Development and validation of a score to detect paroxysmal atrial fibrillation after stroke

Risk scores for prediction of paroxysmal atrial fibrillation after acute ischemic stroke or transient ischemic attack: A systematic review and meta-analysis

Introduction

Detection of paroxysmal atrial fibrillation (PAF) is crucial for secondary prevention in patients with recent strokes of unknown etiology. This systematic review and meta-analysis assess the predictive power of available risk scores for detecting new PAF after acute ischemic stroke (AIS).

Methods

PubMed, Embase, Scopus, and Web of Science databases were searched until September 2023 to identify relevant studies. A bivariate random effects meta-analysis model pooled data on sensitivity, specificity, and area under the curve (AUC) for each score. The QUADAS-2 tool was used for the quality assessment.

Results

Eventually, 21 studies with 18 original risk scores were identified. Age, left atrial enlargement, and NIHSS score were the most common predictive factors, respectively. Seven risk scores were meta-analyzed, with iPAB showing the highest pooled sensitivity and AUC (sensitivity: 89.4%, specificity: 74.2%, AUC: 0.83), and HAVOC having the highest pooled specificity (sensitivity: 46.3%, specificity: 82.0%, AUC: 0.82). Altogether, seven risk scores displayed good discriminatory power (AUC ≥0.80) with four of them (HAVOC, iPAB, Fujii, and MVP scores) being externally validated.

Conclusion

Available risk scores demonstrate moderate to good predictive accuracy and can help identify patients who would benefit from extended cardiac monitoring after AIS. External validation is essential before widespread clinical adoption.

Machine Learning Modeling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients

BACKGROUND

In patients with embolic stroke of undetermined source (ESUS), occult atrial fibrillation (AF) has been implicated as a key source of cardioembolism. However, only a minority acquire implantable cardiac loop recorders (ILRs) to detect occult paroxysmal AF, partly due to financial cost and procedural inconvenience. Without the initiation of appropriate anticoagulation, these patients are at risk of increased ischemic stroke recurrence. Hence, cost-effective and accurate methods of predicting AF in ESUS patients are highly sought after.

OBJECTIVE

We aimed to incorporate clinical and echocardiography data into machine learning (ML) algorithms for AF prediction on ILRs in ESUS.

METHODS

This was a single-center cohort study that included 157 consecutive patients diagnosed with ESUS from October 2014 to October 2017 who had ILR evaluation. We developed four ML models, with hyperparameters tuned, to predict AF detection on an ILR.

RESULTS

The median age of the cohort was 67 (IQR 59-74) years old and the median monitoring duration was 1051 (IQR 478-1287) days. Of the 157 patients, 32 (20.4%) had occult AF detected on the ILR. Support vector machine predicted for AF with a 95% confidence interval area under the receiver operating characteristic curve (AUC) of 0.736-0.737, multilayer perceptron with an AUC of 0.697-0.708, XGBoost with an AUC of 0.697-0.697, and random forest with an AUC of 0.663-0.674. ML feature importance found that age, HDL-C, and admitting heart rate were important non-echocardiography variables, while peak mitral A-wave velocity and left atrial volume were important echocardiography parameters aiding this prediction.

CONCLUSION

Machine learning modeling incorporating clinical and echocardiographic variables predicted AF in ESUS patients with moderate accuracy.

Performance of Different Risk Scores for the Detection of Atrial Fibrillation Among Patients With Cryptogenic Stroke

BACKGROUND

Atrial fibrillation (AF) is a frequent underlying cause of cryptogenic stroke (CS) and its detection can be increased using implantable cardiac monitoring (ICM). We sought to evaluate different risk scores and assess their diagnostic ability in identifying patients with CS with underlying AF on ICM.

METHODS

Patients with CS, being admitted to a single tertiary stroke center between 2017 and 2022 and receiving ICM, were prospectively evaluated. The CHA2DS2-VASc, HAVOC, Brown ESUS-AF, and C2HEST scores were calculated at baseline. The primary outcome of interest was the detection of AF, which was defined as at least 1 AF episode on ICM lasting for 2 consecutive minutes or more. The diagnostic accuracy measures and the net reclassification improvement were calculated for the 4 risk scores. Stroke recurrence was evaluated as a secondary outcome.

RESULTS

A total of 250 patients with CS were included, and AF was detected by ICM in 20.4% (n=51) during a median monitoring period of 16 months. Patients with CS with AF detection were older compared with the rest (P=0.045). The median HAVOC, Brown ESUS-AF, and C2HEST scores were higher among the patients with AF compared with the patients without AF (all P<0.05), while the median CHA2DS2-VASc score was similar between the 2 groups. The corresponding C statistics for CHA2DS2-VASc, HAVOC, Brown ESUS-AF, and C2HEST for AF prediction were 0.576 (95% CI, 0.482-0.670), 0.612 (95% CI, 0.523-0.700), 0.666 (95% CI, 0.587-0.746), and 0.770 (95% CI, 0.699-0.839). The C2HEST score presented the highest diagnostic performance based on C statistics (P<0.05 after correction for multiple comparisons) and provided significant improvement in net reclassification for AF detection (>70%) compared with the other risk scores. Finally, stroke recurrence was documented in 5.6% of the study population, with no difference regarding the 4 risk scores between patients with and without recurrent stroke.

CONCLUSIONS

The C2HEST score was superior to the CHA2DS2-VASc, HAVOC, and Brown ESUS-AF scores for discriminating patients with CS with underlying AF using ICM.

Atrial fibrillation in embolic stroke of undetermined source: role of advanced imaging of left atrial function

AIMS

Atrial fibrillation (AF) is detected in over 30% of patients following an embolic stroke of undetermined source (ESUS) when monitored with an implantable loop recorder (ILR). Identifying AF in ESUS survivors has significant therapeutic implications, and AF risk is essential to guide screening with long-term monitoring. The present study aimed to establish the role of left atrial (LA) function in subsequent AF identification and develop a risk model for AF in ESUS.

METHODS AND RESULTS

We conducted a single-centre retrospective case-control study including all patients with ESUS referred to our institution for ILR implantation from December 2009 to September 2019. We recorded clinical variables at baseline and analysed transthoracic echocardiograms in sinus rhythm. Univariate and multivariable analyses were performed to inform variables associated with AF. Lasso regression analysis was used to develop a risk prediction model for AF. The risk model was internally validated using bootstrapping. Three hundred and twenty-three patients with ESUS underwent ILR implantation. In the ESUS population, 293 had a stroke, whereas 30 had suffered a transient ischaemic attack as adjudicated by a senior stroke physician. Atrial fibrillation of any duration was detected in 47.1%. The mean follow-up was 710 days. Following lasso regression with backwards elimination, we combined increasing lateral PA (the time interval from the beginning of the P wave on the surface electrocardiogram to the beginning of the A' wave on pulsed wave tissue Doppler of the lateral mitral annulus) [odds ratio (OR) 1.011], increasing Age (OR 1.035), higher Diastolic blood pressure (OR 1.027), and abnormal LA reservoir Strain (OR 0.973) into a new PADS score. The probability of identifying AF can be estimated using the formula. Model discrimination was good [area under the curve (AUC) 0.72]. The PADS score was internally validated using bootstrapping with 1000 samples of 150 patients showing consistent results with an AUC of 0.73.

CONCLUSION

The novel PADS score can identify the risk of AF on prolonged monitoring with ILR following ESUS and should be considered a dedicated risk stratification tool for decision-making regarding the screening strategy for AF in stroke.

