(Stroke. 1997;28:537-542.)
© 1997 American Heart Association, Inc.
Articles |
Variables Associated With Hospital Arrival Time After Stroke
Effect of Delay on the Clinical Efficiency of Early Treatment
From the Units of Neurology (G.A., L.F., F.N., R.D'A.), Clinical Pharmacology and Therapeutics (L.B.), and Emergency Medicine (G.R.), S Orsola-Malpighi Hospital, Bologna, and Cesena Local Health Care Authority, Medical Health Care Management Unit (U.M., D.C.), Italy.
Correspondence to Dr Giuseppe Azzimondi, Servizio di Neurologia, Ospedale S Orsola-Malpighi, Via Albertoni 15, 40138 Bologna, Italy. E-mail clinphar{at}orsola.med.unibo.it.
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Abstract |
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Background and Purpose A limiting criterion for the eligibility of patients in clinical trials investigating acute stroke therapies is that time between onset of symptoms and arrival in the hospital should fall within the "therapeutic window." The aims of this study were to estimate hospital arrival time in an unselected sample of stroke patients, to assess the association with some clinical and demographic variables, and to evaluate the effects of the delay on the clinical efficiency of an effective treatment.
Methods We evaluated the delay in hospital arrival time in 189 patients (84 men, 105 women; mean age, 76.5 years) prospectively collected in the S Orsola-Malpighi Community Teaching Hospital in Bologna, Italy. Cutoffs of 2 and 5 hours were chosen to allow for hypothetical treatment within 3 and 6 hours, respectively. Exact multiple logistic regression was used to predict the delay as a function of dichotomized age, sex, symptoms on awakening, day of the week, hour of the day, area of residence, level of consciousness, and level of motor power defect. We then projected the effectiveness of tissue plasminogen activator (TPA) on disability as estimated with the aid of the odds ratio from the National Institute of Neurological Disorders and Stroke (NINDS) rt-PA Stroke Trial onto our unselected sample to evaluate clinical efficiency of treatment as a function of arrival time and of hypothetical effects of educational efforts to reduce it.
Results The mean interval between onset of symptoms and
hospital arrival was 680 minutes; 59 patients (31%) arrived within 2
hours and 100 (53%) within 5 hours. Onset of symptoms when awake,
drowsiness or coma, and paralysis of at least one limb were the only
independent predictors of hospital arrival within 2 and 5 hours in both
the total sample and the subgroup of patients who were awake at stroke
onset. The effectiveness of 17%, extrapolated with the aid of the odds
ratio of 1.6 of having a favorable outcome (Barthel Index 
95 at 3
months) in treated versus untreated patients in the NINDS rt-PA Stroke
Trial, corresponded to a projected clinical efficiency of 5%. This
could be doubled by hypothesizing a 100% effect of educational efforts
in reducing the delay in hospital arrival time.
Conclusions Patients with milder symptoms, for whom treatment might be more effective, were less likely to arrive in time for therapy. The proposed model of the relationship between the delay in hospital presentation after a stroke and the clinical efficiency of a given treatment might be useful for planning future clinical trials on early stroke treatment and predicting the impact of an educational program aimed at shortening arrival time.
Key Words: disability evaluation • hospitalization • models, theoretical • stroke management
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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To evaluate emerging therapies for stroke, it is critical to be able to recruit patients within a few hours from onset of symptoms. This interval, the "therapeutic window," is considered to be within 3 to 4 hours for clinical trials testing thrombolysis and 4 to 6 hours for those evaluating cytoprotective therapies.1 2 Although apparently simple, the concept of therapeutic window actually includes a complex set of multiple windows, each corresponding to a distinct aspect of the phenomenon.1 Since the physiopathology of stroke and timing of brain damage are extremely variable, precise measurement of structural and metabolic cerebral markers would be preferable to temporal criteria, but with present technology these measurements are difficult to obtain in almost all hospitals. Thus, in almost all clinical trials (particularly large, simple, and multicenter ones) the inclusion criteria involve the interval between the onset of symptoms and the time of arrival in the hospital.
