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

Natriuretic Peptides to Classify Risk of Atrial Fibrillation Detection After Stroke: Analysis of the BIOSIGNAL and PRECISE Cohort Studies

BACKGROUND AND OBJECTIVES

Prolonged cardiac monitoring (PCM) increases atrial fibrillation (AF) detection after ischemic stroke, but access is limited, and it is burdensome for patients. Our objective was to assess whether midregional proatrial natriuretic peptide (MR-proANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) could classify people who are unlikely to have AF after ischemic stroke and allow better targeting of PCM.

METHODS

We analyzed people from the Biomarker Signature of Stroke Aetiology (BIOSIGNAL) study with ischemic stroke, no known AF, and ≥3 days cardiac monitoring. External validation was performed in the Preventing Recurrent Cardioembolic Stroke: Right Approach, Right Patient (PRECISE) study of 28 days of cardiac monitoring in people with ischemic stroke or transient ischemic attack and no known AF. The main outcome is no AF detection. We assessed the discriminatory value of MR-proANP and NT-proBNP combined with clinical variables to identify people with no AF. A decision curve analysis was performed with combined data to determine the net reduction in people who would undergo PCM using the models based on a 15% threshold probability for AF detection.

RESULTS

We included 621 people from the BIOSIGNAL study. The clinical multivariable prediction model included age, NIH Stroke Scale score, lipid-lowering therapy, creatinine, and smoking status. The area under the receiver-operating characteristic curve (AUROC) for clinical variables was 0.68 (95% CI 0.62-0.74), which improved with the addition of log10MR-proANP (0.72, 0.66-0.78; p = 0.001) or log10NT-proBNP (0.71, 0.65-0.77; p = 0.009). Performance was similar for the models with log10MR-proANP vs log10NT-proBNP (p = 0.28). In 239 people from the PRECISE study, the AUROC for clinical variables was 0.68 (0.59-0.76), which improved with the addition of log10NT-proBNP (0.73, 0.65-0.82; p < 0.001) or log10MR-proANP (0.79, 0.72-0.86; p < 0.001). Performance was better for the model with log10MR-proANP vs log10NT-proBNP (p = 0.03). The models could reduce the number of people who would undergo PCM by 30% (clinical and log10MR-proANP), 27% (clinical and log10NT-proBNP), or 20% (clinical only).

DISCUSSION

MR-proANP and NT-proBNP help classify people who are unlikely to have AF after ischemic stroke. Measuring MR-proANP or NT-proBNP could reduce the number of people who need PCM by 30%, without reducing the amount of AF detected.

TRIAL REGISTRATION INFORMATION

NCT02274727; clinicaltrials.gov/study/NCT02274727.

Post-Ischemic Stroke Cardiovascular Risk Prevention and Management

Cardiac death is the second most common cause of death among patients with acute ischemic stroke (IS), following neurological death resulting directly from acute IS. Risk prediction models and screening tools including electrocardiograms can assess the risk of adverse cardiovascular events after IS. Prolonged heart rate monitoring and early anticoagulation therapy benefit patients with a higher risk of adverse events, especially stroke patients with atrial fibrillation. IS and cardiovascular diseases have similar risk factors which, if optimally managed, may reduce the incidence of recurrent stroke and other major cardiovascular adverse events. Comprehensive risk management emphasizes a healthy lifestyle and medication therapy, especially lipid-lowering, glucose-lowering, and blood pressure-lowering drugs. Although antiplatelet and anticoagulation therapy are preferred to prevent cardiovascular events after IS, a balance between preventing recurrent stroke and secondary bleeding should be maintained. Optimization of early rehabilitation care comprises continuous care across environments thus improving the prognosis of stroke survivors.

Diagnostic Utility of N-Terminal Pro-B-Type Natriuretic Peptide in Identifying Atrial Fibrillation Post-Cryptogenic Stroke: A Systematic Review and Meta-Analysis

BACKGROUND

Atrial fibrillation (AF) significantly contributes to acute ischemic stroke, with undetected AF being a common culprit in cryptogenic strokes. N-terminal pro-B-type natriuretic peptide (NT-proBNP), indicative of myocardial stress, has been proposed as a biomarker for AF detection, aiding in the selection of patients for extended cardiac monitoring. However, the diagnostic accuracy of NT-proBNP remains uncertain.