External Validation of SAFE Score to Predict Atrial Fibrillation Diagnosis after Ischemic Stroke: A Retrospective Multicenter Study

Introduction

The screening for atrial fibrillation (AF) scale (SAFE score) was recently developed to provide a prediction of the diagnosis of AF after an ischemic stroke. It includes 7 items: age ≥ 65 years, bronchopathy, thyroid disease, cortical location of stroke, intracranial large vessel occlusion, NT-ProBNP ≥250 pg/mL, and left atrial enlargement. In the internal validation, a good performance was obtained, with an AUC = 0.88 (95% CI 0.84-0.91) and sensitivity and specificity of 83% and 80%, respectively, for scores ≥ 5. The aim of this study is the external validation of the SAFE score in a multicenter cohort.

Methods

A retrospective multicenter study, including consecutive patients with ischemic stroke or transient ischemic attack between 2020 and 2022 with at least 24 hours of cardiac monitoring. Patients with previous AF or AF diagnosed on admission ECG were excluded.

Results

Overall, 395 patients were recruited for analysis. The SAFE score obtained an AUC = 0.822 (95% CI 0.778-0.866) with a sensitivity of 87.2%, a specificity of 65.4%, a positive predictive value of 44.1%, and a negative predictive value of 94.3% for a SAFE score ≥ 5, with no significant gender differences. Calibration analysis in the external cohort showed an absence of significant differences between the observed values and those predicted by the model (Hosmer-Lemeshow's test 0.089).

Conclusions

The SAFE score showed adequate discriminative ability and calibration, so its external validation is justified. Further validations in other external cohorts or specific subpopulations of stroke patients might be required.

The Role of Electrocardiographic Markers for Predicting Atrial Fibrillation in Patients with Acute Ischemic Stroke: Data from the BIOSIGNAL Cohort Study

BACKGROUND AND AIMS

P-wave abnormalities in the 12-lead electrocardiogram (ECG) have been associated with a higher risk of acute ischemic stroke (AIS) as well as atrial fibrillation (AF). This study aimed to assess pre-determined ECG criteria during sinus rhythm in unselected AIS patients and their value for predicting newly diagnosed atrial fibrillation (NDAF) after hospital admission.

METHODS

P-wave alterations were measured on 12-lead ECG on admission in all consecutively enrolled patients without known AF between October 2014 and 2017. The outcome of interest was NDAF, identified by prolonged electrocardiographic monitoring within one year after the index AIS. Univariable and multivariable logistic regression was applied to assess the magnitude and independence of the association between pre-selected ECG markers and NDAF. The discriminatory accuracy was evaluated with the area under the receiver operating characteristic curve (AUC), and the incremental prognostic value was estimated with the net reclassification index.

RESULTS

NDAF was detected in 87 (10%) of 856 patients during a follow-up of 365 days. Out of the pre-selected ECG parameters, advanced interatrial block (aIAB) and PR interval in lead II were independently associated with NDAF in univariable regression analysis. Only aIAB remained a significant predictor in multivariable analysis. Adding aIAB to the best-performing multivariable regression model improved the discriminatory accuracy to predict NDAF from an AUC of 0.78 (95%-CI 0.77-0.80) to 0.81 (95%-CI 0.80-0.83, p < 0.001).

CONCLUSION

aIAB is independently and highly associated with NDAF in patients with AIS, has high inter-rater reliability, and therefore may be helpful to refine diagnostic work-up to search for AF in AIS.

Diagnostic value of carotid intima-media thickness and clinical risk scores in determining etiology of ischemic stroke

BACKGROUND

In the general population, carotid intima-media thickness (CIMT) is associated with atherosclerosis as well as atrial fibrillation (AF). However, the extent to which CIMT might be of diagnostic value in clarifying stroke etiology is currently unclear.

METHODS

In this retrospective cohort study, we included 800 consecutive patients with acute ischemic stroke. We compared CIMT-values between stroke etiologies. The association between CIMT and cardioembolic stroke was investigated via logistic regression analysis adjusting for vascular risk factors. Receiver operating characteristic analyses were conducted to investigate the diagnostic value of CIMT in comparison to vascular risk factors and clinical AF risk scores (CHA2DS2VASc, HAVOC, and AS5F).

RESULTS

CIMT-values were highest in patients with cardioembolic or atherosclerotic stroke origin. CIMT was associated with newly diagnosed AF compared against cryptogenic strokes (crude odds ratio (OR) per 0.1 mm-increase of CIMT: 1.26 (95% confidence interval (CI): 1.13-1.41)). After adjustment for vascular risk factors, the effect of CIMT on AF-diagnosis, however, was weakened (adjusted OR: 1.10 (95% CI: 0.97-1.25)). The diagnostic value of CIMT for detection of AF (AUC: 0.60, 95% CI: 0.54-0.65) was outperformed by AF risk scores. Among the scores investigated, the AS5F-score yielded best accuracy and calibration to predict newly diagnosed AF (AUC: 0.71, 95% CI: 0.65-0.78).

CONCLUSIONS

CIMT may help in the diagnosis of stroke etiology. However, compared with vascular risk factors or clinical AF risk scores, CIMT does not provide substantial additional information on the risk of newly detected AF. Thus, stratification of AF risk based on scores, such as the AS5F, is advisable.

Searching for atrial fibrillation post stroke: is it time for digital devices?

The detection of atrial fibrillation (AF) in patients with cryptogenic stroke (CS) is an essential part of management to limit the risk of recurrence. However, in practice, not all patients who need AF screening are screened, or are screened with significant delays. The disparities of access to examinations, their costs as well as the increasing workload require an evolution of practices both in terms of organization and the type of equipment used. The ubiquity and ease of use of digital devices, together with their evaluation in large population and their expected lower cost, make them attractive as potential alternatives to current equipment at all stages of patient management. However, reliability and accuracy of each digital device for the detection of paroxysmal AF in CS patients should be established before consideration for inclusion in clinical practice. The aim of this short analysis is therefore to review the current practical issues for AF detection in post stroke patients, the potential benefits and issues using digital devices in stroke patients and to position the different digital devices as alternative to standard equipment at each stage of stroke patient pathway. This may help to design future studies for the evaluation of these devices in this context. Under this condition, the time for digital devices to detect AF after stroke seems very close.

Prediction of underlying atrial fibrillation in patients with a cryptogenic stroke: results from the NOR-FIB Study

BACKGROUND

Atrial fibrillation (AF) detection and treatment are key elements to reduce recurrence risk in cryptogenic stroke (CS) with underlying arrhythmia. The purpose of the present study was to assess the predictors of AF in CS and the utility of existing AF-predicting scores in The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study.

METHOD

The NOR-FIB study was an international prospective observational multicenter study designed to detect and quantify AF in CS and cryptogenic transient ischaemic attack (TIA) patients monitored by the insertable cardiac monitor (ICM), and to identify AF-predicting biomarkers. The utility of the following AF-predicting scores was tested: AS5F, Brown ESUS-AF, CHA₂DS₂-VASc, CHASE-LESS, HATCH, HAVOC, STAF and SURF.

