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294
Community Hospital-Based Stroke Programs:
North Carolina, Oregon, and New York*
III. Factors Influencing Survival After Stroke: Proportional
Hazards Analysis of 4219 Patients
GEORGE HOWARD, M . S . ,
BRUCE COULL, M . D . ,
MICHAEL D. WALKER, M . D . ,
JOHN FEIBEL, M . D . ,
CAROLINE BECKER,
M.D.,
KENNETH M C L E R O Y , P H . D . , JAMES F. TOOLE,
AND FRANK Y A T S U ,
M.D.
M.D.
SUMMARY The possible effect of age, race, sex, consciousness upon admission, geographic location, and
history of selected risk factors on the survival after stroke due to infarction or hemorrhage was determined
using proportional hazards analysis (Cox regression). For each diagnostic category the most significant
prognostic factor was consciousness upon admission. Increasing age, cardiac disease, or previous stroke
also decreased the survival time of patients with infarctions. For patients with cerebral hemorrhage, no
other variable was significant after control for consciousness level.
Stroke Vol 17, No 2, 1986
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1
IN 1982 Check noted a lack of information useful for
predicting survival and rehabilitation following stroke.
Such information is needed to assist in making financial decisions concerning health care and in considering future care and disposition of stroke victims. Several authors2"4 have reported case fatality rates
associated with stroke, a method that fails to reflect
expected higher mortality with longer follow-up of
patients. Others5"22 have employed life table analysis, a
method that accounts for time dependency but only
permits univariate testing between groups of patients.
Survival has been reported to be influenced by factors
including age, consciousness, blood pressure, history
of cardio-pulmonary disease, previous stroke, specific
diagnosis, marital status, urban or rural residential
status, heart enlargement, Babinski sign, eye deviation, respiratory abnormality, mean body temperature, anemia, extremity paresis, and pulse. Only two
authors employed statistical techniques that allow for
the simultaneous analysis of multiple factors.17-26
Older age and reduced levels of consciousness coma
carry a poor prognosis. However, a stroke patient may
not be only old or only unconscious, but both old and
From the Department of Neurology, Bowman Gray School of Medicine of Wake Forest Medical Center; National Institute of Neurological
and Communicative Disorders and Stroke, Department of Neurology,
Department of Epidemiology, University of North Carolina School of
Public Health; Oregon Health Sciences University, University of Oregon School of Medicine; Department of Neurology, University of
Rochester; Research Triangle Institute, Research Triangle Park, North
Carolina. *North Carolina (NC) refers to data collected from all acute
care hospitals in a 15 county rural area in eastern North Carolina.
Oregon (OR) refers to data collected in community hospitals with approximately one-half of the acute care hospital beds in Oregon. New
York (NY) refers to data collected in all of the community hospitals in
Monroe County, New York.
This study was supported in part by Contract Nos. NOI-NS-2385,
NOI-NS-2386 and NOI-NS-2387 with the National Institute of Neurological and Communicative Disorders and Stroke, NIH, Bethesda, MD.
Address correspondence to: George Howard, M.S., Department of
Neurology, Bowman Gray University School of Medicine, 300 S. Hawthorne Rd., Winston-Salem, North Carolina 27103.
Received September 26, 1984; revision # 2 accepted January 9,
1986.
unconscious. Little has been published concerning the
combined effects of these two factors, nor is it known
whether the numerous factors affecting survival would
be significant after controlling for factors thought to be
more important, such as age and consciousness level.
For example, the sex of the patient may be related to
survival after stroke if considered by itself, but it may
have no statistical importance if one adjusts for differences in the age of the sexes.
Other authors have reported the influence on survival of stroke patients of many factors, such as age, race,
sex, consciousness level at admission, as well as history of previous stroke, transient ischemic attacks
(TIAs), cardiac disease, diabetes, and hypertension.5"22 In this report we examine the impact of these
factors, which have been found to affect survival following stroke by other authors, using proportional hazards analysis. This is a relatively new statistical technique, being introduced in the early 1970's, and allows
for the estimation of the joint effects of factors much
like the more familiar multiple regression or logistic
regression. We have also provided the analysis in a
"traditional" format (Kaplan-Meier survival estimates
and Breslow's Generalized Wilcoxon test) to allow the
reader to compare our population to the many previously reported in the literature.