METHODS

We conducted a meta-analysis to evaluate the diagnostic accuracy of NT-proBNP in detecting AF among cryptogenic stroke patients. A comprehensive literature search was conducted across PubMed, Embase, and Cochrane databases to identify relevant studies. Studies reporting NT-proBNP levels in stroke patients and data on the proportion of patients with AF above a specified cut-off were included. Meta-analyses were performed using the midas command in STATA.

RESULTS

Seven studies encompassing 2171 patients were included in the analysis, of which five studies contained cohorts with cryptogenic strokes. Among patients with cryptogenic stroke, NT-proBNP demonstrated a diagnostic accuracy of 80% (Area Under the Receiver Operating Curve 0.80 [95% CI 0.76-0.83]), with a sensitivity of 81% (95% CI 0.68-0.89) and a specificity of 68% (95% CI 0.60-0.75).

CONCLUSION

Our meta-analysis indicates that NT-proBNP exhibits a good-to-very-good diagnostic accuracy for detecting AF in patients with cryptogenic stroke. These findings suggest potential implications for utilizing NT-proBNP in guiding the selection of patients for prolonged cardiac monitoring, thereby aiding in the management of cryptogenic stroke cases.

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.

In cryptogenic stroke and atrial cardiopathy, apixaban did not reduce recurrent stroke vs. aspirin

SOURCE CITATION

Kamel H, Longstreth WT Jr, Tirschwell DL, et al; ARCADIA Investigators. Apixaban to prevent recurrence after cryptogenic stroke in patients with atrial cardiopathy: the ARCADIA randomized clinical trial. JAMA. 2024;331:573-581. 38324415.

Atrial fibrillation and stroke: State-of-the-art and future directions

Atrial fibrillation (AF) and stroke remain a major cause of morbidity and mortality. The two conditions shared common co-morbidities and risk factors. AF-related strokes are associated with worse clinical outcomes and higher mortality compared to non-AF-related. Early detection of AF is vital for prevention. While various scores have been developed to predict AF in such a high-risk group, they are yet to incorporated into clinical guidelines. Novel markers and predictors of AF including coronary and intracranial arterial calcification have also been studied. There are also ongoing debates on the management of acute stroke in patients with AF, and those who experienced breakthrough stroke while on oral anticoagulants. We provided an overview of the complex interplay between AF and stroke, as well as the treatment and secondary prevention of stroke in AF. We also comprehensively discussed the current evidence and the ongoing conundrums, and highlighted the future directions on the topic.

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.

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.

Embolic Stroke of Undetermined Source: Current Perspectives on Diagnosis, Investigations, and Management

In 2014, Hart et al. introduced the concept of "embolic stroke of undetermined source" (ESUS) to the clinical-research stroke community. The hypothesis underlying the development of the ESUS construct was that this potentially heterogenous group of stroke mechanisms were largely thromboembolic, and would thus benefit from anticoagulation over antiplatelet for secondary prevention. Since then, 2 large clinical trials have shown that, to date, there is not a clear uniform antithrombotic strategy for secondary prevention after ESUS as it was originally broadly defined. However, this work has yielded valuable information about the patient phenotypes that experience ESUS strokes, as well as hypothesis-generating substudies that have given rise to the next generation of secondary prevention trials aimed at more personalized approaches for different suspected mechanisms of embolic stroke. In parallel with the evolution of ESUS, several studies aimed at screening for atrial fibrillation in the secondary stroke prevention population have generated additional questions about the mechanistic relevance of atrial fibrillation detected after stroke, and how this should inform poststroke workup, and secondary prevention strategies. Herein, we provide a synthesis of the current understanding surrounding the patient phenotypes that experience ESUS strokes, and previous, ongoing, and anticipated clinical trials that will guide earlier and later secondary prevention strategies and poststroke cardiac investigations.

A Simplified Risk Score to Predict In-Hospital Newly-Diagnosed Atrial Fibrillation in Acute Ischemic Stroke Patients

Purpose

Atrial fibrillation (AF) is a significant cause of stroke, and newly diagnosed AF (NDAF) is typically detected in the early period of stroke onset. We aimed to identify the factors associated with in-hospital NDAF in acute ischemic stroke patients and developed a simplified clinical prediction model.

Methods

Patients with cryptogenic stroke aged 18 years or older who were admitted between January 2017 and December 2021 were recruited. NDAF was determined by inpatient cardiac telemetry. Univariable and multivariable regression analyses were used to evaluate the factors associated with in-hospital NDAF. The predictive model was developed using regression coefficients.

