Keywords
Ischemic Stroke - Atrial Fibrillation - Embolic Stroke - Anticoagulants
Palavras-chave
AVC Isquêmico - Fibrilação Atrial - AVC Embólico - Anticoagulantes
INTRODUCTION
Atrial fibrillation (AF) is a potent risk factor for stroke, associated with up to
a five-fold increase in ischemic stroke risk.[1] Globally, the estimated number of individuals with atrial fibrillation and flutter
was 37.6 million in 2017.[2] It has an age-dependent prevalence of up to 3% in the adult population over 40 years
old, and several studies suggest that the prevalence of AF is rising.[3]
[4]
[5]
[6]
[7]
[8] Ischemic stroke patients with AF are at high risk of stroke recurrence. This risk
can be dramatically reduced by long-term anticoagulation therapy soon after the presenting
event. However, stroke in these patients is not necessarily cardioembolic;[9] nearly a third of strokes in patients with AF can have a noncardioembolic mechanism.[10]
[11]
In a metanalysis comparing oral anticoagulants versus control/placebo or antiplatelet
agents in noncardioembolic stroke patients there was no benefit of anticoagulation
therapy to prevent death, recurrent stroke or myocardial infarction (MI), and an increased
risk of major bleed.[12] Furthermore, two trials tested direct oral anticoagulants (DOAC) versus antiplatelet
agents in patients with an embolic neuroimaging phenotype but no documented embolic
source, and again no benefit of anticoagulation was shown.[13]
[14]
Many believe that secondary prophylaxis should be tailored according to the presumed
etiologic mechanism. Anticoagulation therapy may not prevent stroke recurrence in
noncardioembolic strokes.[15] Moreover, some patients, especially those with small vessel disease, could have
an increased risk of intracranial bleeding.[16]
[17] On the other hand, patients with AF and previous history of stroke also have an
increased risk of a future, possibly disabling, cardioembolic ischemic stroke.[16]
[18] While usual care for patients with ischemic stroke and atrial fibrillation is to
start oral anticoagulants, the presence of competing etiologies may modify disease
outcomes and, therefore, require different treatment strategies.
The primary purpose of this study was to analyze outcomes for AF patients admitted
with acute recurrent stroke, stratified according to the presumed etiology of the
stroke. As a secondary objective, we examined whether prestroke antithrombotic use
was associated with stroke subtype in this population.
We hypothesized that patients with previous AF and a recurrent cardioembolic stroke
would have a worse prognosis, evidenced by lower likelihood of being discharged home—when
compared to in-hospital mortality or discharge to a facility.
METHODS
This study was conducted among patients admitted with a recurrent ischemic stroke
and a previous diagnosis of AF or paroxysmal AF. We used data from our stroke registry
that included all consecutive patients with stroke from our urban academic comprehensive
stroke center from January 2015 to December 2020. We did not include patients for
whom AF was identified only at the index admission (i.e., without past history). We
also excluded patients for whom information of the stroke etiologic mechanism was
missing. The data collection project has been reviewed and approved by the MGH Institutional
Review Board and, given the retrospective nature of this study, and there being a
minimal risk to the subjects, informed consent was waived.
The trial of org 10172 in acute stroke treatment (TOAST) classification[19] was used for the registry and, in our study, patients were further categorized according
to the presumed stroke mechanism: definite “Cardioembolic”, meaning AF without a competing
mechanism; versus “Undetermined”, under which we grouped all other etiologies—given
the possibility of competing mechanisms besides the AF. The primary outcome of interest
was favorable (discharged home vs not). The study did not involve therapeutic intervention.
All patients were treated at the discretion of the stroke team, following validated
guidelines and institutional protocols.
Descriptive statistics are presented as mean/standard deviations (SD) for normally
distributed continuous variables, median/interquartile range for non-normally distributed
continuous and ordinal variables, and absolute numbers and proportion (%) for categorical
variables. The distribution was analyzed by visual inspection of the histogram and
with the Shapiro-Wilk test. To compare characteristics between etiologies, continuous
variables were compared using the Student t test or the Mann-Whitney U test as appropriate,
while categorical variables were compared using the Fisher exact test.