Methods
Physicians in the Community Hospital-Based
Stroke Programs (CHSPs) of North Carolina, Oregon,
and Rochester (NY) kept detailed records on 4219
stroke patients enrolled during 1979 and 1980. The
combined data file as created included information on
the patients' survival time after admission to hospital,
age, race, sex, consciousness upon admission, and
history of selected risk factors. Other evaluations and
outcome measures, as well as further details concerning the joint project, are described elsewhere.23-24
The CHSP programs surveyed the hospital records
on patients signing informed consent (65%) and those
refusing (35%). u - 24 The survival status at hospital discharge was available for all patients, and hence, the
COMMUNITY HOSPITAL-BASED STROKE PROGRAMS/Howard et al
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survival status during the crucial hospitalization period
is available on all patients. In addition, we followed
those patients signing consent for a period of one year
after their stroke. This report is, hence, on the survival
during the first year following the stroke, but includes
the partial information on those patients refusing consent.
Factors considered in this analysis as possibly influencing survival after stroke, and the coding used for
analysis, are presented in Table 1. Kaplan-Meier life
table estimates and univariate tests (by Breslow's Generalized Wilcoxon test) are provided in Table 2 for
comparison of the CHSP population to the survival of
other populations reported in the literature.
Proportional hazards modeling of the survival of
these stroke victims is considered the primary analysis
because of its ability to examine the joint effects and
significance of the factors under consideration. In the
proportional hazards analysis variables considered to
affect survival significantly were selected in a modified forward stepwise manner and the magnitude of
their effects was estimated. After the factors found to
be significant had been selected, estimates of the survival curves were made by the methods detailed in
Kalbfleisch and Prentice.23 As a check of the proportional hazards model, if there were more than 45 patients at the onset in groups or strata of patients defined
by the significant variables we also estimated survival
by Kaplan-Meier methods. The patients within a strata
should be exposed to roughly the same risk, and, if the
proportional hazards model fits well, should produce a
similar estimated one-year survival as the KaplanMeier method.
Proportional hazards is an analysis of the differences
in "hazards" to which the populations are exposed. The
TABLE 1 Factors Considered as Possibly Influencing Survival
after Stroke, with Coding Used for Analysis
Variables
Codes
1. Age (linear)
2. Age (categorical)
Years
Variables for categories: <55,
55_64, 65-74, and 75 +
3. Race
4. Sex
5. Consciousness upon
admission (linear)
0 = white, 1 = non- white
0 = male, 1 = female
0 = alert, 1 = disoriented or
lethargic, 2 = stuporous or
comatose
Variables for alert, disoriented
or lethargic, stuporous or
comatose
Variables for: North Carolina;
Rochester, New York; and
Oregon
Each coded: 0 = negative
history; 1 = positive history
6. Consciousness upon
admission (categorical)
7. Geographic location of
center
8-12. History of hypertension,
cardiac disease, previous
stroke, previous transient
ischemic attack, or diabetes
Note: In addition, interactions were considered between: age and
admission consciousness, age and center, center and consciousness
level. In the final model selected for each diagnostic code the
interactions between selected variables were also tested where appropriate.
295
hazard is defined as the instantaneous probability of
death at a point of time conditional of being alive at
that time. In the evaluation of the significance of a
factor (eg., sex) the proportional hazards analysis assumes that the hazards of the two groups are proportional (multiplicative) with respect to time. For the
interpretation of the results the authors have employed
the "risk ratio," which is the ratio of the hazards between two groups of patients. Hence, if the ratio of two
groups (males vs. females) is estimated to be " 3 , " then
the interpretation that the males have an instantaneous
probability of death 3 times greater than their female
counterparts.
In either the Kaplan-Meier/Breslow's Generalized
Wilcoxon test analysis or the proportional hazards
analysis the significance of any factor is evaluated
relative to differences at any point in the survival
curve, not only at the endpoint. Hence, for in this work
(as well as work by other authors), "significant" factors should be interpreted as having an impact on survival during the entire follow-up period, not only at the
end of the period.