RESULTS

In univariate analyses increasing age, hypertension, left ventricle hypertrophy, dyslipidaemia, antiarrhythmic drugs usage, valvular heart disease, and neuroimaging findings of stroke due to intracranial vessel occlusions and previous ischemic lesions were associated with a higher likelihood of detected AF. In multivariate analysis, age was the only independent predictor of AF. All the AF-predicting scores showed significantly higher score levels for AF than non-AF patients. The STAF and the SURF scores provided the highest sensitivity and negative predictive values, while the AS5F and SURF reached an area under the receiver operating curve (AUC) > 0.7.

CONCLUSION

Clinical risk scores may guide a personalized evaluation approach in CS patients. Increasing awareness of the usage of available AF-predicting scores may optimize the arrhythmia detection pathway in stroke units.

Initial In-Hospital Visit-to-Visit Heart Rate Variability Is Associated with Higher Risk of Atrial Fibrillation in Patients with Acute Ischemic Stroke

BACKGROUND

To evaluate the association between the visit-to-visit heart rate variability and the risk of atrial fibrillation (AF) in acute ischemic stroke (AIS).

METHODS

We analyzed the data of 8179 patients with AIS. Patients without AF on 12-lead electrocardiography underwent further 24 h Holter monitoring. They were categorized into four subgroups according to the visit-to-visit heart rate variability expressed as the coefficient of variation in heart rate (HR-CV). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using the HR-CV < 0.08 subgroup as a reference.

RESULTS

The adjusted OR of paroxysmal AF was 1.866 (95% CI = 1.205-2.889) for the HR-CV ≥ 0.08 and <0.10 subgroup, 1.889 (95% CI = 1.174-3.038) for the HR-CV ≥ 0.10 and <0.12 subgroup, and 5.564 (95% CI = 3.847-8.047) for the HR-CV ≥ 0.12 subgroup. The adjusted OR of persistent AF was 2.425 (95% CI = 1.921-3.062) for the HR-CV ≥ 0.08 and <0.10 subgroup, 4.312 (95% CI = 3.415-5.446) for the HR-CV ≥ 0.10 and <0.12 subgroup, and 5.651 (95% CI = 4.586-6.964) for the HR-CV ≥ 0.12 subgroup.

CONCLUSIONS

HR-CV can facilitate the identification of patients with AIS at a high risk of paroxysmal AF.

Post-Stroke Detection of Subclinical Paroxysmal Atrial Fibrillation in Patients With Embolic Stroke of Undetermined Source in the Real World Practice: The Empoli ESUS Atrial Fibrillation (E 2 AF) Study

BACKGROUND

Subclinical paroxysmal atrial fibrillation (AF) is one of the main occult causative mechanisms of embolic stroke of undetermined source (ESUS). Aim of this study was to identify AF predictors, and to develop a score to predict the probability of AF detection in ESUS.

METHODS

We retrospectively analyzed ESUS patients undergoing 2-week external electrocardiographic monitoring. Patients with and without AF detection were compared. On the basis of multivariate analysis, predictors of AF were identified and used to develop a predictive score, which was then compared with other existing literature scores.

RESULTS

Eighty-two patients, 48 females, mean age±SD 72±10 years, were included. In 36 patients (43.9%) AF was detected. The frequency of age 75 years or above and arterial hypertension, and the median CHA 2 DS 2 -VASc score were significantly higher in patients with AF compared with those without. National Institutes of Health Stroke Scale (NIHSS) score ≥8 was the only independent variable associated with AF detection. We derived the Empoli ESUS-AF (E 2 AF) score (NIHSS ≥8 5 points, arterial hypertension 3 points, age 75 years or above 2 points, age 65 to 74 years 1 point, history of coronary/peripheral artery disease 1 point, left atrial enlargement 1 point, posterior lesion 1 point, cortical or cortical-subcortical lesion 1 point), whose predictive power in detecting AF was good (area under the curve: 0.746, 95% confidence interval: 0.638-0.836) and higher than that of CHA 2 DS 2 -VASc and other scores.

CONCLUSIONS

In our study NIHSS score ≥8 was the only independent predictor of post-ESUS-AF detection. The E 2 AF score appears to have a good predictive power for detecting AF. External validations are required.

Atrial fibrillation and stroke

INTRODUCTION

Stroke is one of the leading causes of mortality and morbidity globally. Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. It is set to reach epidemic proportions. AF is associated with a five-fold increase in risk of stroke. Strokes caused by AF more often are fatal or result in severe disability. Even though the incidence of stroke has been significantly reduced by oral anticoagulation, AF is thought to account for a significant proportion of cryptogenic strokes where no etiology is identified.

AREAS COVERED

This article reviews the literature related to AF and stroke, pathophysiological insights, diagnosis of AF in stroke patients, and its management (Graphical Abstract).

EXPERT OPINION

The pathophysiology of thrombogenesis that links AF and stroke is not well understood and is an area of active research to identify new therapeutic targets to prevent AF and stroke. As the nature of AF and stroke is multifaceted, an integrated care approach to managing AF and stroke is increasingly essential.

14-day Holter monitoring for atrial fibrillation after ischemic stroke: The yield of guideline-recommended monitoring duration

Introduction:

Current European Stroke Organisation (ESO) guidelines recommend >48 h of continuous electrocardiographic monitoring for atrial fibrillation (AF) in all patients with ischemic stroke or transient ischemic attack (TIA) with undetermined origin. We assessed the yield of the guideline-recommended monitoring for AF, as well as of extending monitoring up to 14 days.

Patients and methods:

We included consecutive patients with stroke/TIA without AF in an academic hospital in The Netherlands. We reported AF incidence and number needed to screen (NNS) in the overall sample after 48 h and 14 days of Holter monitoring.

Results:

Among 379 patients with median age 63 years (IQR 55–73), 58% male, Holter monitoring detected 10 cases of incident AF during a median of 13 (IQR 12–14) days of monitoring. Seven AF cases were detected within the first 48 hours (incidence 1.85%, 95% CI 0.74–3.81; NNS 54), and three additional AF cases were recorded among the 362 patients with >48 h of monitoring and without AF ⩽ 48 h (incidence 0.83%, 95% CI: 0.17–2.42; NNS 121). All AF cases were detected within the first 7 days of monitoring. Our sample was subject to sampling bias favoring inclusion of participants with low AF risk.

Discussion:

Strengths of this work were the broad inclusion criteria as recommended by ESO guidelines, and high Holter adherence among participants. The analysis was limited by inclusion of lower-risk cases and a relatively small sample size.

Conclusion:

In low-risk patients with recent stroke or TIA, ESO guideline-recommended screening for AF resulted in a low AF yield, with limited additional value of monitoring up to 14 days. Our results underline the need for a personalized approach in determining a patient’s optimum duration for post-stroke non-invasive ambulatory monitoring.

Incremental value of the combined brain-cardiac CT protocol on prediction of atrial fibrillation after stroke

Introduction:

Atrial fibrillation (AF) is one of the most common causes of ischemic stroke. It is essential to target patients at highest risk of AF detected after stroke (AFDAS), who should benefit from a prolonged rhythm screening strategy. Cardiac-CT angiography (CCTA) was added to the stroke protocol used in our institution in 2018. We sought to assess, for AFDAS, the predictive value of atrial cardiopathy markers by a CCTA performed on admission for acute ischemic stroke.

Patients and Methods:

From November 2018 to October 2019, consecutive stroke patients with no history of AF were included. Let atrial volume (LAV), epicardial adipose tissue (EAT) attenuation and volume, and LAA characteristics were measured on CCTA. The primary endpoint was the presence of AFDAS at follow-up, diagnosed by continuous electrocardiographic monitoring, long-term external Holter monitoring during hospital stay, or implantable cardiac monitor (ICM).

