there was great variation in entry criteria and outcome measures. This was
associated with divergent outcomes; for example, in some studies most placebo group patients died, while in other
studies nearly all had no disability. Entry criteria were significantly correlated with outcome; for example, higher age
cutoff for study entry correlated with 3-month mortality. Entry criteria also predicted baseline clinical characteristics;
for example, wider time window for study entry correlated directly with time to treatment and inversely with stroke
severity (initial National Institutes of Health Stroke Scale score). Baseline characteristics predicted outcome. Greater
stroke severity predicted higher 3-month mortality rate; despite this, successful thrombolytic trials have enrolled more
severe strokes than most trials. The mean age of enrollees also predicted 3-month mortality and was inversely related
to percentage of patients with 3-month Barthel Index score ⱖ95. The strongest predictors of 3-month mortality were
obtained with multivariate models.
Conclusions—Acute stroke studies vary widely in entry criteria and outcome measures. Across multiple studies,
differences in entry criteria, and the baseline clinical characteristics they predict, influence patient outcomes along a
continuum. In some studies, enrolling a specific subset of patients may have improved the chances of identifying a
treatment-related effect, while in others, such chances may have been reduced. These findings may be useful in the
design of future stroke therapeutic trials. (Stroke. 2001;32:909-916.)
Key Words: clinical trials 䡲 outcome 䡲 stroke, acute
A
A recent study noted the lack of consensus on acute stroke
trial outcome measures.8 There has been limited study of the
range of entry criteria used and the baseline characteristics
measured in acute stroke trials. These issues were explored in
the present study. Furthermore, the effect that variation in
entry criteria and baseline clinical characteristics has on
clinical outcome has received limited attention. This study
addressed this issue by exploring the hypothesis that the
clinical outcome of patients randomized to placebo group has
been very different across studies and that some of these
differences can be accounted for on the basis of variation in
entry criteria and baseline characteristics across studies.
number of reasons have been advanced to explain the
paucity of successful human stroke trials.1,2 Trial design
may also be important because it influences the population
under study and thus the likelihood of identifying a difference
between treatment and placebo groups.
Several aspects of trial design may be important to interpreting results of previous stroke studies. Stroke study entry
criteria result in enrollment of patients who are not representative of the stroke population in general.3,4 Selectively
enrolling a particular subpopulation of stroke patients could
affect study outcome, and therefore there is a need to better
understand how changes in entry criteria influence the specific population under study. Differences in baseline patient
characteristics can also influence study group outcomes. In
stroke studies using a single cohort of patients, a number of
clinical characteristics have been shown to influence final
clinical status. For example, age,5 stroke severity,6 and stroke
subtype7 each influence mortality rate.
Methods
Randomized, placebo-controlled, therapeutic trials of human patients
with acute ischemic stroke were identified by searching the Cochrane
Controlled Trials Register with the terms stroke, acute, and therapy.
Additional trials were identified through a MEDLINE search with
the keywords ischemic, stroke, acute, therapy, and trials. Secondary
Received July 10, 2000; final revision received November 3, 2000; accepted December 21, 2000.
From the Department of Neurology and Statistics, University of Washington, Seattle.
Correspondence to Steven C. Cramer, MD, University of Washington, Department of Neurology, 1959 NE Pacific St, Room RR650, Box 356465,
Seattle, WA 98195-6465. E-mail [email protected]
© 2001 American Heart Association, Inc.
Stroke is available at http://www.strokeaha.org
909
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April 2001
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references and review articles were also used to identify relevant
studies. Studies published in abstract form, having no placebo group,
or enrolling patients ⬎72 hours after stroke were excluded. When
treatment subgroups were prespecified and placebo group data were
reported separately, each subgroup was evaluated separately. Data
from intention-to-treat analyses were used whenever possible. Entry
criteria, baseline characteristics, and outcome measures were identified for the placebo group in each trial.
A wide range of entry criteria was used across stroke studies. Of
these, 2 entry criteria appeared most often and were extracted for
each study: the maximum age for study entry and the maximum time
from stroke onset to study entry (time window). The definition for
time of stroke onset varied across studies, ranging from the last time
the patient was known to be normal to the first time the patient was
found to have neurological abnormalities. The definition for time of
study entry also varied across studies and included time to treatment
and time to randomization. The minimum time window of 1 hour
specified in several trials was not further considered in the present
analysis.