Results
Survival After Cerebral Infarction
When the 10 factors shown in Table 2 are considered individually four factors clearly (p < 0.001) affected survival of patients with infarctions: age, consciousness upon admission, history of previous stroke,
and cardiac disease. Older patients, patients with lower consciousness levels upon admission, and patients
with a history of previous stroke or cardiac disease
have a worse prognosis of survival to one year. To a
lesser degree, sex was seen possibly to influence outcome, with females having a poorer prognosis (p <
0.05). When considered individually, race, center
(North Carolina; Rochester, NY; or Oregon), and history of diabetes or hypertension were considered as not
influencing the chance of surviving to one year.
A forward stepwise proportional hazards procedure7
that considered all factors for 1468 patients, with complete data for all variables, selected consciousness
upon admission as the most important prognostic factor (p < 0.0001), followed by age (p < 0.0001),
history of cardiac disease (p = 0.0002), and history of
previous TIA (p = 0.0402). No other factor was found
to influence the estimated survival (p > 0.05). A proportional hazards analysis was performed with only
these factors to provide better estimates of effects and
significance. (More complete data for these variables
increased the sample size to 1566 patients.) History of
previous TIA, which was marginally significant on the
reduced sample size, was now found to be insignificant
(p = 0.0912). The patients age (p < 0.0001), admission consciousness (p < 0.0001), and history of cardiac disease (p < 0.0007) were found to affect estimated
one-year survival. The other factors not in the model
were considered to determine whether any contributed
information after controlling for these three. A history
of previous stroke did provide information that can be
considered marginally significant (p = 0.03), but not
296
STROKE
VOL 17, No 2, MARCH-APRIL
1986
TABLE 2 Kaplan-Meier Estimates of Survival and Univariate Significance by Individual Factors by Type of Stroke
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Infarct
N
203
407
762
1048
2150
274
1146
1283
582
631
1217
1058
646
252
1748
521
1565
521
853
1543
1730
564
937
1379
Hemorrhage
N
P value
111
.061
96
100
103
.003
333
77
.522
180
231
.022
113
132
167
88
<:.001
90
139
.211
352
41
293
.391
23
227
.226
172
.447
283
62
151
.649
202
Nonspecific
N
P value
58
<55
89
77
<.OO1
85
55-64
82
70
63
182
59
47
65-74
75
382
58
46
35
75 +
577
47
White
.243
55
.152
69
937
Race
Nonwhite
72
53
67
284
Male
.013
Sex
51
73
60
.177
576
52
Female
67
50
652
.722
54
68
Center
55
717
NC
.010
44
69
52
NY
201
56
72
57
312
OR
88
Adm. con.
Alert
<.001
-C.OOl
85
79
467
67
65
59
D/L
261
24
32
18
S/C
299
53
Prev. str.
<.OO1
62
73
830
No
.139
Yes
42
61
327
42
72
64
Prev. TIA
.077
55
.031
418
No
Yes
67
50
51
108
<.001
57
81
70
Car. dis.
<.001
No
425
Yes
43
63
738
46
70
No
Diabetes
.969
.868
51
447
58
Yes
68
64
186
55
.676
.795
48
Hyperten.
No
69
386
58
70
Yes
411
58
57
Abbreviations: Adm. con. = consciousness upon admission; prev. str. = history of previous stroke; prev. TIA
history of previous TTA; car. dis. = history of previous cardiac disease; hyperten. = hypertension.
Factor
Age
Level
%Surv
P value
<.001
providing a major contribution. After history of previous stroke was included in the model, no other factor
contributed any further significant information (p >
0.05). Also, the categorical (nonlinear) effects of age
and consciousness upon admission, as well as the interactions between the variables selected were insignificant (p > 0.05).
