Outcomes of recurrent stroke in patients with atrial fibrillation according to presumed etiology

Desfecho de AVC recorrente em pacientes com fibrilação atrial de acordo com etiologia presumida

Abstract

Background

Atrial fibrillation (AF) is a potent risk factor for stroke. The presence of competing etiologies can modify disease outcomes and demand different treatment strategies.

Objective

The primary purpose of the study was to examine the differences in outcomes for patients with AF admitted with a recurrent stroke, stratified according to the presumed etiology of the stroke.

Methods

We analyzed AF patients admitted for a recurrent ischemic stroke in an academic comprehensive stroke center. Recurrent strokes were categorized as “Cardioembolic”, meaning AF without any competing mechanism, versus “Undetermined” etiology due to competing mechanisms. We used logistic regression to test the association between recurrent stroke etiology and favorable outcome (discharge home), after accounting for important covariates.

Results

We included 230 patients, with a mean age 76.9 (SD ± 11.3), 52.2% male, median National Institute of Health Stroke Scale (NIHSS) score of 7 (IQR 2–16). Patients with cardioembolic stroke (65.2%) had higher median NIHSS 8.5 (3–18) versus 3 (1–8) and were more likely to be treated with reperfusion therapies. The favorable outcome was reached by 64 patients (27.8%), and in-hospital mortality was 15.2% overall. After adjustment, there was no difference in outcome between patients with cardioembolic versus undetermined stroke etiology (odds ratio for discharge home: 1.41; 95% CI: 0.65–3.15).

Conclusions

In this single-center sample of AF patients with history of stroke, there was no difference in discharge outcomes between those with cardioembolic and those with undetermined stroke etiology. This question warrants examination in larger samples to better understand the importance of the stroke mechanism and secondary prophylaxis.

Keywords
Ischemic Stroke; Atrial Fibrillation; Embolic Stroke; Anticoagulants

Resumo

Antecedentes

Fibrilação atrial (FA) é um fator de risco importante para AVC. A presença de mecanismos concorrentes para o AVC pode modificar o desfecho e demandar estratégias de tratamento diferentes.

Objetivo

O objetivo primário do estudo foi examinar diferenças no desfecho de pacientes com FA admitidos por um AVC recorrente, sendo estratificados de acordo com a etiologia presumida do AVC.

Métodos

Nós analisamos pacientes com FA admitidos por conta de AVC recorrente em um centro acadêmico terciário de AVC. Os casos de AVC recorrentes foram classificados como “Cardioembólicos”, sendo FA sem outros mecanismos alternativos, versus aqueles de etiologia “Indeterminada” por conta de mecanismos concorrentes. Foi usada regressão logística para testar a associação entre a etiologia do AVC recorrente e desfecho favorável (alta direto para casa) após controle para covariáveis importantes.

Resultados

Nós incluímos 230 pacientes, com uma idade média 76,9 anos (DP ± 11.3), 52.2% homens, com um escore mediano do National Institute of Health Stroke Scale (NIHSS) de 7 (IIQ 2–16). Pacientes com AVC cardioembólicos (65,2%) tiveram um escore de NIHSS mediano mais alto 8,5 (3–18) versus 3 (1–8), e com maior chance de tratamento com terapias de reperfusão. O desfecho favorável ocorreu em 64 pacientes (27,8%) e a mortalidade institucional foi de 15,2% no total. Após ajustes, não encontramos diferença no desfecho entre pacientes com AVC cardioembólico versus AVC de etiologia indeterminada (odds ratio para alta para casa: 1,41; 95% IC: 0,65–3,15).

Conclusões

Nessa amostra de pacientes com FA e história de AVC recorrente de centro único, não houve diferença no desfecho de alta entre aqueles com AVC cardioembólico e aqueles com etiologia indeterminada. Essa questão deve ser examinada em amostras maiores para melhor compreender a importância do mecanismo do AVC e a profilaxia secundária.

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.³–⁸ 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) classification¹⁹ 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).

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,²³–²⁵ 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.²⁷–²⁹

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.