Results:

Sixty of the 247 included patients developed AFDAS. Multivariable analysis shows independent predictors of AFDAS: age >80 years (HR 2.46; 95%CI (1.23–4.92), p = 0.011), indexed LAV >45 mL/m2 (HR 2.58; 95%CI (1.19–5.62), p = 0.017), EAT attenuation > −85HU (HR 2.16; 95%CI (1.13–4.15), p = 0.021) and LAA thrombus (HR 2.50; 95%CI (1.06–5.93), p = 0.037). Added consecutively to AFDAS prediction AS5F score (combining age and NIHSS >5), these markers had an incrementally better predictive value compared with the global Chi2 of the initial model ( p = 0.001, 0.035, and 0.015 respectively).

Discussion and conclusion:

Adding CCTA to the acute stroke protocol to assess markers of atrial cardiopathy associated with AFDAS may help to better stratify the AF screening strategy, including the use of an ICM.

Atrial Fibrillation Detected by Implantable Monitor in Embolic Stroke of Undetermined Source: A New Clinical Entity

Background: High incidence of covert paroxysmal atrial fibrillation (CPAF) detected by an implantable cardiac monitor (ICM) is expected in embolic stroke of undetermined source (ESUS) patients. This study aimed to determine the CPAF rate in an ESUS cohort using ICMs and compare stroke characteristics of patients with CPAF to those with known or inpatient-diagnosed AF (KIDAF). Methods: ESUS patients with ICMs were enrolled. ESUS diagnosis was defined as a non-lacunar stroke in the absence of symptomatic atherosclerotic stenosis (≥50%), no major-risk cardioembolic source, and no other specific cause. ESUS characteristics of patients with CPAF were compared to ESUS patients without CPAF and to KIDAF stroke patients. Results: During the median follow-up of 476 (371–615) days, CPAF was newly detected in 38/163 (23.31%) patients within 236 (115.50–510.75) days after the stroke. CPAF was independently associated to older age, coronaropathy, left atrial dilation, and atrial hyperexcitability, but not to stroke severity. Compared to KIDAF strokes, ESUS with CPAF had lower rates of proximal occlusion leading to milder clinical severity (NIHSS: 3.00 (1.00–8.25) vs. 14.50 (6.00–21.00)). Conclusions: Our study revealed a high proportion of CPAF in ESUS. We highlight that CPAF is a distinct clinical entity compared to KIDAF based on differences in stroke characteristics and AF diagnosis temporality.

Long-Term Cardiac Monitoring After Embolic Stroke of Undetermined Source: Search Longer, Look Harder

Embolic stroke of undetermined source (ESUS) represents a heterogeneous subgroup of patients with cryptogenic stroke, in which despite an extensive diagnostic workup the cause of stroke remains uncertain. Identifying covert atrial fibrillation among patients with ESUS remains challenging. The increasing use of cardiac implanted electronic devices (CIED), such as pacemakers, implantable defibrillators, and implantable loop recorders (ILR), has provided important information on the burden of subclinical atrial fibrillation. Accumulating evidence indicate that long-term continuous monitoring, especially in selected patients with ESUS, significantly increases the possibility of atrial fibrillation detection, suggesting it may be a cost-effective tool in secondary stroke prevention. This review summarizes available evidence related to the use of long-term cardiac monitoring and the use of implantable cardiac monitoring devices in patients with ESUS.

Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing

Background

Timely detection of atrial fibrillation (AF) after stroke is highly clinically relevant, aiding decisions on the optimal strategies for secondary prevention of stroke. In the context of limited medical resources, it is crucial to set the right priorities of extended heart rhythm monitoring by stratifying patients into different risk groups likely to have newly detected AF (NDAF). This study aimed to develop an electronic health record (EHR)-based machine learning model to assess the risk of NDAF in an early stage after stroke.

Methods

Linked data between a hospital stroke registry and a deidentified research-based database including EHRs and administrative claims data was used. Demographic features, physiological measurements, routine laboratory results, and clinical free text were extracted from EHRs. The extreme gradient boosting algorithm was used to build the prediction model. The prediction performance was evaluated by the C-index and was compared to that of the AS5F and CHASE-LESS scores.

Results

The study population consisted of a training set of 4,064 and a temporal test set of 1,492 patients. During a median follow-up of 10.2 months, the incidence rate of NDAF was 87.0 per 1,000 person-year in the test set. On the test set, the model based on both structured and unstructured data achieved a C-index of 0.840, which was significantly higher than those of the AS5F (0.779, p = 0.023) and CHASE-LESS (0.768, p = 0.005) scores.

Conclusions

It is feasible to build a machine learning model to assess the risk of NDAF based on EHR data available at the time of hospital admission. Inclusion of information derived from clinical free text can significantly improve the model performance and may outperform risk scores developed using traditional statistical methods. Further studies are needed to assess the clinical usefulness of the prediction model.

Development of a Score to Predict the Paroxysmal Atrial Fibrillation in Stroke Patients: The Screening for Atrial Fibrillation Scale

Background and Purpose

An individual selection of ischemic stroke patients at higher risk of atrial fibrillation (AF) might increase the diagnostic yield of prolonged cardiac monitoring and render it cost-effective.

Methods

The clinical, laboratory, and brain/cardiac imaging characteristics of consecutive ischemic stroke patients without documented AF were recorded. All patients underwent at least 72 h of cardiac monitoring unless AF was diagnosed before, transthoracic echocardiogram, blood biomarkers, and intracranial vessels imaging. A predictive grading was developed by logistic regression analysis, the screening for atrial fibrillation scale (SAFE).

Results

A total of 460 stroke patients were analyzed to develop the SAFE scale, a 7-items score (possible total score 0–10): age ≥ 65 years (2 points); history of chronic obstructive pulmonary disease or obstructive sleep apnea (1 point); thyroid disease (1 point); NT-proBNP ≥ 250 pg/ml (2 points); left atrial enlargement (2 points); cortical topography of stroke, including hemispheric or cerebellar cortex (1 point); and intracranial large vessel occlusion (1 point). A score = 5 identified patients with paroxysmal AF with a sensitivity of 83% and a specificity of 80%.

Conclusion

Screening for atrial fibrillation scale (SAFE) is a novel and simple strategy for selecting ischemic stroke patients at higher risk of having AF who can benefit from a more thorough etiological evaluation. External validation of SAFE in a multicenter study, with a larger number of patients, is warranted.

Development and Validation of a Novel Score for Predicting Paroxysmal Atrial Fibrillation in Acute Ischemic Stroke

Atrial fibrillation (AF)—whether paroxysmal or sustained—increases the risk of stroke. We developed and validated a risk score for identifying patients at risk of paroxysmal atrial fibrillation (pAF) after acute ischemic stroke (AIS). A total of 6033 patients with AIS who received 24 h Holter monitoring were identified in the Chang Gung Research Database. Among the identified patients, 5290 with pAF and without AF were included in the multivariable logistic regression analysis to develop the pAF prediction model. The ABCD-SD score (Age, Systolic Blood pressure, Coronary artery disease, Dyslipidemia, and Standard Deviation of heart rate) comprises age (+2 points for every 10 years), systolic blood pressure (−1 point for every 20 mmHg), coronary artery disease (+2 points), dyslipidemia (−2 points), and standard deviation of heart rate (+2 points for every 3 beats per minute). Overall, 5.2% (274/5290) of patients had pAF. The pAF risk ranged from 0.8% (ABCD-SD score ≤ 7) to 18.3% (ABCD-SD score ≥ 15). The model achieved an area under the receiver operating characteristic curve (AUROCC) of 0.767 in the model development group. The ABCD-SD score could aid clinicians in identifying patients with AIS at risk of pAF for advanced cardiac monitoring.