Table 3 provides a summary of the results with the
estimated coefficients (BETA) in the model and the
estimated risk ratio. This risk ratio may be interpreted
as the estimated ratio of hazards (probability of death
at time "t" conditional on survival to "t") associated
with a change for each of the variables, after controlling for the other variables in the model. For each year
increase in age the hazard is 1.025 times greater. For
each decrease in consciousness level, the hazard is
estimated to be about 2.4 times greater. Thus, the
hazard is about 2.4 times greater for patients who are
disoriented or lethargic as compared to alert patients,
and there is the same increase in hazard associated with
patients who were stuporous or comatose as compared
to patients disoriented or lethargic. As compared to
patients with no history of cardiac disease or stroke,
the hazard is estimated to be about 1.8 times greater for
patients with a history of cardiac disease, and about
1.3 times greater for a history of previous stroke.
%Surv
%Surv
Table 4 provides survival estimates for patients at
arbitrarily selected ages (55, 70, and 85 years), each
level of consciousness upon admission, and history of
previous cardiac disease and stroke. Estimates were
made for all combinations by the proportional hazards
model, and by Kaplan-Meier methods for strata with
more than 45 patients at onset. There is generally good
agreement between the survival estimates made by
either method; only 2 of 13 Cox estimates fell outside
the two standard errors of the estimate by KaplanMeier. The range of survival estimates is large: at age
55, alert patients who have no history of cardiac disease survived one year an estimated 93 percent of the
time, and at age 85, stuporous-comatose patients who
have had both cardiac disease and previous stroke survived only an estimated 13 percent of the time.
Survival Following Cerebral Hemorrhage
The influence of individual factors (Table 2) on survival following cerebral hemorrhage are different from
those following an infarction. Again, consciousness
upon admission is the overwhelming factor, with 88
percent of alert patients surviving for a year, but only
24 percent of the stuporous or comatose patients surviving for a year. Age, which is quite important in
patients who have an infarct is of borderline signifi-
COMMUNITY HOSPITAL-BASED STROKE PROGRMAS/Howard et al
TABLE 3 Simultaneous Evaluation of Factors Influencing Survival After Stroke by Diagnostic Category
Factor
p value
Beta
Risk
ratio
Infarct
Factor
Age (linear)
<.0001
.025
1.025
Consciousness upon
admission (linear)
-C.OOOl
.886
2.425
History of cardiac disease
<.0O01
.573
1.773
History of previous stroke
.0301
.252
1.286
<.0001
1.246
3.478
<.0001
.870
2.386
Hemorrhage
Factor
Consciousness upon
admission (linear)
Non-specific stroke
Factor
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Consciousness upon
admission (linear)
(for North Carolina or
Rochester)
Consciousness upon
admission (linear)
(for Oregon)
History of cardiac disease
297
one year estimated proved to agTee well with KaplanMeier estimates.
Survival After a Nonspecific Diagnosis of Stroke
Age, consciousness upon admission and history of
cardiac disease, when considered individually (Table
2), influence survival of patients who did not receive a
specific diagnosis of infarct or hemorrhage. Geographic center and history of previous TIA proved to be
marginally significant prognostic indicators when considered individually.
The proportional hazards modeling of survival of
patients with a nonspecific diagnosis indicated that
consciousness upon admission was clearly the most
significant factor, but that its effects were not consistent among the geographic centers (Table 3). The effect of consciousness upon admission was found not to
differ significantly between North Carolina and Rochester (NY), where the hazard was estimated to increase
about 2.4 times for each level of decrease in the level
TABLE 4
Survival Estimates at One Year by Proportional
Haz-
ards Model
<.0001
.0001
1.321
.587
3.745
1.799
Notes: Beta is the estimated coefficient in a proportional hazards
analysis. The risk ratio provides a clinical interpretation of the
magnitude of the effect associated with the factor, and is the estimated ratio of hazards (or instantaneous probability of death) between two groups of patients alike on all traits but that under
consideration. For example, the risk ratio of 1.025 associated with
the factor age for patients with infarcts indicates that, other factors
held constant, with each year increase of age the hazard of death
increases 1.025 times.
cance for the patients who have had a hemorrhage.