Logistic regression models were used to test the association between the presumed
stroke etiology and outcomes. We tested three predefined models in which variables
were chosen a priori, based on existing literature and clinical experience: model
1 adjusted for age, sex, anticoagulation status, receipt of intravenous alteplase
(IV tPA), receipt of mechanical thrombectomy (MT) and admission National Institute
of Health Stroke Scale (NIHSS). Model 2 included the aforementioned variables, as
well as patients' comorbidities (such as hypertension, diabetes, renal failure etc.).
Finally, model 3 included only comorbidities potentially associated with the binary
outcome in univariable analyses (p < 0.2). For each model, the association between stroke etiology and each outcome
was considered significant if the p-value < 0.05.
To examine the association of antithrombotic use and stroke etiology we used logistic
regression adjusting for age, sex, NIHSS and patients' comorbidities (hypertension,
diabetes, dyslipidemia, obesity, heart failure, renal failure).
Analyses were performed using the R statistical (R Foundation for Statistical Computing,
Vienna, Austria) software.
RESULTS
A total of 1,141 consecutive patients were admitted with a recurrent ischemic stroke
or transient ischemic attack (TIA) in the period of January 2015 to December 2020,
of which 230 met our inclusion criteria and were part of the analysis ([Figure 1]). A comparison between included and excluded patients is shown in [Table 4]. For the 230 included patients, the mean age was 76.9 years old (SD ± 11.3), and
120 (52.2%) were male. The majority of patients were white (81.7%), and the median
NIHSS score was 7 (interquartile range [IQR]: 2–16).
Abbreviation: AF, atrial fibrillation. Note: *Comparison with included patients is shown in [Table 4]. Figure 1 Patient inclusion flow diagram.
From the total of 230 patients included in this study, 150 (65.2%) had a cardioembolic
stroke (AF without other competing mechanism). Compared to patients with stroke of
undetermined mechanism, cardioembolic stroke patients had more severe strokes with
median NIHSS scores, 3 (1–8) versus 8.5 (3–18) respectively, and were more commonly
treated with reperfusion therapies: intravenous tissue plasminogen activator (IV tPA),
8.0 versus 2.5%, and mechanical thrombectomy (MT) 14.0 versus 3.8%, respectively ([Table 1]).
Table 1
Patients admitted with a recurrent stroke and known prior atrial fibrillation
|
All patients (n = 230)
|
Cardioembolic (n = 150)
|
Undetermined (n = 80)
|
P-value
|
|
Age
|
|
|
|
0.192
|
|
Mean (SD)
|
76.9 (11.3)
|
77.4 (11.8)
|
76.1 (10.4)
|
|
|
Median (IQR)
|
78 (69–86)
|
78 (69–87)
|
77 (68–84)
|
|
|
Male sex, n (%)
|
120 (52.2)
|
72 (48.0)
|
48 (60.0)
|
0.097
|
|
Race / ethnicity, n (%)
|
|
|
|
0.041
|
|
Hispanic
|
11 (4.8)
|
9 (6.0)
|
2 (2.5)
|
|
|
Non-H Asian
|
10 (4.4)
|
9 (6.0)
|
1 (1.3)
|
|
|
Non-H black
|
16 (7.0)
|
13 (8.7)
|
3 (3.8)
|
|
|
Non-H white
|
188 (81.7)
|
114 (76.0)
|
74 (92.5)
|
|
|
Unknown
|
5 (2.2)
|
5 (3.3)
|
0
|
|
|
Diabetes, n (%)
|
76 (33.0)
|
50 (33.3)
|
26 (32.5)
|
1.000
|
|
Hypertension, n (%)
|
181 (78.7)
|
115 (76.7)
|
66 (82.5)
|
0.398
|
|
Dyslipidemia, n (%)
|
146 (63.5)
|
89 (59.3)
|
57 (71.3)
|
0.085
|
|
Smoking, n (%)
|
20 (8.7)
|
12 (8.0)
|
8 (10.0)
|
0.628
|
|
Obesity / overweight, n (%)
|
60 (26.1)
|
43 (28.7)
|
17 (21.3)
|
0.270
|
|
Heart failure, n (%)
|
56 (24.4)
|
40 (26.7)
|
16 (20.0)
|
0.333
|
|
CAD / Prior MI, n (%)