Several studies investigating the delay in hospital arrival time after stroke have been published.3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Some of these studies provided contradictory results on the role of the type of stroke on the delay in hospital arrival.4 5 6 7 10 11 12 13 Barsan et al14 15 found that time of day, geographic context, and the type of organization of the emergency services influenced the delay. Furthermore, there is clear evidence that efforts in public and professional education have a positive effect on reducing delay of hospital arrival time.6 15 Nevertheless, the wide differences in geographic areas, ethnic characteristics, and healthcare organization among different countries make it difficult to generalize the results from single studies. No data are yet available on this topic in Italy.
The aims of our study were to determine the delay in hospital arrival time after a stroke and to investigate its possible association with some variables in an unselected sample of stroke patients prospectively collected in the S Orsola-Malpighi Community Teaching Hospital in Bologna, Italy. We also estimated the effect of arrival time after a stroke on the clinical efficiency of an effective treatment by projecting the results of the NINDS rt-PA Stroke Trial18 regarding the use of TPA within 3 hours of the onset of stroke among our total sample of stroke patients.
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Subjects and Methods |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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The data collected and analyzed in this report are part of a wider prospective observational study that is under way at the S Orsola-Malpighi Hospital in Bologna, Italy. People in the northeastern half of the urban area and in the towns up to 15 km from Bologna are served by our hospital, which contained at the time 2543 beds distributed in 67 wards, including 14 medical departments where patients with acute stroke were usually treated. The overall project was planned in three stages: first, the baseline prognosis of patients with acute stroke was evaluated (stage 1); then guidelines were introduced to improve and standardize the entire approach (nursing, diagnostic, and therapeutic) to these patients in the hospital (stage 2); finally, data collection after the introduction of the guidelines (stage 3) is in progress. The patients considered in this report were those from stage 1. The diagnosis of stroke was based on the criteria proposed by the World Health Organization.19 A detailed description of the method concerning the study sample and the data collection of the entire study has been published.20
Data Collection
Data recorded in the general database and considered in the
present report included the following: hour and day of onset of
symptoms and of hospital arrival (if symptoms were present on
awakening, the hour and the day of onset were arbitrarily recorded as
the last time the patient did not present the symptoms of stroke); age;
sex; presence of symptoms on awakening; day of the week; and area of
residence. Level of consciousness, classified according to Plum and
Posner,21 and severity of motor power defect were both
recorded during the first 24 hours after hospital arrival. The Barthel
Index score22 recorded at 3 months after the stroke in
surviving patients described their disability status.
Statistical Methods
We considered 1 hour as the time necessary between arrival time
and the possibility of effectively beginning treatment. Therefore, the
cutoffs of 2 and 5 hours were separately chosen for the time of
hospital arrival to allow for treatment within 3 and 6 hours of onset,
respectively. For each cutoff, patients were divided into two groups
(
cutoff versus >cutoff), then a univariate analysis tested the
effect on the delay in hospital arrival. The following dichotomized
variables were considered: age (>median versus median); sex (men
versus women); presence of symptoms on awakening (yes versus no);
weekday or weekend (Monday to Friday versus Saturday and Sunday); day
or night (8 AM to 7:59 PM versus 8
PM to 7:59 AM); area of residence (Bologna
versus other localities); level of consciousness (fully alert versus
drowsy or comatose); and level of motor power defect (paralysis of at
least one limb versus paresis or no motor defect). The variables
associated with the delay in hospital arrival in the univariate
analysis were introduced stepwise as covariates in an exact multiple
logistic regression analysis23 with the aforementioned
dichotomized time of hospital arrival as the dependent variable, and
ORs with 95% CIs were provided. At each step, the covariates with a
nonsignificant OR at the 5% level were removed, then the forward
stepwise procedure was continued to arrive at a basic model.