Association of stroke lesion shape with newly detected atrial fibrillation – Results from the MonDAFIS study

Paroxysmal Atrial fibrillation (AF) is often clinically silent and may be missed by the usual diagnostic workup after ischemic stroke. We aimed to determine whether shape characteristics of ischemic stroke lesions can be used to predict AF in stroke patients without known AF at baseline. Lesion shape quantification on brain MRI was performed in selected patients from the intervention arm of the Impact of standardized MONitoring for Detection of Atrial Fibrillation in Ischemic Stroke (MonDAFIS) study, which included patients with ischemic stroke or TIA without prior AF. Multiple morphologic parameters were calculated based on lesion segmentation in acute brain MRI data. Multivariate logistic models were used to test the association of lesion morphology, clinical parameters, and AF. A stepwise elimination regression was conducted to identify the most important variables. A total of 755 patients were included. Patients with AF detected within 2 years after stroke (n = 86) had a larger overall oriented bounding box (OBB) volume (p = 0.003) and a higher number of brain lesion components (p = 0.008) than patients without AF. In the multivariate model, OBB volume (OR 1.72, 95%CI 1.29–2.35, p < 0.001), age (OR 2.13, 95%CI 1.52–3.06, p < 0.001), and female sex (OR 2.45, 95%CI 1.41–4.31, p = 0.002) were independently associated with detected AF. Ischemic lesions in patients with detected AF after stroke presented with a more dispersed infarct pattern and a higher number of lesion components. Together with clinical characteristics, these lesion shape characteristics may help in guiding prolonged cardiac monitoring after stroke.

Review and update of the concept of embolic stroke of undetermined source

Ischaemic strokes have traditionally been classified according to the TOAST criteria, in which strokes with unclear aetiology are classified as cryptogenic strokes. However, the definition of cryptogenic stroke did not meet the operational criteria necessary to define patient populations for randomized treatment trials. To address this problem, the concept of embolic stroke of undetermined source (ESUS) was developed and published in 2014. A hypothesis that underpinned this concept was that most strokes in patients with ESUS are caused by embolic events, perhaps many cardioembolic, and that anticoagulation would prevent secondary ischaemic events. On this basis, two large randomized trials were conducted to compare the non-vitamin K antagonist oral anticoagulants (NOACs) dabigatran and rivaroxaban with aspirin. Neither NOAC was superior to aspirin in these trials, although subgroups of patients with ESUS seemed to benefit specifically from anticoagulation or antiplatelet therapy. The neutral results of the trials of anticoagulation and insights into ESUS from research conducted since the concept was introduced warrant reassessment of the ESUS construct as a research concept and a treatment target. In this Review, we discuss the evidence produced since the concept of ESUS was introduced, and propose updates to the criteria and diagnostic algorithm in light of the latest knowledge.

Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study

Background

Poststroke atrial fibrillation (AF) screening aids decisions regarding the optimal secondary prevention strategies in patients with acute ischemic stroke (AIS). We used an electronic medical record (EMR) algorithm to identify AF in a cohort of AIS patients, which were used to validate eight risk scores for predicting AF detected after stroke (AFDAS).

Methods

We used linked data between a hospital stroke registry and a deidentified database including EMRs and administrative claims data. EMR algorithms were constructed to identify AF using diagnostic and medication codes as well as free clinical text. Based on the optimal EMR algorithm, the incidence rate of AFDAS was estimated. The predictive performance of 8 risk scores including AS5F, C₂HEST, CHADS₂, CHA₂DS₂-VASc, CHASE-LESS, HATCH, HAVOC, and Re-CHARGE-AF scores, were compared using the C-index, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis.

Results

The algorithm that defines AF as any positive mention of AF-related keywords in electrocardiography or echocardiography reports, or presence of diagnostic codes of AF was used to identify AF. Among the 5,412 AIS patients without known AF at stroke admission, the incidence rate of AFDAS was 84.5 per 1,000 person-year. The CHASE-LESS and AS5F scores were well calibrated and showed comparable C-indices (0.741 versus 0.730, p = 0.223), which were significantly higher than the other risk scores.

Conclusion

The CHASE-LESS and AS5F scores demonstrated adequate discrimination and calibration for predicting AFDAS. Both simple risk scores may help select patients for intensive AF monitoring.

Measurement of Midregional Pro-Atrial Natriuretic Peptide to Discover Atrial Fibrillation in Patients With Ischemic Stroke

BACKGROUND

Midregional pro-atrial natriuretic peptide (MR-proANP) is a promising biomarker to differentiate the underlying etiology of acute ischemic stroke (AIS).

OBJECTIVES

This study aimed to determine the role of MR-proANP for classification as cardioembolic (CE) stroke, identification of newly diagnosed atrial fibrillation (NDAF), and risk assessment for major adverse cardiovascular events (MACE).

METHODS

This study measured MR-proANP prospectively collected within 24 hours after symptom-onset in patients with AIS from the multicenter BIOSIGNAL (Biomarker Signature of Stroke Aetiology) cohort study. Primary outcomes were CE stroke etiology and NDAF after prolonged cardiac monitoring, as well as a composite outcome of MACE (recurrent cerebrovascular events, myocardial infarction, or cardiovascular death) within 1 year. Logistic/Poisson and subproportional hazard regression were applied to evaluate the association between MR-proANP levels and outcomes. Additionally, a model for prediction of NDAF was derived and validated as a decision tool for immediate clinical application.

RESULTS

Between October 1, 2014, and October 31, 2017, this study recruited 1,759 patients. Log₁₀MR-proANP levels were associated with CE stroke (OR: 7.96; 95% CI: 4.82-13.14; risk ratio: 3.12; 95% CI: 2.23-4.37), as well as NDAF (OR: 35.3; 95% CI: 17.58-71.03; risk ratio: 11.47; 95% CI: 6.74-19.53), and MACE (subdistributional HR: 2.02; 95% CI: 1.32-3.08) during follow-up. The model to predict NDAF including only age and MR-proANP levels had a good discriminatory capacity with an area under the curve of 0.81 (95% CI: 0.76-0.86), was well calibrated (calibration in the large: -0.086; calibration slope 1.053), and yielded higher net-benefit compared with validated scores to predict NDAF (AS5F score, CHA₂DS₂-VASc [Congestive Heart Failure, Hypertension, Age ≥65 or ≥75, Diabetes, Prior Cardioembolic Event, (female) Sex, or Vascular Disease] score).

CONCLUSIONS

MR-proANP is a valid biomarker to determine risk of NDAF and MACE in patients with AIS and can be used as a decision tool to identify patients for prolonged cardiac monitoring. (Biomarker Signature of Stroke Aetiology Study: The BIOSIGNAL study [BIOSIGNAL]; NCT02274727).