|
82 (35.7)
|
51 (34.0)
|
31 (38.8)
|
0.474
|
|
Prosthetic Heart Valve, n (%)
|
4 (1.7)
|
3 (2.0)
|
1 (1.3)
|
1.000
|
|
Renal failure, n (%)
|
50 (21.7)
|
32 (21.3)
|
18 (22.5)
|
0.868
|
|
Antithrombotic use, n (%)
|
|
|
|
0.027
|
|
Anticoagulant
|
133 (57.8)
|
81 (54.0)
|
52 (65.0)
|
|
|
Antiplatelet only
|
72 (31.3)
|
47 (31.3)
|
25 (31.3)
|
|
|
Not on antithrombotics
|
25 (10.9)
|
22 (14.7)
|
3 (3.8)
|
|
|
CHADS2, median (IQR)
|
4 (3–5)
|
4 (3–5)
|
4 (3–5)
|
0.632
|
|
NIHSS, median (IQR)
|
7 (2–16)
|
8.5 (3–18)
|
3 (1–8)
|
< 0.001
|
|
Reperfusion therapy, n (%)
|
|
|
|
|
|
IV tPA
|
14 (6.1)
|
12 (8.0)
|
2 (2.5)
|
0.022
|
|
IA treatment
|
24 (10.4)
|
21 (14.0)
|
3 (3.8)
|
0.031
|
|
Favorable outcome (discharge home)
|
64 (27.8)
|
37 (24.7)
|
27 (33.8)
|
0.165
|
Abbreviations: CAD, coronary arterial disease; IA, intra-arterial; IQR, interquartile range; CHADS,
Congestive heart failure, Hypertension, Age, Diabetes, prior Stroke (stroke risk prediction);
IV tPA, intravenous tissue plasminogen activator; MI, myocardial infarction; NIHSS,
National Institute of Health Stroke Scale; SD, standard deviation.
There were 64 patients (27.8%) with a favorable outcome (discharged home after hospital
admission), and in-hospital mortality was 15.2%. In bivariate analyses, age and admission
NIHSS were associated with an unfavorable outcome ([Table 2]).
Table 2
Predictive factors for favorable outcome, (discharge home)
|
Variable
|
Unadjusted OR
|
95% CI
|
Adjusted OR
|
95% CI
|
|
Etiology
|
Undetermined
|
|
|
|
|
|
Cardioembolic
|
0.64
|
(0.36–1.17)
|
1.41
|
(0.65–3.15)
|
|
Antithrombotic use
|
Anticoagulant (reference)
|
|
|
|
|
|
Antiplatelet only
|
0.72
|
(0.37–1.36)
|
0.73
|
(0.31–1.68)
|
|
Not in use
|
0.41
|
(0.11–1.17)
|
0.62
|
(0.11–2.77)
|
|
Age (5 years)
|
0.81
|
(0.71–0.93)
|
0.85
|
(0.72–1.01)
|
|
Male sex
|
1.64
|
(0.91–2.97)
|
1.33
|
(0.60–2.98)
|
|
IV tPA
|
1.04
|
(0.28–3.24)
|
6.29
|
(1.11–35.56)
|
|
IA treatment
|
0.34
|
(0.08–1.03)
|
5.45
|
(0.66–46.66)
|
|
Admission NIHSS (4 points)
|
0.39
|
(0.27–0.54)
|
0.30
|
(0.18–0.45)
|
Abbreviations: CI, confidence interval; IA, intra-arterial; IV tPA, intravenous tissue plasminogen
activator; NIHSS, National Institute of Health Stroke Scale; OR, odds ratio.
After adjustment for important covariates (model 1), there was no association between
cardioembolic stroke etiology and favorable outcome (adjusted odds ratio [aOR]: 1.41,
95% confidence interval [CI] = 0.65–3.15). The patient characteristics that were associated
with being discharged home in adjusted analysis were: IV tPA (aOR 6.29, 95% CI = 1.11–35.56)
and admission NIHSS (aOR for each 4 points increase was 0.30, 95% CI = 0.18–0.45)
([Table 2]).
The additional analysis as per prespecified multivariate models including risk factors
(model 2) and variables potentially associated with the binary outcome in univariate
analyses (p < 0.2) (model 3), yielded similar results, with no significant association between
the presumed etiology and outcome.
When comparing patients who were admitted with acute stroke previously using anticoagulants,
antiplatelets, or neither ([Table 3]), the likelihood of a cardioembolic etiology (no competing mechanism) was higher
when none of the two agents were used when compared to anticoagulant use (OR = 4.71;
95% CI = 1.53–20.59).Furthermore, there was no difference between antiplatelet and
anticoagulant use (OR = 1.21; 95% CI = 0.67–2.21) for the likelihood of a cardioembolic
etiology.