Interactions among the remaining covariates were tested and added if
significant. If two covariates were each significant when added
stepwise when they appeared singly but not when they appeared together,
alternative models were reported. In the case of alternative models,
the 
coefficient was used to describe the association between the
two covariates.
We projected the NINDS results regarding the use of TPA within 3 hours of the onset of stroke18 onto our unselected sample of stroke patients as a function of arrival time of our patients and hypothetical educational efforts to reduce delay.
We used exact logistic regression to investigate possible effects
on unfavorable outcome (Barthel Index score at 3 months 90 or death)
in our sample. The covariates were arrival time (2 h versus >2 h)
and level of consciousness and level of motor power defect dichotomized
as above. Since arrival time was not significantly related to
unfavorable outcome in the multivariate model, the probability of
unfavorable outcome was estimated from our total sample. The OR and
95% CI obtained from Table 418 of the NINDS rt-PA Stroke
Trial were used to extrapolate a range of probabilities of unfavorable
outcome if our patients had been treated with TPA.
The number of additional patients (A) with favorable
outcome (F) attributable to timely treatment with TPA
(NAF) was estimated as follows:
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2 is number of
patients arriving within 2 hours; NT is total number of
patients in our sample;
PUt=1/1+{ORx[(NT-NU)/NU]}
,
corresponding to the extrapolated probability of unfavorable
outcome if timely TPA were administered; N>2
is number of patients arriving after 2 hours; and PUnt is
NU/NT, corresponding to the estimated
probability of unfavorable outcome (from our sample) if no TPA were
administered.
We defined effectiveness as the proportion of timely arrived patients
with unfavorable outcome who could be saved if they had been treated,
estimated as
100%x[NAF/(N2xPUnt)].
Clinical efficiency was then modeled in our sample as the proportion of
all patients with unfavorable outcome who could be saved additionally,
calculated as 100%x(NAF/NU).
Since it would be almost impossible for an educational program to
reduce the arrival time of patients with symptoms on awakening to less
than 2 hours, educational effort was modeled as shifting only patients
with onset while awake from late to timely arrival. For example, a 25%
educational effect would have estimated the number of patients arriving
within 2 hours as N2 plus 25% of the
fraction of N>2 with onset while awake, to
arrive at a new value of N2.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Baseline Characteristics
Of 204 patients recruited in stage 1 of the entire study, 15 were excluded from the present analysis because of lack of information about the hour of onset of symptoms. Complete data were thus available for 189 patients (mean age, 76.5 years; median, 79 years; range, 45 to 95 years; 84 men [44%]). Symptoms were present on awakening in 58 (31%), the hour of onset of stroke was between 8 AM and 7:59 PM in 113 (60%), stroke occurred on a weekday in 137 (73%), and 157 (83%) came from the city area. One hundred forty-one patients (75%) were fully alert, and 120 (64%) presented paresis or absence of motor power defect. The overall outcome at 3 months for the total sample was as follows: death, 32% (61 patients); high disability (Barthel Index=0 to 50), 16% (30 patients); moderate disability (Barthel Index=55 to 90), 19% (35 patients); and mild or no disability (Barthel Index=95 to 100), 33% (63 patients). All 63 with favorable outcome had been fully alert, and only 3 of them had had a paralysis of at least one limb.
Delay in Hospital Arrival Time
The mean time between onset of symptoms and hospital arrival time
was 680 minutes (median, 250 minutes; range, 10 to 8550 minutes).
Cumulatively, 59 patients (31%) arrived in the hospital within 2
hours, 100 (53%) within 5 hours, and 170 (90%) within 24 hours. The
distribution of delay over the complete time range in our series is
shown in Fig 1
.
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Univariate Analysis
Hospital arrival after 2 or 5 hours was not associated with age,
sex, day of the week, or area of residence. A lower probability of
arriving later (corresponding to OR <1 in Table 1) was
found for patients with drowsiness or coma or paralysis of at least one
limb. The opposite effect (OR >1 in Table 1) was found for those with
symptoms on awakening or nighttime onset.