History of previous stroke and cardiac disease, both
highly prognostic factors for patients who have had an
infarct, provides no significant predictive information
about patients who have had a hemorrhage. Survival
for one year of nonwhite patients was estimated to be
67%, but survival of white patients for one year was
estimated to be only 47%. The geographic location of
the treatment center proved to have marginally significant effects. Again, no prognostic information was
related to the sex of the patient or a history of previous
TIA, diabetes, or hypertension.
The proportional hazards model clearly showed, as
for patients who had a cerebral infarct, the most important prognostic factor for survival after a hemorrhage is
the level of consciousness at admission. When the
level of consciousness was controlled, no other factor
contained additional significant (p > 0.05) prognostic
information. As compared to alert patients, the hazard
rate for disoriented or lethargic patients was estimated
to be about 3.5 times greater. The hazard rate is again
about 3.5 times greater for lowering the consciousness
level to stuporous or comatose from disoriented or
lethargic. Table 4 gives the estimated survival probabilities by the proportional hazards model, and the
Consc.
level
Age
Cardiac
disease
Prev.
stroke
55
70
85
Alert
No
No
.93
.90
.86
Alert
No
Yes
.91
.88
.82
Alert
Yes
No
.88
.83
.77
Alert
Yes
Yes
.85
.79
.71
D-L
No
No
.84
.78
.70
D-L
No
Yes
.80
.73
.63
D-L
Yes
No
.74
.64
.53
D-L
Yes
Yes
.68
.57
.46
S-C
No
No
.66
.55
.41
S-C
No
Yes
.59
.46
.32
S-C
Yes
No
.48
.34
.21
S-C
Yes
Yes
.39
.25
.13
Infarcts
Hemorrhages
Alert
.89
D-L
.66
.23
S-C
~
Consc.
level
Diagnosis of non-specific stroke*
r> ACenter
Cardiac
disease
NC or NY
Oregon
Alert
No
.86
.86
Alert
Yes
.77
.77
D-L
No
.70
.57
D-L
Yes
.53
.37
S-C
No
.43
.12
S-C
Yes
.22
.02
Abbreviations: consc. level = consciousness level; prev. stroke
= previous stroke; NC = North Carolina; NY = Rochester, New
York; D-L = disoriented or lethargic; S-C = stuporous or comatose.
•Results have no clinical usefulness in predicting survival for one
year because of the extreme heterogeneity of the group.
298
STROKE
of consciousness. In Oregon the decrease in level of
consciousness was accompanied by larger increases in
the hazard, estimated to be about 3.8 times greater per
level of change in consciousness. History of cardiac
disease also proved to be a significant prognostic factor. When the level of consciousness was controlled,
the hazard was estimated to be about 1.8 times greater
for patients with a positive history of cardiac disease.
When consciousness upon admission and history of
cardiac disease were controlled, no other factor contained additional significant information.
Survival estimates from the proportional hazards
model are provided in Table 4. The agreement between
these estimates and Kaplan-Meier estimates was moderate: two of the eight proportional hazard estimates
falling outside of two standard errors of the KaplanMeier estimates.
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Discussion
For the physician who may measure a large array of
factors and then attempt to estimate a patient's chances
of survival, there is little information available about
the joint effects of prognostic factors on survival. In
this paper, we have established variables of prime importance and estimated their effects on the chances of
survival.
For any type of stroke, consciousness upon admission is clearly the prime factor in the chances of a
patient's survival. This variable probably serves as a
measure of the severity of the stroke and the function
remaining in the damaged tissue, which is a major
underlying factor of increased mortality. For all three
categories of stroke, the increased hazard from alertness to disorientation or lethargy was not significantly
different from the increased hazard from disorientation
or lethargy to stupor or coma.
Age only provided prognostic information (after
controlling for other variables) for the patients who
have had an infarct. This effect of age may berelatedto
sequelae factors that cause death, such as older patients
being at greater risk of death from pneumonia secondary to the stroke. We had anticipated that the survival
of patients who had a nonspecific diagnosis would
resemble the survival of the patients who had an infarct, since it is reasonable to assume the majority of
the patients with a nonspecific stroke diagnosis were in
truth infarcts.
