A score to predict early risk of recurrence after ischemic stroke

Article (PDF Available)inNeurology 74(2):128-35 · December 2009with139 Reads
DOI: 10.1212/WNL.0b013e3181ca9cff · Source: PubMed
Abstract
There is currently no instrument to stratify patients presenting with ischemic stroke according to early risk of recurrent stroke. We sought to develop a comprehensive prognostic score to predict 90-day risk of recurrent stroke. We analyzed data on 1,458 consecutive ischemic stroke patients using a Cox regression model with time to recurrent stroke as the response and clinical and imaging features typically available to physician at admission as covariates. The 90-day risk of recurrent stroke was calculated by summing up the number of independent predictors weighted by their corresponding beta-coefficients. The resultant score was called recurrence risk estimator at 90 days or RRE-90 score (available at: http://www.nmr.mgh.harvard.edu/RRE-90/). Sixty recurrent strokes (54 had baseline imaging) occurred during the follow-up period. The risk adjusted for time to follow-up was 6.0%. Predictors of recurrence included admission etiologic stroke subtype, prior history of TIA/stroke, and topography, age, and distribution of brain infarcts. The RRE-90 score demonstrated adequate calibration and good discrimination (area under the ROC curve [AUC] = 0.70-0.80), which was maintained when applied to a separate cohort of 433 patients (AUC = 0.70-0.76). The model's performance was also maintained for predicting early (14-day) risk of recurrence (AUC = 0.80). The RRE-90 is a Web-based, easy-to-use prognostic score that integrates clinical and imaging information available in the acute setting to quantify early risk of recurrent stroke. The RRE-90 demonstrates good predictive performance, suggesting that, if validated externally, it has promise for use in creating individualized patient management algorithms and improving clinical practice in acute stroke care.
DOI: 10.1212/WNL.0b013e3181ca9cff
2010;74;128-135; originally published online Dec 16, 2009; Neurology
L. Furie, W. J. Koroshetz and A. G. Sorensen
H. Ay, L. Gungor, E. M. Arsava, J. Rosand, M. Vangel, T. Benner, L. H. Schwamm, K.
A score to predict early risk of recurrence after ischemic stroke
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A score to predict early risk of recurrence
after ischemic stroke
H. Ay, MD
L. Gungor, MD
E.M. Arsava, MD
J. Rosand, MD, MSc
M. Vangel, PhD
T. Benner, PhD
L.H. Schwamm, MD
K.L. Furie, MD
W.J. Koroshetz, MD
A.G. Sorensen, MD
ABSTRACT
Background: There is currently no instrument to stratify patients presenting with ischemic stroke
according to early risk of recurrent stroke. We sought to develop a comprehensive prognostic
score to predict 90-day risk of recurrent stroke.
Methods: We analyzed data on 1,458 consecutive ischemic stroke patients using a Cox regres-
sion model with time to recurrent stroke as the response and clinical and imaging features typi-
cally available to physician at admission as covariates. The 90-day risk of recurrent stroke was
calculated by summing up the number of independent predictors weighted by their corresponding
-coefficients. The resultant score was called recurrence risk estimator at 90 days or RRE-90
score (available at: http://www.nmr.mgh.harvard.edu/RRE-90/).
Results: Sixty recurrent strokes (54 had baseline imaging) occurred during the follow-up period.
The risk adjusted for time to follow-up was 6.0%. Predictors of recurrence included admission
etiologic stroke subtype, prior history of TIA/stroke, and topography, age, and distribution of brain
infarcts. The RRE-90 score demonstrated adequate calibration and good discrimination (area
under the ROC curve [AUC] 0.70 0.80), which was maintained when applied to a separate
cohort of 433 patients (AUC 0.700.76). The model’s performance was also maintained for
predicting early (14-day) risk of recurrence (AUC 0.80).
Conclusions: The RRE-90 is a Web-based, easy-to-use prognostic score that integrates clinical
and imaging information available in the acute setting to quantify early risk of recurrent stroke.
The RRE-90 demonstrates good predictive performance, suggesting that, if validated externally,
it has promise for use in creating individualized patient management algorithms and improving
clinical practice in acute stroke care.
Neurology
®
2010;74:128–135
GLOSSARY
AUC area under the ROC curve; CI confidence interval; DWI diffusion-weighted imaging; ROC receiver operating
characteristic; RRE-90 recurrence risk estimator at 90 days.
The risk of recurrent stroke is highest during the first 90 days after an index stroke; longitudinal
studies indicate that approximately 1 out of every 2 recurrences occurring in the first year
occurs within the first 90 days.
1-5
Prevention of such events is critical because early recurrence is
associated with severe consequences including longer duration of hospitalization and increased
neurologic disability and death.