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Multivariate Analysis
ORs and 95% CIs for all models are summarized in Table 2. The multiple logistic regression analysis of the
total sample with the cutoff of 2 hours led to two alternative models
without interactions. Both contained as the most significant covariate
the presence of symptoms on awakening, followed by an indicator of
clinical severity, and neither contained time of onset. We determined a
value of =0.68 between level of consciousness and level of motor
power defect. Analogously, for the subgroup of 131 patients in whom
stroke occurred when they were awake, two alternative models based on
clinical severity described the data.
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When the cutoff of 5 hours was chosen as dependent variable, the calculations, performed for the total sample of 189 patients, indicated that the presence of symptoms on awakening was associated with a higher probability of arriving after 5 hours, whereas the presence of drowsiness or coma had the opposite effect. When we considered only the 131 patients in whom stroke occurred when they were awake, presence of drowsiness or coma was a predictor of early arrival, as was paralysis of at least one limb, in two alternative models.
Effects of Arrival Time on Clinical Efficiency of a Hypothetical
Treatment
In our sample, 16.7% effectiveness in treated patients,
extrapolated from the OR=1.6 of having a favorable outcome (Barthel
Index 95 at 3 months) in the NINDS rt-PA stroke trial, corresponded
to a projected clinical efficiency of 5.2%. The same value of clinical
efficiency could be obtained from many combinations of effectiveness
and educational effect. For example, a clinical efficiency of 5.2%
could be obtained with an effectiveness of 16.7% and no educational
effect, an effectiveness of 10.4% and 50% educational effect, or an
effectiveness of 7.5% and 100% educational effect (Fig 2).
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Approximately one third of our stroke patients arrived at the hospital within 2 hours from the onset of symptoms and more than half within 5 hours. These results were obtained under baseline conditions, and during the study no educational program had been started. Probably the predominantly urban area of the territory referring to our hospital and the good organization of the out-of-hospital emergency services accounted for these results. Our findings were similar to those reported by previous studies,3 6 8 9 10 11 12 13 14 15 16 17 but comparisons are difficult because of the use of different methods. The studies of Eriksson et al3 and Anderson et al12 had a population-based design; Morris et al16 reported a series of patients admitted to an acute stroke unit; Barer et al8 included only patients with evident limb weakness; and Herderscheê et al11 excluded patients with a devastating stroke; Alberts et al6 as well as Anderson et al12 considered the hour of awakening as the onset of stroke for patients awakening with symptoms, while for similar patients Fogelholm et al13 assumed that stroke occurred during the night at 3 AM; Barsan et al14 15 calculated percentages excluding patients who arrived after 24 hours, thus obtaining values not generalizable to the total population of stroke patients. Furthermore, the results of Alberts et al6 and Barsan et al14 15 were achieved after an intensive public and professional educational effort to reduce the delay of arrival time after stroke.
Investigating the variables possibly associated with time of presentation in the hospital, we did not consider the type of stroke (ischemic versus hemorrhagic). CT scan was performed in only 60% of our patients because the main design of the study required that we avoid whatever bias could have modified the diagnostic and therapeutic approaches to the patients before guidelines were introduced.
The higher probability of later hospital arrival for patients with symptoms on awakening was obviously exaggerated by the artificial definition of onset time of stroke as the last time without symptoms, but it must be noted that for clinical trial purposes such patients are generally not candidates for inclusion because it is not possible to reconstruct the exact hour of onset. Having a stroke during sleep represented a real limit not modifiable by human intervention.