A large number of baseline characteristics at study entry were
identified. Mean age, mean time from stroke onset to treatment,
median National Institutes of Health Stroke Scale (NIHSS) score,
and stroke subtype were noted for each study. For the purposes of
this analysis, median values were also accepted when mean values
were not reported. The definitions for each stroke subtype were
determined by the individual studies. As a secondary analysis, the
prevalence in placebo group patients was also noted for 8 other
baseline characteristics: previous stroke, previous transient ischemic
attack, aspirin use, hypertension, diabetes mellitus, coronary artery
disease, atrial fibrillation, and tobacco use.
Finally, across the studies, a large number of outcome measures
were used. Of these, mortality at 3 months and the percentage of
patients with a Barthel Index ⱖ95 at 3 months were extracted for
each study. Data collected at 90 days were not distinguished from
data collected at 3 months. A secondary analysis included the
mortality rate at 6 months, the percentage of patients with Barthel
Index ⱖ60 at 3 months, and the mean Barthel Index at 3 months. The
primary end point used by each trial was also noted.
The bivariate relationships between entry criteria and outcome
measures, between baseline characteristics and outcome measures,
and between entry criteria and baseline characteristics were evaluated with Spearman rank-order correlation statistic; significance was
set at P⬍0.05. We acknowledge that multiple tests may produce an
occasional spurious result when there are truly no relationships;
however, in the context of this analysis we believe that the consequences of missing an important relationship outweigh those of
reporting a spurious one.
Multiple linear regression was used to evaluate multivariate
relationships between entry criteria and baseline characteristics in
predicting outcome. In addition, because the NIHSS score is known
to be a powerful predictor of outcome,9 entry criteria were used to
model NIHSS score. For outcome measures that are proportions
(3-month mortality, percentage of patients with Barthel score ⬎95),
the arcsine–square root transform was used to stabilize variance.10
Because studies varied widely in the criteria and outcomes reported,
sample sizes available for multiple regression were consistently
smaller than the 90 studies surveyed. We considered only regression
models in which ⱖ10 studies reported both dependent and independent variables. Multivariate models were weighted according to
placebo group size.
Results
A total of 87 trials that met criteria were identified, published
during 1978 –2000.11–96 Three of these trials had 2 prespecified placebo subgroups, results for which were reported
separately, leading to a total of 90 placebo groups evaluated
and 31 919 patients. The trials evaluated a wide range of
stroke therapies, including neuroprotectants (29 trials),
thrombolytics (15 trials), calcium channel blockers (15 trials),
hemodilution (10 trials), anticoagulants (5 trials), and other
interventions (16 trials).
Table 1 describes the number of studies reporting each
entry criterion, baseline characteristic, and outcome measure
for placebo groups. The range of values is also shown, as is
the median value for these trial placebo groups.
A broad range of outcome measures was used across the
studies (Table 1). As a result, a limited number of studies
were available for comparison of each pair of variables. The
most common time for assessing clinical outcome was 3
months, followed by 1 month and 6 months after stroke.
Several measures were used to assess clinical outcome,
including survival, dependence, mobility, infarct volume,
cerebral blood flow, as well as the following 12 scales:
Barthel Index, NIHSS, Scandinavian Stroke Scale, Toronto
Stroke Scale, Canadian Neurological Scale, Glasgow Outcome Scale, modified Rankin Scale, modified Mathew Scale,
Orgogozo Scale, Turnhill weighted neurological scale, WHO
disability score, and European Stroke Scale.
Bivariate analysis identified several relationships between
entry criteria, baseline characteristics, and outcome (Table 2).
Across studies performed at many sites in numerous countries
across several decades, several variables were powerful
predictors of outcome. Entry criteria also predicted placebo
group characteristics at study entry. Examples are shown in
the Figure. Interestingly, the year of study publication correlated inversely with time window to treatment (r⫽⫺0.47,
P⬍0.0001), from a median of 72 hours in 1978 to 9 hours in
2000.
Few of the baseline characteristics considered in secondary
analyses showed a significant relationship with entry criteria
or outcome. In addition, none of the entry criteria or baseline
characteristics correlated with the 6-month mortality rate, the
3-month median Barthel Index, or the percentage of patients
with 3-month Barthel Index ⱖ60.