6,7
Several recent studies consistently indicate that early initia-
tion of available treatments after TIA or minor stroke is imperative to avoid missed opportuni-
ties to prevent a recurrent stroke.
8-10
Nevertheless, prioritization occurs in clinical practice
because diagnosis and treatment of stroke is often constrained by queues, limited resources, or
even by patient reluctance.
11
Because the majority of patients will not have a subsequent
ischemic event after stroke, a tool that could identify patients at high risk of early recurrent
stroke, in whom urgent evaluation and intervention is most justified, would be of tremendous
benefit. There is currently no robust prognostic tool for predicting the short-term risk of
e-Pub ahead of print on December 16, 2009, at www.neurology.org.
From the Stroke Service, Department of Neurology (H.A., J.R., L.H.S., K.L.F.), AA Martinos Center for Biomedical Imaging, Department of
Radiology (H.A., L.G., E.M.A., M.V., T.B., A.G.S.), and Center for Human Genetic Research (J.R.), Massachusetts General Hospital, Harvard
Medical School, Boston; Program in Medical and Population Genetics (J.R.), Broad Institute of MIT and Harvard, Cambridge, MA; and National
Institute of Neurological Disorders and Stroke (W.J.K.), NIH, Bethesda, MD.
Disclosure: Author disclosures are provided at the end of the article.
Editorial, page 102
Supplemental data at
www.neurology.org
Address correspondence and
reprint requests to Dr. Hakan Ay,
A.A. Martinos Center for
Biomedical Imaging and Stroke
Service, Departments of
Neurology and Radiology,
Massachusetts General Hospital,
Harvard Medical School, 149
13th Street, Room 2301,
Charlestown, MA 02129
hay@partners.org
128 Copyright © 2010 by AAN Enterprises, Inc.
at Washington University on April 28, 2010 www.neurology.orgDownloaded from
recurrent stroke. Available prognostic models
such as the Stroke Prognosis Instrument II
12
and The Essen Stroke Risk Score
13
are de-
signed to predict the long-term risk and have
not been validated for short-term risk predic-
tion. Our goal was to develop a predictive
score based on information typically available
to the physician at the time of hospital admis-
sion to estimate the 90-day risk of recurrent
stroke.
METHODS Patient population. The primary derivation
cohort was retrospectively identified from consecutive patients
with ischemic stroke who were admitted within 72 hours of
stroke onset between 2003 and 2006 to a single center. We col-
lected data on previously published clinical
1-7,14-18
and imaging
features
7,19-23
of index stroke associated with increased risk of re-
Table 1 Baseline clinical and imaging characteristics
Recurrent stroke:
No (n 1,398)
Recurrent stroke:
Yes (n 60) HR (95% CI) p
Age, y, median (IQR) 72 (60–80) 74 (60–80) 1.01 (0.99–1.02) 0.529
Male gender, n (%) 749 (54) 34 (57) 1.08 (0.65–1.79) 0.781
Hypertension, n (%) 979 (70) 44 (73) 1.30 (0.73–2.30) 0.376
Diabetes mellitus, n (%) 336 (24) 13 (22) 0.85 (0.46–1.57) 0.609
Atrial fibrillation, n (%) 417 (30) 14 (23) 0.85 (0.46–1.54) 0.582
Smoking, n (%) 295 (21) 15 (25) 1.21 (0.67–2.17) 0.527
History of TIA or stroke within the month
preceding index stroke, n (%)
96 (7) 14 (23) 3.86 (2.12–7.02) 0.001
Admission NIH Stroke Scale score,
median (IQR)
7 (3–14) 5 (3–9) 0.97 (0.92–1.01) 0.120
Admission plasma glucose level
>140 mg/dL, n (%)
448 (32) 14 (23) 0.71 (0.39–1.29) 0.263
Admission CCS subtype, n (%)
Large artery atherosclerosis 315 (23) 23 (38) 10.65 (1.44–78.90) 0.001
Extracranial ICA stenosis 131 (9) 6 (10)
Cardio-aortic embolism 346 (25) 12 (20) 6.20 (0.81–47.72)
Small artery occlusion 152 (11) 1 (2) 1.00
Other causes, n (%) 72 (5) 11 (18) 20.87 (2.69–161.66)
Dissection 50 (4) 7 (12)
Other rare causes 22 (2) 4 (7)
Undetermined causes 513 (37) 13 (22) 3.74 (0.49–28.56)
Treatment after index stroke, n (%)
Anticoagulation 568 (41) 25 (42) 0.92 (0.55–1.54) 0.759
Antiplatelet therapy 939 (67) 41 (68) 0.91 (0.53–1.57) 0.742
Statin 791 (57) 30 (50) 0.65 (0.39–1.08) 0.095
ACE inhibitor/angiotensin receptor blocker 375 (27) 16 (27) 0.90 (0.51–1.59) 0.714
Carotid endarterectomy/angioplasty/stent 64 (5) 3 (5) 1.19 (0.37–3.81) 0.764
Time to carotid intervention, d, median (IQR) 5 (3–9) 5 (3–80) 1.01 (0.98–1.04) 0.536
Imaging predictors, n (%)
Multiple infarcts of different ages 134 (11) 20 (37) 4.27 (2.46–7.43) 0.001
Simultaneous infarcts in different
circulations
128 (11) 15 (28) 3.16 (1.74–5.74) 0.001
Multiple acute infarcts 544 (45) 41 (76) 3.65 (1.96–6.82) 0.001
Chronic infarcts 408 (34) 25 (46) 1.57 (0.92–2.69) 0.097
Internal watershed infarcts 47 (4) 5 (9) 1.99 (0.79–4.99) 0.143
Isolated cortical infarcts 125 (10) 12 (22) 2.07 (1.09–3.93) 0.026
Isolated lacunar infarcts 191 (16) 2 (4) 0.21 (0.05–0.86) 0.030
Subcortical with or without cortical infarcts 887 (74) 40 (74) 1.09 (0.60–2.01) 0.774
Abbreviations: CCS Causative Classification of Stroke System; CI confidence interval; HR hazard ratio; IQR
interquartile range.