Table 3
Predictive factors for cardioembolic etiology (no competing mechanism(s)
|
Variable
|
Unadjusted OR
|
95% CI
|
Adjusted OR
|
95% CI
|
|
Antithrombotic use
|
|
|
|
|
|
Anticoagulant (reference)
|
Antiplatelet only
|
1.21
|
(0.67–2.21)
|
1.30
|
(0.65–2.67)
|
|
Not in use
|
4.71
|
(1.53–20.59)
|
4.71
|
(1.12–33.74)
|
|
Age (5 years)
|
1.05
|
(0.93–1.18)
|
0.98
|
(0.84–1.14)
|
|
Male sex
|
0.62
|
(0.35–1.06)
|
0.63
|
(0.32–1.23)
|
|
Hypertension
|
0.70
|
(0.34–1.37)
|
0.55
|
(0.21–1.37)
|
|
Diabetes
|
1.04
|
(0.59–1.87)
|
0.88
|
(0.44–1.77)
|
|
Dyslipidemia
|
0.59
|
(0.32–1.05)
|
0.78
|
(0.36–1.68)
|
|
Obesity
|
1.49
|
(0.79–2.89)
|
1.69
|
(0.79–3.73)
|
|
Heart failure
|
1.45
|
(0.77–2.87)
|
1.67
|
(0.75–3.88)
|
|
Renal failure
|
0.93
|
(0.49–1.82)
|
0.95
|
(0.45–2.07)
|
|
Admission NIHSS (4 points)
|
1.45
|
(1.22–1.76)
|
1.40
|
(1.16–1.73)
|
Abbreviations: CI, confidence interval; OR, odds ratio; NIHSS, National Institute of Health Stroke
Scale.
DISCUSSION
This is a real-world, retrospective study using a cohort of patients with stroke treated
in a single-center. In the analysis of patients with previous AF and admission for
a recurrent stroke, it was found that prior anticoagulant use was associated with
stroke etiology; furthermore, we did not find an association between stroke etiology
and favorable poststroke outcome.
The most common serious arrythmia type is AF, and it accounts for the majority of
cardioembolic stroke cases. Such cases are known to be associated with worse outcomes
relative to other etiologies.[20] Furthermore, it has been shown that the NIHSS score predicts the likelihood of recovery
after stroke.[21] Accordingly, in our study, cardioembolic strokes were associated with a higher admission
NIHSS (median 8.5 [IQR: 3–18] vs. 3 [1–8], p < 0.001). However, we did not find an association of stroke etiology and likelihood
of favorable outcome. It is possible that this population, composed of AF patients
with prior strokes, was at a greater risk for unfavorable outcomes (occurred in 72%
of patients). Another possibility is that the relationship between stroke type and
outcome was confounded by the greater frequency of reperfusion therapies in cardioembolic
stroke patients (8.0 vs. 2.5% for IVtPA, and 14.0 vs. 3.8% for IA treatment). This
finding is similar to another study, which found that history of AF was not associated
with worse outcomes when compared with other cardioembolic strokes.[22]
Investigators in that study also drew attention to the lost opportunity of anticoagulation
therapy, especially in such a high-risk population. Consistent with data from stroke
registries that show an unjustifiable underuse of anticoagulation in atrial fibrillation
patients,[23]
[24]
[25] in our sample, more than 40% of patients were not on anticoagulation medication.
We found that the use of this type of therapy was lower in patients with cardioembolic
etiology of their recurrent stroke, supporting this need for optimization of secondary
prophylaxis.
This study has some limitations. First, given its retrospective nature and the use
of secondary data, we were unable to include patients for whom we did not have information
of the stroke mechanism, which may have introduced bias. However, it is reassuring
that the study population had similar baseline characteristics and clinical outcomes
when compared to the excluded population, suggesting representativeness ([Table 4]).