Using machine learning to predict atrial fibrillation diagnosed after ischemic stroke

BACKGROUND

Selecting best candidates for prolonged poststroke cardiac monitoring in acute ischemic stroke (AIS) patients is still challenging. We aimed to develop a machine learning (ML) model to select AIS patients at high risk of poststroke atrial fibrillation (AF) for prolonged cardiac monitoring and then to compare ML model with traditional risk scores and classic statistical logistic regression (classic-LR) model.

METHODS

AIS patients from July 2012 to September 2020 across Nanjing First Hospital were collected. We performed the LASSO regression for selecting the critical features and built five ML models to assess the risk of poststroke AF. The SHAP and partial dependence plot (PDP) method were introduced to interpret the optimal model. We also compared ML model with CHADS₂ score, CHA₂DS₂-VASc score, AS5F score, HAVOC score, and classic-LR model.

RESULTS

A total of 3929 AIS patients were included. Among the five ML models, deep neural network (DNN) was the model with best performance. It also exhibited superior performance compared with CHADS₂ score, CHA₂DS₂-VASc score, AS5F score, HAVOC score and classic-LR model. The results of SHAP and PDP method revealed age, cardioembolic stroke, large-artery atherosclerosis stroke, and NIHSS score at admission were the top four important features and revealed the DNN model had good interpretability and reliability.

CONCLUSION

The DNN model achieved best performance and improved prediction performance compared with traditional risk scores and classic-LR model. The DNN model can be applied to identify AIS patients at high risk of poststroke AF as best candidates for prolonged poststroke cardiac monitoring.

Biomarkers for Atrial Fibrillation Detection After Stroke: Systematic Review and Meta-analysis

BACKGROUND AND OBJECTIVE

To identify clinical, ECG, and blood-based biomarkers associated with atrial fibrillation (AF) detection after ischaemic stroke or TIA that could help inform patient selection for cardiac monitoring.

METHODS

We performed a systematic review and meta-analysis and searched electronic databases for cohort studies from January 15, 2000, to January 15, 2020. The outcome was AF ≥30 seconds within 1 year after ischemic stroke/TIA. We used random effects models to create summary estimates of risk. Risk of bias was assessed using the Quality in Prognostic Studies tool.

RESULTS

We identified 8,503 studies, selected 34 studies, and assessed 69 variables (42 clinical, 20 ECG, and 7 blood-based biomarkers). The studies included 11,569 participants and AF was detected in 1,478 (12.8%). Overall, risk of bias was moderate. Variables associated with increased likelihood of AF detection are older age (odds ratio [OR] 3.26, 95% confidence interval [CI] 2.35-4.54), female sex (OR 1.47, 95% CI 1.23-1.77), a history of heart failure (OR 2.56, 95% CI 1.87-3.49), hypertension (OR 1.42, 95% CI 1.15-1.75) or ischemic heart disease (OR 1.80, 95% CI 1.34-2.42), higher modified Rankin Scale (OR 6.13, 95% CI 2.93-12.84) or National Institutes of Health Stroke Scale score (OR 2.50, 95% CI 1.64-3.81), no significant carotid/intracranial artery stenosis (OR 3.23, 95% CI 1.14-9.11), no tobacco use (OR 1.93, 95% CI 1.48-2.51), statin therapy (OR 2.07, 95% CI 1.14-3.73), stroke as index diagnosis (OR 1.59, 95% CI 1.17-2.18), systolic blood pressure (OR 1.61, 95% CI 1.16-2.22), IV thrombolysis treatment (OR 2.40, 95% CI 1.83-3.16), atrioventricular block (OR 2.12, 95% CI 1.08-4.17), left ventricular hypertrophy (OR 2.21, 95% CI 1.03-4.74), premature atrial contraction (OR 3.90, 95% CI 1.74-8.74), maximum P-wave duration (OR 3.19, 95% CI 1.40-7.25), PR interval (OR 2.32, 95% CI 1.11-4.83), P-wave dispersion (OR 7.79, 95% CI 4.16-14.61), P-wave index (OR 3.44, 95% CI 1.87-6.32), QTc interval (OR 3.68, 95% CI 1.63-8.28), brain natriuretic peptide (OR 13.73, 95% CI 3.31-57.07), and high-density lipoprotein cholesterol (OR 1.49, 95% CI 1.17-1.88) concentrations. Variables associated with reduced likelihood are minimum P-wave duration (OR 0.53, 95% CI 0.29-0.98), low-density lipoprotein cholesterol (OR 0.73, 95% CI 0.57-0.93), and triglyceride (OR 0.51, 95% CI 0.41-0.64) concentrations.

DISCUSSION

We identified multimodal biomarkers that could help guide patient selection for cardiac monitoring after ischaemic stroke/TIA. Their prognostic utility should be prospectively assessed with AF detection and recurrent stroke as outcomes.

Embolic Stroke of Undetermined Source: Approaches in Risk Stratification for Cardioembolism

Ischemic stroke is a leading cause of morbidity and mortality worldwide. Embolic stroke of undetermined source has been recently proposed to categorize nonlacunar ischemic strokes without confirmed etiology after adequate investigation with a likely embolic stroke mechanism. A strategy of empirical anticoagulation for embolic stroke of undetermined source patients is attractive but may only be beneficial in a select subset of patients. Strategies which would help identify the subset of embolic stroke of undetermined source patients most likely to have cardioembolic origin of stroke, and hence benefit from anticoagulation, are needed. This article will review current evidence which may be useful in the development of a risk stratification approach based on arrhythmia monitoring, cardiac imaging, and clinical risk stratification. This approach may be beneficial in clinical practice in improving patient outcomes and reducing stroke recurrence in this population; however, further work is required with active trials underway.

Use of risk scores for predicting new atrial fibrillation after ischemic stroke or transient ischemic attack-A systematic review

BACKGROUND

Newly detected atrial fibrillation (NDAF) following an ischemic stroke or transient ischemic attack is often paroxysmal in nature. While challenging to detect, extended electrocardiographic (ECG) monitoring is often used to identify NDAF which has resource implications. Prognostic risk scores have been derived which may stratify the risk of NDAF and inform patient selection for ECG monitoring approaches after ischemic stroke/transient ischemic attack.

AIM

The overall aim was to identify risk scores that were derived and/or validated to predict NDAF after ischemic stroke/transient ischemic attack and evaluate their performance.

SUMMARY OF REVIEW

A systematic literature review was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, with application of the Quality Assessment of Diagnostic Accuracy-2 tool. Published studies, which derived and validated clinical risk scores in patients with ischemic stroke/transient ischemic attack, or externally validated an existing score to predict NDAF after ischemic stroke/transient ischemic attack, were considered and independently screened by two reviewers. Twenty-one studies involving 23 separate cohorts were analyzed from which 17 integer-based risk scores were identified. The overall frequency of NDAF was 9.7% (95% confidence intervals 8%-11.5%; I²= 98%). The performance of the scores varied widely among derivation and validation cohorts (area under the receiver operating characteristic curve (AUC) 0.54-0.94); scores derived from stroke cohorts (12 scores) appeared to perform better (AUC 0.7-0.94) than those derived from non-stroke cohorts (five scores; AUC 0.53-0.79). The scores also varied considerably in their complexity, ascertainment, component variables, participant characteristics, outcome definition, and ease of application limiting their generalizability and utility.

CONCLUSION

Overall, the risk scores identified performed variably in their discriminative ability and the utility of these scores to predict NDAF in clinical practice remains uncertain. Further studies are required using larger prospective cohorts and randomized control trials to evaluate the usefulness of such scores for clinical decision making and preventative intervention.