In multiple linear regression modeling, weighted by group
size, NIHSS score was the single best predictor of observed
3-month mortality, accounting for 91% of the observed
variation. Adding mean age further improved the model to
account for 95% of the variation in mortality, based on 16
studies reporting all 3 variables. Adding time to treatment
instead also improved the model, accounting for 96% of the
variation in mortality, based on 12 studies. To summarize, we
predict 3-month mortality as follows.
M3mo⫽[sin(Y)]2
where Y⫽0.049⫻NIHSS⫹0.0048⫻time to treatment⫺0.26
or Y⫽0.035⫻NIHSS⫹0.011⫻mean age⫺0.76.
Multivariate modeling did not improve univariate analysis
in predicting percentage of patients with Barthel score ⱖ95.
The NIHSS score was well predicted in a model that
combined 2 entry criteria. This model accounted for 62% of
the observed variation and was based on 11 studies:
NIHSSpredicted⫽0.42⫻maximum age⫺0.22
⫻time window ⫺20.9
where maximum age is the age cutoff for study entry (in
years) and time window is the maximum time from stroke
onset to study entry (in hours).
Uchino et al
Predictors of Outcome in Acute Stroke Trials
911
TABLE 1. Variables of Interest, Extracted From 90 Acute Stroke Trial
Placebo Groups
Variable
No. Studies
Reporting
Range of
Values
Median
Value
Entry criteria
Maximum age for study entry, y
32
69–90
80
Time window for treatment, h
89
1.5–72
12
Baseline characteristics
No. patients in placebo group
89
Mean age, y
77
54–78
8–10 552
71
Time to treatment, h
47
2.7–37
9
Median NIHSS score
22
5–19
12
Large-artery disease
10
18–69
38
Small-artery disease
22
0–60
21
Cardioembolic disease
17
10–75
26
Previous stroke
34
0–30
13
Previous transient ischemic attack
20
0–34
14
Atrial fibrillation
30
7–35
23
8
16–39
26
42
5–85
47
68
Stroke pathogenesis, % of patients
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Prevalence at time of stroke, % of patients
Previous aspirin use
Previous diagnosis hypertension
Previous diagnosis diabetes mellitus
39
5–37
18
Tobacco use
24
17–62
38
Previous diagnosis coronary artery disease
26
0–84
24
Outcome measures
3-mo mortality, %
47
0–53
17
3-mo % patients with Barthel ⱖ95
12
24–88
40
3-mo % patients with Barthel ⱖ60
10
47–71
60
3-mo median Barthel
10
50–95
67
6-mo mortality, %
13
19–39
28
Discussion
The present study of placebo group patients provides some
insight into how entry criteria and patient characteristics
influence outcome in acute stroke trials. Previous studies of a
single cohort of patients have described a number of predictors of outcome after stroke. Many of these relationships hold
true in placebo groups from randomized controlled trials of
acute ischemic stroke, despite great variability between trials
in the year, site, and conditions of evaluation.
Clinical trials vary widely in the choice of entry criteria.
These entry criteria determine which patients are included in
the study. Entry criteria also influence patient baseline
characteristics, and both influence clinical outcome and thus
the likelihood of finding a difference between treatment arms.
In particular, enrollment of patients with greater neurological
deficits or a smaller proportion of patients with small-artery
strokes is associated with higher mortality. Enrollment of
younger patients is associated with lower rates of placebo
group mortality and disability at 3 months. Patients enrolled
many hours after stroke onset have less severe deficits at time
of enrollment. Many of these relationships have been established in previous studies examining a single cohort of
patients, such as the relationship between early presentation
and greater stroke severity,97,98 or the influence of age5 and
stroke severity6 on mortality. The present study confirms the
role of these factors in acute stroke trials and furthermore
describes the continuum by which these principles are expressed across multiple studies of acute ischemic stroke.
Findings are based on data from placebo group patients, but
many of the same relationships are likely operative in those
patients randomized to active-treatment groups.