Table 4
Comparison with not-included patients (due to missing data)
|
All patients (n = 289)
|
Included patients (n = 230)
|
Not included (n = 59)
|
P-value
|
|
Age
|
Mean (SD)
|
77.3 (11.3)
|
76.9 (11.3)
|
78.6 (10.9)
|
0.304
|
|
Median (IQR)
|
78 (69–85)
|
78 (69–86)
|
81 (72.5–85)
|
0.296
|
|
Male sex, n (%)
|
156 (54)
|
120 (52.2)
|
36 (61)
|
0.244
|
|
Race / ethnicity, n (%)
|
Hispanic
|
11 (3.8)
|
11 (4.8)
|
0
|
0.444
|
|
Non-H Asian
|
12 (4.2)
|
10 (4.4)
|
2 (3.4)
|
|
Non-H black
|
21 (7.3)
|
16 (7)
|
5 (8.5)
|
|
Non-H White
|
238 (82.4)
|
188 (81.7)
|
50 (84.8)
|
|
Unknown
|
7 (2.4)
|
5 (2.2)
|
2 (3.4)
|
|
Diabetes, n (%)
|
92 (31.8)
|
76 (33)
|
16 (27.1)
|
0.436
|
|
Hypertension, n (%)
|
229 (79.2)
|
181 (78.7)
|
48 (81.4)
|
0.722
|
|
Dyslipidemia, n (%)
|
174 (60.2)
|
146 (63.5)
|
28 (47.5)
|
0.036
|
|
Smoking, n (%)
|
22 (7.6)
|
20 (8.7)
|
2 (3.4)
|
0.269
|
|
Obesity/overweight, n (%)
|
64 (22.2)
|
60 (26.1)
|
4 (6.8)
|
0.001
|
|
Heart failure, n (%)
|
70 (24.2)
|
56 (24.4)
|
14 (23.7)
|
1
|
|
CAD / Prior MI, n (%)
|
103 (35.6)
|
82 (35.7)
|
21 (35.6)
|
1
|
|
Prosthetic heart valve, n (%)
|
4 (1.4)
|
4 (1.7)
|
0
|
0.585
|
|
Renal failure, n (%)
|
59 (20.4)
|
50 (21.7)
|
9 (15.3)
|
0.365
|
|
Antithrombotic use, n (%)
|
Anticoagulant
|
168 (58.1)
|
133 (57.8)
|
35 (59.3)
|
0.167
|
|
Antiplatelet only
|
85 (29.4)
|
72 (31.3)
|
13 (22)
|
|
Not on antithrombotics
|
36 (12.5)
|
25 (10.9)
|
11 (18.6)
|
|
CHADS2, median (IQR)
|
4 (3–5)
|
4 (3–5)
|
4 (3–5)
|
0.957
|
|
NIHSS, median (IQR)
|
6 (2–16)
|
5 (2–14.25)
|
7 (2–16)
|
0.502
|
|
Reperfusion therapy, n (%)
|
IV tPA
|
16 (5.5)
|
14 (6.1)
|
2 (3.4)
|
0.539
|
|
EVT
|
24 (8.3)
|
24 (10.4)
|
0
|
0.006
|
|
Favorable outcome (discharge home)
|
83 (28.7)
|
64 (27.8)
|
19 (32.2)
|
0.521
|
Abbreviations: CAD, coronary arterial disease; EVT, Endovascular thrombectomy; IQR, interquartile
range; IV tPA, intravenous tissue plasminogen activator; MI, myocardial infarction;
NIHSS, National Institute of Health Stroke Scale; SD, standard deviation.
Second, this was a single center study in a university-based setting, which may not
generalize to community-based stroke centers. Third, the classification of the stroke
mechanisms was made by the treating team as part of the clinical practice; these classifications
may have greater interrater variation than if a validated formal classification algorithm
was used. We were unable to calculate a kappa score; however, in our study the patients
were reclassified as “cardioembolic” or “undetermined” (with other possible competing
etiologies) and the ‘cardioembolic’ subtype appears to have the highest interrater
agreement (> 90%).[26] Furthermore, this attribution reflects real-world practice and is representative
of patients for whom treatment decisions will be made.
While we were unable to compare long-term functional outcomes between groups due to
the nature of our registry data, previous studies have used discharge destination
as a valid measure of poststroke patient outcome.[27]
[28]
[29]
In conclusion, in this single-center comprehensive sample of patients with history
of previous AF and recurrent stroke, we found no difference in outcome between those
with cardioembolic versus undetermined stroke etiology, however this could be due
to a type 2 error. Given the limitations, our study cannot be interpreted as conclusive.
With the increasing detection of AF due to the availability of monitoring devices
and aging of the general population, this question should be examined in larger samples
to better understand secondary prophylaxis for stroke.