Predicting atrial fibrillation after cryptogenic stroke via a clinical risk score-a prospective observational study

Background and purpose

Atrial fibrillation (AF) often remains undiagnosed in cryptogenic stroke (CS), mostly because of limited availability of cardiac long‐term rhythm monitoring. There is an unmet need for a pre‐selection of CS patients benefitting from such work‐up. A clinical risk score was therefore developed for the prediction of AF after CS and its performance was evaluated over 1 year of follow‐up.

Methods

Our proposed risk score ranges from 0 to 16 points and comprises variables known to be associated with occult AF in CS patients including age, N‐terminal pro‐brain natriuretic peptide, electrocardiographic and echocardiographic features (supraventricular premature beats, atrial runs, atrial enlargement, left ventricular ejection fraction) and brain imaging markers (multi‐territory/prior cortical infarction). All CS patients admitted to our Stroke Unit between March 2018 and August 2019 were prospectively followed for AF detection over 1 year after discharge.

Results

During the 1‐year follow‐up, 24 (16%) out of 150 CS patients with AF (detected via electrocardiogram controls, n = 18; loop recorder monitoring, n = 6) were diagnosed. Our predefined AF Risk Score (cutoff ≥4 points; highest Youden's index) had a sensitivity of 92% and a specificity of 67% for 1‐year prediction of AF. Notably, only two CS patients with <4 score points were diagnosed with AF later on (negative predictive value 98%).

Conclusions

A clinical risk score for 1‐year prediction of AF in CS with high sensitivity, reasonable specificity and excellent negative predictive value is presented. Generalizability of our score needs to be tested in external cohorts with continuous cardiac rhythm monitoring.

[Cardiac diagnostics after ischemic stroke or transitory ischemic attack]

Stroke is the most common cause of permanent disability and one of the most common causes of death. Cardio-embolic strokes are associated with a poor prognosis and a high risk of recurrence compared to other stroke etiologies. The most common source of cardiac embolism is atrial fibrillation which must be quickly identified to optimize secondary stroke prevention. A structured evaluation after ischemic stroke includes taking the medical history, a physical examination, 12-lead ECG recording, rhythm monitoring for 72 h, transthoracic echocardiography and transesophageal echocardiography, if an atrial embolic source of stroke is suspected. Extended cardiac work-up (e. g., MRI/CT, prolonged rhythm monitoring) should be performed in selected patients based on diagnostic findings.

Combined Omic Analyzes of Cerebral Thrombi: A New Molecular Approach to Identify Cardioembolic Stroke Origin

[Figure: see text].

[Diagnosis and treatment of acute ischemic insults]

In cases of stroke a distinction is made between a transient ischemic attack (TIA), a manifest ischemic infarction and cerebral hemorrhage. Cerebral ischemia can be caused by large vessel disease, small vessel disease, embolic causes, rare causes or stroke of unknown etiology. Acute diagnostic tests include a neurological examination, computed tomography (CT) and/or magnetic resonance imaging (MRI) with angiography, electrocardiography (ECG), and laboratory tests. The basic treatment of patients with TIA or acute ischemic infarction is performed in the stroke unit and includes monitoring of respiratory function, cardiac function, treatment of potential heart failure, detection of swallowing disorders, prophylaxis of thromboembolism, control of blood pressure and elevated blood sugar levels, and lowering of elevated body temperature. In patients with cardioembolic infarction, oral anticoagulation is initiated depending on the severity of the stroke and the size of the stroke on imaging.

Software-based analysis of 1-hour Holter ECG to select for prolonged ECG monitoring after stroke

Objective

Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice.

Methods

In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into “no risk of AF” or “risk of AF” and compared to clinical variables to predict AF during 72 hours Holter‐ECG.

Results

pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as “risk of AF” (n = 21, 17.8%) compared to “no risk of AF” (n = 33, 3.6%). AA‐based risk stratification as “risk of AF” remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024‐7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021‐1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023‐1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313‐5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724‐0.778; AUC for the combination: 0.789, 95% CI 0.763‐0.814; difference between the AUC P = 0.022).

Interpretation

Automated software‐based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.

Recurrent Stroke With Rivaroxaban Compared With Aspirin According to Predictors of Atrial Fibrillation: Secondary Analysis of the NAVIGATE ESUS Randomized Clinical Trial

Importance

The NAVIGATE ESUS randomized clinical trial found that 15 mg of rivaroxaban per day does not reduce stroke compared with aspirin in patients with embolic stroke of undetermined source (ESUS); however, it substantially reduces stroke risk in patients with atrial fibrillation (AF).

Objective

To analyze whether rivaroxaban is associated with a reduction of recurrent stroke among patients with ESUS who have an increased risk of AF.

Design, Setting, and Participants

Participants were stratified by predictors of AF, including left atrial diameter, frequency of premature atrial contractions, and HAVOC score, a validated scheme using clinical features. Treatment interactions with these predictors were assessed. Participants were enrolled between December 2014 and September 2017, and analysis began March 2018.

Intervention

Rivaroxaban treatment vs aspirin.

Main Outcomes and Measures

Risk of ischemic stroke.

Results

Among 7112 patients with a mean (SD) age of 67 (9.8) years, the mean (SD) HAVOC score was 2.6 (1.8), the mean (SD) left atrial diameter was 3.8 (1.4) cm (n = 4022), and the median (interquartile range) daily frequency of premature atrial contractions was 48 (13-222). Detection of AF during follow-up increased for each tertile of HAVOC score: 2.3% (score, 0-2), 3.0% (score, 3), and 5.8% (score, >3); however, neither tertiles of the HAVOC score nor premature atrial contractions frequency impacted the association of rivaroxaban with recurrent ischemic stroke (P for interaction = .67 and .96, respectively). Atrial fibrillation annual incidence increased for each tertile of left atrial diameter (2.0%, 3.6%, and 5.2%) and for each tertile of premature atrial contractions frequency (1.3%, 2.9%, and 7.0%). Among the predefined subgroup of patients with a left atrial diameter of more than 4.6 cm (9% of overall population), the risk of ischemic stroke was lower among the rivaroxaban group (1.7% per year) compared with the aspirin group (6.5% per year) (hazard ratio, 0.26; 95% CI, 0.07-0.94; P for interaction = .02).

Conclusions and Relevance

The HAVOC score, left atrial diameter, and premature atrial contraction frequency predicted subsequent clinical AF. Rivaroxaban was associated with a reduced risk of recurrent stroke among patients with ESUS and moderate or severe left atrial enlargement; however, this needs to be independently confirmed before influencing clinical practice.

Identification of patients with embolic stroke of undetermined source and low risk of new incident atrial fibrillation: The AF-ESUS score

Background and aims

Only a minority of patients with Embolic Stroke of Undetermined Source (ESUS) receive prolonged cardiac monitoring despite current recommendations. The identification of ESUS patients who have low probability of new diagnosis of atrial fibrillation (AF) could potentially support a strategy of more individualized allocation of available resources and hence, increase their diagnostic yield. We aimed to develop a tool that can identify ESUS patients who have low probability of new incident AF.

Methods

We performed multivariate stepwise regression in a pooled dataset of consecutive ESUS patients from three prospective stroke registries to identify predictors of new incident AF. The coefficient of each independent covariate of the fitted multivariable model was used to generate an integer-based point scoring system.