Our analysis, performed in both the total sample and the subgroup of
patients who were awake at stroke onset, yielded clinical severity,
measured as impairment of level of consciousness or as level of motor
power defect, as the second independent predictor of arriving in the
hospital later than 2 or 5 hours after onset. Except for one analysis
(total sample with 5-hour cutoff), the two aforementioned covariates
were found to predict hospital arrival time in alternative distinct
models. This may have been due to the association of these two
covariates; the coefficient was 0.68, and a larger sample size
might have permitted the separation of the effects of the covariates.
In any case, our data showed that drowsiness or coma and paralysis of
at least one limb were both associated with a lower risk of arriving
later (OR <1). This might be due to different factors, both clinical
(eg, absence of pain, mild symptoms) and cultural (eg, waiting for
spontaneous regression of symptoms, nihilism of physicians). Our result
agreed with the finding of Barer et al,8 while it differed
from those of two previous reports.9 10 Paradoxically, our
result indicated that patients with milder symptoms, for whom a
hypothetical treatment might be more effective, would be less likely to
be eligible for therapy because of delay in arrival time. Previous
studies have demonstrated the largely beneficial effects of public and
professional educational programs, and thus it is reasonable to hope
that more patients with mild disease will present early to the hospital
if such programs are implemented.
It is well known that timing is a crucial factor in determining the eligibility of patients for clinical trials for acute stroke, and in the future such difficulties could represent a real problem in administering effective treatment. Therefore, we tried to estimate how the clinical efficiency (ie, benefit with respect to an unselected population of stroke patients) of a therapy with proven effectiveness (ie, the experimental setting of the clinical trial) could be modified by the variable arrival time.
Considering that only patients arriving within 2 hours from onset of symptoms could be treated within 3 hours, we found that 16.7% effectiveness, extrapolated from the OR of 1.6 of favorable outcome for treated patients in the NINDS rt-PA Stroke Trial,18 gave rise to a clinical efficiency of less than one third of the effectiveness. Furthermore, hypothesizing a 100% effect of the educational program, we estimated that clinical efficiency doubled to approximately two thirds of the effectiveness of the treatment.
The NINDS rt-PA Stroke Trial did not stratify patients by clinical
severity, and therefore it is not possible to estimate the effects of
TPA in different subgroups. In any case, it is likely that the
probability of obtaining such a favorable outcome (Barthel Index 95)
is higher in less severely affected patients. Because patients with
lower clinical severity were less likely to arrive in the hospital
within 2 hours, an educational effort, if successful, could increase
clinical efficiency substantially in our population.
Obviously our projection of the NINDS data onto our unselected sample of consecutive stroke patients without distinction of the type of stroke (ischemic versus hemorrhagic) was artificial, although to some extent it was self-correcting through the introduction of our odds in the estimate of PUt. This was an extreme simplification, and different types and subtypes could actually require different kinds of treatments. Our results suggested that the clinical efficiency in an unselected population of stroke patients of a treatment found to be effective in a selected sample could be strongly influenced by hospital arrival time. Obviously, the intrahospital delay (eg, CT brain scan and neurological evaluation) is a limiting variable at least as important as hospital arrival time, and it will be evaluated in stage 3 of our study.
In conclusion, our results indicated that patients with less severe stroke arrived later after the onset of stroke. Previous studies have demonstrated that educational programs for the public and physicians shorten the delay of presentation time after a stroke. Therefore, these programs could be powerful tools in our population, and every effort should be made to reduce hospital arrival time as much as possible, especially for patients with milder symptoms. This could help to recruit more patients for clinical trials and to extend the beneficial effects of therapy to the largest possible number of patients, thus increasing its clinical efficiency.
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Selected Abbreviations and Acronyms |
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Acknowledgments |
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This study was supported in part by MURST (60%) (Ministero dell'Università e della Ricerca Scientifica e Tecnologica). This study would not have been possible without the cooperation of the registered nurses Vera Serra, Rovena Rubini, and Eleonora Conti, who were responsible for daily review of the emergency room reports. We thank Anne Collins for revising the English.
Received June 16, 1996; revision received November 4, 1996; accepted November 18, 1996.
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