References

• ¹
  Wolf PA, Dawber TR, Thomas HE Jr, Kannel WB. Epidemiologic assessment of chronic atrial fibrillation and risk of stroke: the Framingham study. Neurology 1978;28(10):973–977
• ²
  Wang L, Ze F, Li J, et al. Trends of global burden of atrial fibrillation/flutter from Global Burden of Disease Study 2017. Heart 2021;107(11):881–887
• ³
  Silva Pinto S, Teixeira A, Henriques TS,Monteiro H, Martins C. AFReact study: atrial fibrillation management strategies in clinical practice-retrospective longitudinal study from real-world data in Northern Portugal. BMJ Open 2021;11(03):e040404
• ⁴
  Miyasaka Y, Barnes ME, Gersh BJ, et al. Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation 2006;114(02):119–125
• ⁵
  Friberg J, Scharling H, Gadsbøll N, Jensen GB. Sex-specific increase in the prevalence of atrial fibrillation (The Copenhagen City Heart Study). Am J Cardiol 2003;92(12):1419–1423
• ⁶
  Krijthe BP, Kunst A, Benjamin EJ, et al. Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. Eur Heart J 2013;34(35):2746–2751
• ⁷
  Marcolino MS, Palhares DMF, Benjamin EJ, Ribeiro AL. Atrial fibrillation: prevalence in a large database of primary care patients in Brazil. Europace 2015;17(12):1787–1790
• ⁸
  Colilla S, Crow A, PetkunW, Singer DE, SimonT, Liu X. Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population. Am J Cardiol 2013;112(08): 1142–1147
• ⁹
  Miller VT, Pearce LA, Feinberg WM, Rothrock JF, Anderson DC, Hart RGStroke Prevention in Atrial Fibrillation Investigators. Differential effect of aspirin versus warfarin on clinical stroke types in patients with atrial fibrillation. Neurology 1996;46(01): 238–240
• ¹⁰
  Hart RG, Pearce LA, Miller VT, et al. Cardioembolic vs. noncardioembolic strokes in atrial fibrillation: frequency and effect of antithrombotic agents in the stroke prevention in atrial fibrillation studies. Cerebrovasc Dis 2000;10(01):39–43
• ¹¹
  Paciaroni M, Agnelli G, Caso V, et al. Causes and Risk Factors of Cerebral Ischemic Events in Patients With Atrial Fibrillation Treated With Non-Vitamin K Antagonist Oral Anticoagulants for Stroke Prevention. Stroke 2019;50(08):2168–2174
• ¹²
  Schachter ME, Tran HA, Anand SS. Oral anticoagulants and noncardioembolic stroke prevention. Vasc Med 2008;13(01):55–62
• ¹³
  Diener H-C, Sacco RL, Easton JD, et al; RE-SPECT ESUS Steering Committee and Investigators. Dabigatran for Prevention of Stroke after Embolic Stroke of Undetermined Source. N Engl J Med 2019; 380(20):1906–1917
• ¹⁴
  Hart RG, SharmaM, Mundl H, et al; NAVIGATE ESUS Investigators. Rivaroxaban for Stroke Prevention after Embolic Stroke of Undetermined Source. N Engl J Med 2018;378(23):2191–2201
• ¹⁵
  Evans A, Perez I, Yu G, Kalra L. Should stroke subtype influence anticoagulation decisions to prevent recurrence in stroke patients with atrial fibrillation? Stroke 2001;32(12):2828–2832
• ¹⁶
  Hert L, Polymeris AA, Schaedelin S, et al. Small vessel disease is associated with an unfavourable outcome in stroke patients on oral anticoagulation. Eur Stroke J 2020;5(01):63–72
• ¹⁷
  Seiffge DJ,Wilson D, Ambler G, et al. Small vessel disease burden and intracerebral haemorrhage in patients taking oral anticoagulants. J Neurol Neurosurg Psychiatry 2021;92(08):805–814
• ¹⁸
  Gage BF, Waterman AD, Shannon W, Boechler M, Rich MW, Radford MJ. Validation of clinical classification schemes for predicting stroke: results from the National Registry of Atrial Fibrillation. JAMA 2001;285(22):2864–2870
• ¹⁹
  Adams HP Jr, Bendixen BH, Kappelle LJ, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 1993;24(01):35–41
• ²⁰
  Henninger N, Goddeau RP Jr, Karmarkar A, Helenius J, McManus DD. Atrial Fibrillation Is Associated With a Worse 90-Day Outcome Than Other Cardioembolic Stroke Subtypes. Stroke 2016;47 (06):1486–1492
• ²¹
  Adams HP Jr, Davis PH, Leira EC, et al. Baseline NIH Stroke Scale score strongly predicts outcome after stroke: A report of the Trial of Org 10172 in Acute Stroke Treatment (TOAST). Neurology 1999;53(01):126–131
• ²²
  Amaral CHD, Amaral AR, Nagel V, et al. Incidence and functional outcome of atrial fibrillation and non-atrial fibrillation- related cardioembolic stroke in Joinville, Brazil: a population-based study. Arq Neuropsiquiatr 2017;75(05):288–294
• ²³
  Rose AJ, Goldberg R, McManus DD, et al. Anticoagulant Prescribing for Non-Valvular Atrial Fibrillation in the Veterans Health Administration. J Am Heart Assoc 2019;8(17):e012646
• ²⁴
  Hsu JC,Maddox TM, Kennedy KF, et al. Oral Anticoagulant Therapy Prescription in Patients With Atrial Fibrillation Across the Spectrum of Stroke Risk: Insights From the NCDR PINNACLE Registry. JAMA Cardiol 2016;1(01):55–62
• ²⁵
  Bartholomay E, Polli I, Borges AP, et al. Prevalence of oral anticoagulation in atrial fibrillation. Clinics (São Paulo) 2014;69(09): 615–620
• ²⁶
  Kunt R, Kutluk MK, Tiftikçioğlu Bİ, et al. Comparison of conventional and modern methods in determining ischemic stroke etiology by general and stroke neurologists. Turk J Med Sci 2019;49(01):170–177
• ²⁷
  Zhang Q, Yang Y, Saver JL. Discharge destination after acute hospitalization strongly predicts three month disability outcome in ischemic stroke. Restor Neurol Neurosci 2015;33(05): 771–775
• ²⁸
  Asaithambi G, Tipps ME. Predictive value of discharge destination for 90-day outcomes among ischemic stroke patients eligible for endovascular treatment: Post-hoc analysis of DEFUSE 3. J Stroke Cerebrovasc Dis 2020;29(08):104902
• ²⁹
  ElHabr AK, Katz JM, Wang J, et al. Predicting 90-day modified Rankin Scale score with discharge information in acute ischaemic stroke patients following treatment. BMJ Neurol Open 2021;3(01):e000177

Publication Dates

• Publication in this collection
  21 Aug 2023
• Date of issue
  2023

History

• Received
  18 Sept 2022
• Reviewed
  31 Jan 2023
• Accepted
  16 Mar 2023