Results

The study enrolled 244 eligible participants, of which 52 NDAFs were documented (21.31%), and the median time to detection was two days (1-3.5). After multivariable regression analysis, parameters significantly associated with in-hospital NDAF were elderly (>75 years) (adjusted Odds ratio, 2.99; 95% confident interval, 1.51-5.91; P = 0.002), female sex (2.08; 1.04-4.14; P = 0.04), higher admission national institute of health stroke scale (1.04; 1.00-1.09; P = 0.05), and presence of hyperdense middle cerebral artery sign (2.33; 1.13-4.79; P = 0.02). The area under the receiver operating characteristic curve resulted in 0.74 (95% CI 0.65-0.80), and the cut-point of 2 showed 87% sensitivity and 42% specificity.

Conclusion

The validated and simplified risk scores for predicting in-hospital NDAF primarily rely on simplified parameters and high sensitivity. It might be used as a screening tool for in-hospital NDAF in stroke patients who initially presumed cryptogenic stroke.

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.

Association of Chronic Covert Cerebral Infarctions and White Matter Hyperintensities With Atrial Fibrillation Detection on Post-Stroke Cardiac Rhythm Monitoring: A Cohort Study

Background This study was conducted to explore the association of different phenotypes, count, and location of chronic covert brain infarctions (CBIs) with detection of atrial fibrillation (AF) on prolonged post-stroke cardiac rhythm monitoring (PCM). Methods and Results We conducted a cohort single-center study of consecutive first-ever ischemic stroke or transient ischemic attack patients undergoing PCM between January 2015 and December 2017. We blindly rated CBI phenotypes according to established definitions and white matter hyperintensities (WMHs) according to the age-related white matter changes rating scale. We used (multiple) regression models to assess the association of the imaging biomarkers and incident AF on PCM. A total of 795 patients (median [interquartile range]) aged 69 (57-78) years, 41% women, median National Institutes of Health Stroke Scale score 2 (0-5), median PCM duration 14 (7-14) days, and AF detection in 61 patients (7.7%) were included. On univariate analysis, WMHs (per point odds ratio, 1.35 [95% CI, 1.03-1.78]) but not CBIs (odds ratio, 0.90 [95% CI, 0.52-1.56]) were associated with AF detection. Neither CBI phenotype, count, nor location were associated with AF detection. After adjustment for age, hypertension, and stroke severity, neither increasing WMHs (per point adjusted odds ratio, 0.85 [95% CI, 0.60-1.20]) nor CBIs (adjusted odds ratio, 0.60 [95% CI, 0.33-1.09]) were independently associated with AF detection. Conclusions Although WMHs and CBIs represent surrogate biomarkers of vascular risk factors, neither WMHs nor CBIs, including their phenotypes, count, and location, were independently associated with AF detection on PCM. In patients with manifest ischemic stroke or transient ischemic attack, the presence of imaging biomarkers of chronic ischemic injury does not seem promising to further refine prediction tools for AF detection on PCM.

Molecular biomarkers predicting newly detected atrial fibrillation after ischaemic stroke or TIA: A systematic review

Background:

Several molecular biomarkers are available that predict newly detected atrial fibrillation (NDAF). We aimed to identify such biomarkers that predict NDAF after an Ischaemic stroke (IS)/Transient Ischaemic Attack (TIA) and evaluate their performance.

Methods:

A systematic review was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Studies of patients with IS, TIA, or both, who underwent ECG monitoring for ⩾24 h, which reported molecular biomarkers and frequency of NDAF after electronic searches of multiple databases were included.

Results:

Twenty-one studies (76% IS, 24% IS and TIA) involving 4640 patients were included. Twelve biomarkers were identified, with cardiac biomarkers evaluated in the majority (75%) of patients. Performance measures were inconsistently reported. Among cohorts selecting high-risk individuals (12 studies), the most studied biomarkers were N-Terminal-Pro Brain Natriuretic Peptide (NT-ProBNP, five studies; C-statistics reported by three studies, 0.69–0.88) and Brain Natriuretic Peptide (BNP, two studies; C-statistics reported in two studies, 0.68–0.77). Among unselected cohorts (nine studies), the most studied biomarker was BNP (six studies; C-statistics reported in five studies, 0.75–0.88). Only BNP was externally validated (two studies) but using different thresholds to categorise risk of NDAF.

Conclusion:

Cardiac biomarkers appear to have modest to good discrimination for predicting NDAF, although most analyses were limited by small, heterogeneous study populations. Their clinical utility should be explored further, and this review supports the need to assess the role of molecular biomarkers in large prospective studies with standardised selection criteria, definition of clinically significant NDAF and laboratory assays.

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.