Many studies enroll patients whose outcome deviates from
studies of historical controls, though the latter may be
imperfect for comparison given their own vulnerability to
bias. In some instances, enrollment was biased toward patients with better than average outcomes. The mean age of
placebo group patients across 77 studies was 68 years. This is
somewhat lower than most,5,99,100 but not all,101 previously
published values, typically 73 to 74 years, in populationbased studies and stroke data banks. In 8 studies, the mean
age was lower, being ⬍64 years; the maximum age for study
entry was ⬍80 years in 3 of these studies, while in 4 of these
studies no maximum age was used. In 1 study that limited the
upper age for study enrollment to 69 years,48 the mean age of
placebo group patients was 58 years. As another example, in
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TABLE 2.
Bivariate Relationships Between Entry Criteria, Baseline Characteristics, and Outcome Measures
Predictor
Response
n
r
P
Entry criteria predicting outcome
Maximum age
3-mo mortality
20
0.52
0.018
Time window (among studies with time window ⱕ24 hs)
3-mo mortality
35
⫺0.41
0.014
Baseline characteristics predicting outcome
Mean age
NIHSS score
3-mo mortality
45
0.49
% patients with 3-mo Barthel ⱖ95
12
⫺0.77
3-mo mortality
16
0.89
0.0007
0.004
⬍0.0001
% patients with 3-mo Barthel ⱖ95
7
⫺0.77
0.041
% patients with small-artery disease
3-mo mortality
15
⫺0.80
0.0004
% patients with large-artery disease
3-mo mortality
6
0.83
0.042
% patients with 3-mo Barthel ⱖ95
5
0.90
0.0374
% patients with coronary artery disease
% patients with atrial fibrillation
% patients using tobacco
3-mo mortality
22
0.52
0.014
% patients with 3-mo Barthel ⱖ95
7
0.86
0.014
Mean age
30
0.54
0.002
0.96
⬍0.0001
Entry criteria predicting baseline measures
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Maximum age
Time window
Time to treatment
47
% patients with hypertension
41
⫺0.4
0.0099
Baseline NIHSS score
22
⫺0.54
0.009
% patients with small-artery disease
22
0.48
0.024
For each row, n is the number of studies reporting both variables; r is Spearman’s ␳. Note that the time window did not correlate
significantly (0.05⬍P⬍0.1) with 3-month mortality when evaluated across all studies, but a significant relationship was found among
studies with time window ⱕ24 hours.
7 studies,26,48,51,80,84,88,91 the 3-month placebo group mortality
rate was ⬍10%, also lower than the value found in most
stroke trials and in historical controls. Some stroke studies
enroll patients with greater severity of illness. The median
value for 6-month mortality rate was 28% among the placebo
groups of the 13 studies reporting this measure, with 3 studies
having rates ⬎30%. These values are higher than the rates of
21% to 22% reported in unselected historical controls.5,99
These variations from historical controls are in part due to
choice of entry criteria and study design.
Enrolling patients with a relatively high or low chance of
having a good outcome may influence the ability of a study to
demonstrate a treatment-related benefit. In some trials, enrolling patients with a poorer prognosis might increase the
likelihood of demonstrating a treatment-related benefit. Some
successful trials of thrombolytics have enrolled patients with
more severe strokes, their median NIHSS scores of 14 to 17
being higher than the value of 12 over all acute stroke studies
(Table 1). On the other hand, enrolling patients with a very
good prognosis may impede the ability to identify a significant treatment effect. The median value for 3-month Barthel
Index among placebo group patients in 10 stroke studies was
67, similar to the value of 69 found in historical controls.102
In 5 studies, however, the majority of placebo group patients
reached a Barthel Index score ⱖ95. In these studies, identifying a treatment-related effect would be very difficult using
this outcome measure. Trials most vulnerable to this effect
may be those that enter all patients regardless of stroke
subtype, those that use a relatively low age cutoff for study
entry, or those with a wide time window.
The results of this study may have value in the design of
future trials. The bivariate relationships (Table 2 and Figure)
and results of multiple linear regression modeling may be
useful for predicting placebo group morbidity and mortality
and thus for power calculations. Such predictions may aid in
selection of entry criteria as well as target baseline patient
characteristics. Studies that include variables most frequently
used in previous trials (those with the highest n in Table 1)
will best be able to make use of this information. Mortality is
a central issue in stroke studies, and its rate correlated
strongly with several entry criteria and baseline characteristics. Although 3 disability measures obtained at 3 months
after stroke (mean Barthel Index score, percentage of patients
with Barthel ⱖ60, percentage of patients with Barthel ⱖ95)
were used by studies with almost equal frequency (Table 1),
only the percentage of patients with Barthel ⱖ95 correlated
with baseline patient characteristics (Table 2). Among the 3,
this measure may therefore most reliably reflect differences
in clinical status between patients.