Results

Among 839 patients (43.1% women, median age 67.0 years) followed-up for a median of 24.3 months (2999 patient-years), 125 (14.9%) had new incident AF. The proposed score assigns 3 points for age ≥ 60 years; 2 points for hypertension; −1 point for left ventricular hypertrophy reported at echocardiography; 2 points for left atrial diameter >40 mm; −3 points for left ventricular ejection fraction <35%; 1 point for the presence of any supraventricular extrasystole recorded during all available 12-lead standard electrocardiograms performed during hospitalization for the ESUS; −2 points for subcortical infarct; −3 points for the presence of non-stenotic carotid plaques. The rate of new incident AF during follow-up was 1.97% among the 42.3% of the cohort who had a score of ≤0, compared to 26.9% in patients with > 0 (relative risk: 13.7, 95%CI: 5.9--31.5). The area under the curve of the score was 84.8% (95%CI: 79.9--86.9%). The sensitivity and negative predictive value of a score of ≤0 for new incident AF during follow-up were 94.9% (95%CI: 89.3--98.1%) and 98.0% (95%CI: 95.8--99.3%), respectively.

Conclusions

The proposed AF-ESUS score has high sensitivity and high negative predictive value to identify ESUS patients who have low probability of new incident AF. Patients with a score of 1 or more may be better candidates for prolonged automated cardiac monitoring.

Clinical trial registration

URL: https://www.clinicaltrials.gov / Unique identifier: NCT02766205.

Usefulness of the ACTEL Score to Predict Atrial Fibrillation in Patients with Cryptogenic Stroke

INTRODUCTION

To assess the probability of undetected atrial fibrillation (AF) in patients with ischemic stroke, we previously compared patients who were first diagnosed with AF with patients with large or small artery disease and obtained the MrWALLETS 8-item scoring system. In the present study, we utilized cryptogenic strokes (CS) as the control group, as AF is normally sought among CS patients.

METHODS

We retrospectively examined 191 ischemic stroke patients (72.5 ± 12.6 years), 68 with first diagnosed AF and 123 with CS, who had undergone 2 brain CT scans, echocardiography, carotid/vertebral ultrasound, continuous electrocardiogram monitoring and anamnestic/laboratory search for cardiovascular risk factors.

RESULTS

In logistic regression, 5 variables were independently associated with AF, forming the "ACTEL" score: Age ≥75 years (OR 2.42, 95% CI 1.18-4.96, p = 0.02; +1 point); hyperCholesterolemia (OR 0.38, 95% CI 0.18-0.78, p = 0.009; -1 point); Tricuspid regurgitation ≥ mild-to-moderate (OR 4.99, 95% CI 1.63-15.27, p = 0.005; +1 point); left ventricular End-diastolic volume <65 mL (OR 7.43, 95% CI 2.44-22.6, p = 0.0004; +1 point); Left atrium ≥4 cm (OR 4.57, 95% CI 1.97-10.62, p = 0.0004; +1 point). The algebraic sum of these points may range from -1 to +4. For AF identification, the area under the receiver operating characteristic curve was 0.80 (95% CI 0.73-0.87). With a cutoff of ≥2, positive predictive value was 80.8%, specificity 92.7% and sensitivity 55.9%.

CONCLUSIONS

The ACTEL score, a simplified and improved version of the MrWALLETS score, allows the identification of patients with first diagnosed AF, in the context of CSs, with a high positive predictive value.

Development of a novel score to predict newly diagnosed atrial fibrillation after ischemic stroke: The CHASE-LESS score

BACKGROUND AND AIMS

Prompt detection of atrial fibrillation (AF) is essential for optimal secondary stroke prevention, but routine long-term cardiac monitoring of all ischemic stroke patients is neither practical nor affordable. We aimed to develop and validate a risk score to identify patients at risk for newly diagnosed AF (NDAF) after ischemic stroke.

METHODS

Information on adult patients hospitalized for ischemic stroke without known AF was retrieved from a nationwide database. Primary outcome was NDAF within one year following index stroke. A stepwise Cox model was used to screen for predictors. Beta coefficients for the independent predictors were converted to integer points, which were summed to create a risk score.

RESULTS

We identified 4 positive predictors and 3 negative predictors. The CHASE-LESS score (Coronary, Heart failure, Age, stroke SEverity, - LipidEmia, Sugar, prior Stroke) comprises coronary artery disease (1 point), congestive heart failure (1 point), age (1 point for every 10 years), stroke severity (National Institutes of Health Stroke Scale; 1 point for 6-13 and 4 points for ≥14), hyperlipidemia (-1 point), diabetes (-1 point), and prior history of stroke or transient ischemic attack (-1 point). Overall, 6.0% (1029/17,076) of patients developed NDAF. The incidence rate ranged from 8/1000 person-years (CHASE-LESS ≤3) to 240/1000 person-years (CHASE-LESS ≥10). The model achieved a c-index of 0.730 in the development cohort and 0.732 in the validation cohort.

CONCLUSIONS

The CHASE-LESS score could aid clinicians to identify patients at risk of developing NDAF and help prioritize patients for advanced cardiac monitoring.

Searching for Atrial Fibrillation Poststroke: A White Paper of the AF-SCREEN International Collaboration

Cardiac thromboembolism attributed to atrial fibrillation (AF) is responsible for up to one-third of ischemic strokes. Stroke may be the first manifestation of previously undetected AF. Given the efficacy of oral anticoagulants in preventing AF-related ischemic strokes, strategies of searching for AF after a stroke using ECG monitoring followed by oral anticoagulation (OAC) treatment have been proposed to prevent recurrent cardioembolic strokes. This white paper by experts from the AF-SCREEN International Collaboration summarizes existing evidence and knowledge gaps on searching for AF after a stroke by using ECG monitoring. New AF can be detected by routine plus intensive ECG monitoring in approximately one-quarter of patients with ischemic stroke. It may be causal, a bystander, or neurogenically induced by the stroke. AF after a stroke is a risk factor for thromboembolism and a strong marker for atrial myopathy. After acute ischemic stroke, patients should undergo 72 hours of electrocardiographic monitoring to detect AF. The diagnosis requires an ECG of sufficient quality for confirmation by a health professional with ECG rhythm expertise. AF detection rate is a function of monitoring duration and quality of analysis, AF episode definition, interval from stroke to monitoring commencement, and patient characteristics including old age, certain ECG alterations, and stroke type. Markers of atrial myopathy (eg, imaging, atrial ectopy, natriuretic peptides) may increase AF yield from monitoring and could be used to guide patient selection for more intensive/prolonged poststroke ECG monitoring. Atrial myopathy without detected AF is not currently sufficient to initiate OAC. The concept of embolic stroke of unknown source is not proven to identify patients who have had a stroke benefitting from empiric OAC treatment. However, some embolic stroke of unknown source subgroups (eg, advanced age, atrial enlargement) might benefit more from non-vitamin K-dependent OAC therapy than aspirin. Fulfilling embolic stroke of unknown source criteria is an indication neither for empiric non-vitamin K-dependent OAC treatment nor for withholding prolonged ECG monitoring for AF. Clinically diagnosed AF after a stroke or a transient ischemic attack is associated with significantly increased risk of recurrent stroke or systemic embolism, in particular, with additional stroke risk factors, and requires OAC rather than antiplatelet therapy. The minimum subclinical AF duration required on ECG monitoring poststroke/transient ischemic attack to recommend OAC therapy is debated.