Neurology 74 January 12, 2010 129
at Washington University on April 28, 2010 www.neurology.orgDownloaded from
current events through medical record search (table 1). MRI
analysis was performed by visual assessment of images obtained
at the time of index stroke blinded to the patient’s recurrence
status. Table 2 presents the definitions used for each imaging
feature. The image acquisition parameters were summarized in
detail elsewhere.
24
For each imaging feature, we calculated inter-
examiner reliability based on assessment of 200 consecutive pa-
tients by 2 examiners blinded to each other’s assignments.
Two investigators retrospectively acquired 90-day follow-up
information through review of data collected within the context
of a prospective study that assessed clinical outcome by phone
interview
25
and inspection of medical records and the social secu-
rity death index. A separate neurologist adjudicated recurrent
events by evaluating all pertinent brain images without the
knowledge of clinical and diffusion-weighted imaging (DWI)
characteristics of the index stroke.
All patients underwent an assessment of medical history,
physical examination, brain CT/CT angiography or MRI/mag-
netic resonance angiography, EKG, CBC, and blood chemistry.
The stroke etiology was classified according to the Causative
Classification of Stroke System
18
using information available af-
ter initial line of tests listed above.
Standard protocol approvals, registrations, and patient
consents.
The study protocol received approval by the local
institutional review board.
Generation of the predictive model and statistics. The
study end point was time to last known to be free of recurrent
stroke or nonstroke death. We considered that the end point was
reached after the first event in patients who had multiple recur-
rent events within 90 days. We defined recurrent stroke as a
clinical incident that is clearly attributable to a new area of brain
infarction visualized by imaging as spatially distinct from the
index lesion.
We used univariate Cox regression to identify baseline differ-
ences in clinical and imaging variables between patients with or
without recurrent stroke. We determined the risk of subsequent
stroke in relation to each variable by multivariable Cox regres-
sion analysis. The regression model included time to recurrent
stroke as response and imaging and clinical predictors of recur-
rence with a univariate p value 0.05 as independent variables.
We first generated a clinical-based model (model A) based on
pertinent clinical data in the univariate analyses (table 3). We
then constructed a second model that included important clini-
cal and imaging information available to the physician at the
time of hospital admission. This was called clinical- and
imaging-based model or model B (table 3).
All covariates except for etiologic stroke subtype were intro-
duced into the regression model as dichotomous variables. The
latter was introduced as dummy variables using small artery oc-
clusion as the reference category (table 3). We evaluated each
independent variable for adherence to the assumption of propor-
tional hazards and examined the data for collinearity. We as-
signed scores for each predictor variable generated by rounding
the corresponding
-coefficient from the regression model to the
nearest integer. We calculated an overall risk score by summing
up scores for each independent predictor for a given patient. We
quantified the predictive validity of the model by computing the
receiver operating characteristic (ROC) curves in patients with
90-day follow-up,
26
and compared the area under the ROC
curve (AUC) for different models using the Z-test.
26,27
We evalu-
ated the calibration of our models as a measure of agreement
between observed risk (estimated risk from the Kaplan-Meier
curve) and predicted risks stratified according to risk scores using
Hosmer-Lemeshow
2
statistic.
Validation of the predictive model. We used cross-
validation to provide an unbiased internal assessment of the
model’s accuracy. For this, the whole dataset was randomly par-
titioned into 2 halves