Several approaches can be used during study design to
ensure that the natural course of enrollees is not so favorable,
or unfavorable, as to reduce the likelihood of identifying a
possible treatment effect. Stratification, blocking, and other
randomization methods103 can be used to ensure that the
proportion of patients with certain characteristics, such as
those with small-artery disease or a particular range of
NIHSS scores, does not exceed a desired limit. Depending on
the drug under study, there may be a need to avoid enrolling
a large proportion of patients expected to have a very good
outcome, such as those with mild strokes or a young age. In
such a case, consideration should be given to setting the
Uchino et al
Predictors of Outcome in Acute Stroke Trials
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Nonparametric univariate correlations are shown for data extracted from the 90 placebo groups from randomized trials of patients with
acute ischemic stroke. The graphs depict the interrelationships between entry criteria, baseline characteristics, and clinical outcome.
Each point on a graph represents a single study. The value for n represents the number of studies that reported both variables for a
given comparison and could thus be evaluated. In each graph, n is ⬍90 because data for any pair of variables were only reported in a
subset of studies. a, The baseline characteristic “proportion of patients with small-artery disease” correlated with an increase in the
outcome measure “3-month mortality rate.” b, The baseline measure “NIHSS score” also correlated with this outcome measure. c, The
baseline measure “mean age at enrollment” correlated with the outcome measure “percentage of patients with a Barthel score ⱖ95 at
3 months.” d, The entry criterion “time window for study enrollment,” measured in hours, correlated with the baseline measure “NIHSS
score.”
maximum age for study entry to ⱖ80 years and to using a
time window for study entry ⱕ12 hours. Some studies, such
as Stroke Treatment with Acrod (STAT),96 Prolyse in Acute
Cerebral Thromboembolism (PROACT II),82 and part 2 of the
National Institute of Neurological Disorders and Stroke tissue
plasminogen activator (tPA) trial,58 specified a minimum
score on neurological testing as an entry criterion to specifically exclude patients with mild neurological deficits. Enrolling a selected population may, however, limit the extent to
which results can be generalized to all stroke patients.
Divergent entry criteria and outcome measures have been
used during the decades of acute stroke studies. As a result,
most of the statistical evaluations described in this report are
based on a limited number of comparisons. A wide range of
measures was used to assess outcome, including mortality,
radiological measures, physiological assessments, and combinations of 12 different neurological scales. Studies also
varied widely in choice of entry criteria. The number of
different entry criteria and outcome measures used in these
trials in part reflects the heterogeneity of the trials reviewed
and also highlights the nonuniformity of primary end points
in stroke therapeutic trials. This variability limits the ability to
make direct comparisons across studies and increases the
likelihood of a type I error in the present analyses.
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April 2001
The present analysis of clinical trials, restricted to patients
enrolled within 72 hours of stroke onset, confirms the
findings of Duncan et al,8 who analyzed a broader set of
stroke trials and found a lack of consensus on choice and
timing of clinical end points. In the present study a wide
range of values was also found for entry criteria and baseline
characteristics. Entry criteria, as well as the baseline patient
characteristics they predict, are significantly related to clinical outcome when evaluated across multiple stroke trials.
These resulting effects on placebo group outcomes may
influence the likelihood of demonstrating therapeutic efficacy. The present results provide some insight into how entry
criteria and baseline characteristics influence outcome measures in acute stroke trials and may be of utility in the design
of future trials.
Acknowledgments
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Dr Cramer is supported by grants from the National Institute of Child
Health and Human Development and the American Heart Association, Northwest Affiliate (to Dr Cramer).
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Entry Criteria and Baseline Characteristics Predict Outcome in Acute Stroke Trials
Ken Uchino, Dean Billheimer and Steven C. Cramer
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Stroke. 2001;32:909-916
doi: 10.1161/01.STR.32.4.909
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