Thefindingthat age, race, sex, and a history of risk
factors do not influence survival of patients who have
had a hemorrhage may have important clinical implications. Since older patients who have several risk
factors and who are alert after a hemorrhage have the
same prognosis as younger patients who have no risk
factors, these older patients should receive treatment
based on the assumption of a favorable outcome.
A history of cardiac disease is highly significant for
patients who have had either an infarct or nonspecific
diagnosis. The elevated risk of cardiac events associated with TLA or stroke is common knowledge. In patients who have had an infarct and most of those whose
diagnosis was nonspecific (since most nonspecific pa-
VOL 17, No
2, MARCH-APRIL
1986
tients are likely to be infarcts), heart disease and stroke
probably related to the same underlying process of
atherosclerosis. In patients who have a history of heart
disease, atherosclerosis is more advanced and the risk
for associated diseases is greater.
History of previous stroke proved to be marginally
important in prediction of survival following infarct,
again possibly a marker for advanced atherosclerosis.
Many of the risk factors considered to be related to
the onset of stroke did not prove to be risk factors for
death after a stroke. That race, sex, and history of
diabetes or hypertension did not influence survival appears to contradict medical knowledge, since for example, diabetic patients remain diabetic after their
stroke and hence "carry" the risk factor for a repeat
event. However, there is noreasonto expect the development of the disease to have the same process as the
course after the onset. For example, hypertension is
commonly recognized as a major risk factor for stroke,
but a history of hypertension did not prove to be predictive of survival following the event. As in all analysesfindingnorelationshipbetween factors, this lack of
a significant link might be. due be established with an
increased sample size.
Although Table 4 may be used quickly and easily to
estimate survival of a patient for a year, the results
must be interpreted with caution. As for any statistical
modeling technique, proportional hazards analysis requires many assumptions. These assumptions include
multiplicative, not additive, effects of factors; and proportional hazards across time. We caution the reader
not to use clinically the results from the nonspecific
stroke patients. Both the heterogeneity of the nonspecific stroke group, as well as the incomplete follow-up
of the patients greatly decrease the clinical utility of
this information. The results were presented only to
account for all patients in the study. Table 4 simply
provides estimates of survival and represents the average chances of survival in a large group of stroke
patients. Alert, young patients with no history of risk
factors may not survive; comatose elderly patients with
history of cardiac disease and previous stroke may not
die within a year. The proportional hazards model does
provide the statistically proper simultaneous evaluation of the effect of multiple factors on the survival of
stroke patients.
References
1. Check WA: Predicting outcome for stroke, TIA patients. JAMA
247: 2080, 1982
2. Carpenter RR, Reed DE: The outcome for patients with cerebrovascular disease in university and community hospitals. Stroke 3:
747-757, 1972
3. Kuller L, Anderson H, Peterson D, Cassel J, Spiers P, Curry H,
Paegel B, Saslaw M, Sisk C, Wilber J, Millward D, Winkelstein
W, Iilienfeld A, Seltser R: Nationwide cerebrovascular disease
morbidity study. Stroke 1: 86-99, 1970
4. Pincock JG: The natural history of cerebral thrombosis. Ann Intern
Med 46: 925-930, 1957
5. Abu-Zeid HAH, Choi NW, Hsu PH, Maini KK: Prognostic factors
in the survival of 1,484 stroke cases observed for 30 to 48 months:
I. Diagnostic types and descriptive variables. Arch Neurol 35:
121-125, 1978
6. Abu-Zeid HAH, Choi NW, Hsu PH, Maini KK: Prognostic factor?
COMMUNITY HOSPITAL-BASED STROKE PROGRAMS/tfowarJ et al
7.
8.
9.
10.
11.
12.
13.
Downloaded from http://stroke.ahajournals.org/ by guest on June 12, 2017
14.
15.
16.
in the survival of 1,484 stroke cases observed for 30 to 48 months:
n. Clinical variables and laboratory measurements. Arch Neurol
35: 213-218, 1978
Baum HM, Robins M: "Survival and prevalence" in Stroke 12; The
National Survey of Stroke, Weinfeld FD (ed.), American Heart
Association, 59-68, 1981
David NJ, Heyman A: Factors influencing the prognosis of cerebral
thrombosis and infarction due to atherosclerosis. J Chronic Dis 11:
394-404, 1960
Dyken ML: Precipitating factors, prognosis, and demography of
cerebrovascular disease in an Indiana community: A review of all
patients hospitalized from 1963 to 1965 with neurological examination of survivors. Stroke 1: 260-269, 1970
Haerer AF, Woosley PC: Prognosis and quality of survival in a
hospitalized stroke population from the south. Stroke 6: 543-548,
1975
Herman B, Schulte BPM, van Luijk JH, Leyten ACM, Frenken
CWGM: Epidemiology of stroke in Tilburg, the Netherlands.
Stroke 11: 162-165, 1980
Kaste MK, Waltimo O: Prognosis of patients with middle cerebral
artery occlusion. Stroke 7: 482-485, 1976
Katz S, Ford AB.Chinn AB, Newill VA: Part II. Long term course
of 159 patients. Medicine 45: 236-246, 1966
Marquardsen J: "Natural history and prognosis of cerebrovascular
disease," In Cerebral Arterial Disease, Russell R (ed.). ChurchillLivingstone, New York, 24-39, 1976
Matsumoto N, Whisnant JP, Kurland LT, Okazaki H: Natural
history of stroke in Rochester, Minnesota, 1955 through 1969: An
extension of a previous study, 1945 through 1954. Stroke 4:
20-29, 1973
Sacco RL, Wolf PA, Kannel WB, McNamara PM: Survival and
recurrence following stroke. Stroke 13: 290-295, 1982
299
17. Rabkin SW, Mathewson RAL, Tate RB: The relation of blood
pressure to stroke prognosis. Ann Intern Med 89: 15-20, 1978
18. Robinson RW, Cohen WD, Higano N, Meyer R, Lukowsky GH,
McLaughlin RB, MacGilpin HH: Life-table analysis of survival
after cerebral thrombosis — Ten-Year Experience. JAMA 169:
1149-1152, 1959
19. Sacquegna T, DeCarolis P, Pazzaglia P, Andreoli A, Limoni P,
Testa C, Lugaresi: The clinical course and prognosis of carotid
artery occlusion. J Neurol Neurosurg Psychiatry 45: 1037-1039,
1982
20. Waltimo O, Kaste M, Fogelholm R: Prognosis of patients with
unilateral extracranial occlusion of the internal carotid artery.
Stroke 7: 480-482, 1976
21. Whisnant JP, Fitzgibbons JP, Kurland LT, Sayer GP: Natural history of stroke in Rochester, Minnesota, 1945 through 1954. Stroke
2: 11-22, 1971
22. Zupping R, Roose M: Epidemiology of cerebrovascular disease in
Tartu, Estonia, USSR, 1970 through 1973. Stroke 7: 187-190,
1976
23. YatsuFM, Becker C, McLeroy KR, Coull B, Feibel J, Howard G,
Toole JF, Walker MN: Community Hospital-Based Stroke Programs: North Carolina, Oregon, and New York. I. Goals, Objectives and data collection procedures. Stroke (in press)
24. Becker C, HowardG, McLeroy KR, Yatsu FM, Toole JF, Coull B,
Feibul J, Walker MN: Community Hospital-Based Stroke Programs: North Carolina, Oregon, and New York. II. Description of
Study Population. Stroke (in press)
25. Kalbfleisch JD, Prentice RL: The statistical analysis of failure time
data. John Wiley and Sons, 1980
26. Solzi P, Ring H, Najenson T, Luz Y: Hemiplegics after a first
stroke: Late survival and risk factors. Stroke 14: 703-709, 1983
Community Hospital-based Stroke Programs: North Carolina, Oregon, and New York. III.
Factors influencing survival after stroke: proportional hazards analysis of 4219 patients.
G Howard, M D Walker, C Becker, B Coull, J Feibel, K McLeroy, J F Toole and F Yatsu
Stroke. 1986;17:294-299
doi: 10.1161/01.STR.17.2.294
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