A Comprehensive Model to Predict Atrial Fibrillation in Cryptogenic Stroke: The Decryptoring Score

Computer-Assisted Electrocardiogram Analysis Improves Risk Assessment of Underlying Atrial Fibrillation in Cryptogenic Stroke

Background

The detection of underlying paroxysmal atrial fibrillation (AF) in patients with cryptogenic stroke (CS) can be challenging, and there is great interest in finding predictors of its hidden presence. The recent development of sophisticated software has enhanced the diagnostic and prognostic performance of the 12-lead electrocardiogram (ECG). Our aim was to assess the additional role of a computer-assisted ECG analysis in identifying predictors of AF in patients with CS.

Methods

Sixty-seven patients with ischemic stroke or high-risk transient ischemic attack of unknown etiology were prospectively studied. Their 12-lead digitized ECG was analyzed with dedicated software, quantifying 468 morphological variables. The main clinical, biochemical, and echocardiographic variables were also collected. At discharge, patients were monitored with a wearable Holter for 15 days, and the primary outcome was the detection of AF.

Results

The median age was 80 (interquartile range (IQR): 73 - 84) and AF was detected in 21 patients (31.3%). After preselecting significant ECG variables from the univariate analysis, a multivariate regression including other significant clinical, biochemical and echocardiographic predictors of AF was performed. Among the automatically analyzed ECG parameters, the amplitude of the R wave in V1 (V1_ramp) was significantly associated with the outcome. The best model to predict AF was composed of age, N-terminal B-type natriuretic peptide (NT-proBNP), left atrial reservoir strain (LASr) and V1_ramp. This model showed good discrimination capacity (corrected Somer's Dxy: 0.907, Brier's B: 0.079, area under the curve (AUC): 0.941) and performed better than the same model without the ECG variable (Somer's Dxy: 0.827, Brier's B: 0.119, AUC: 0.896).

Conclusions

The addition of computer-assisted ECG analysis can help stratify the risk of AF in the challenging clinical setting of CS.

Predicting the Propensity of Atrial Cardiopathy and Paroxysmal Atrial Fibrillation in Patients with Embolic Stroke of Undetermined Source (ESUS)

BACKGROUND

There is still a significant population of patients with embolic stroke of Undetermined Source (ESUS) whose specific attributable cause of the stroke remains unknown.

OBJECTIVES

Our research aimed to assess clinical, electrocardiogram, laboratory, and echocardiographic parameters that may predict the propensity of paroxysmal atrial fibrillation (PAF).

METHODS

We enrolled seventy-five ESUS patients who were in sinus rhythm at the time of stroke diagnosis to undergo in-hospital 7-day Holter monitoring, testing for Pro-BNP, and a standard echocardiographic examination. For statistical analysis, a P-value < 0.05 was considered significant.

RESULTS

The average age of the 75 ESUS patients was 58 years old. 60% of the patients were male, and the most prevalent concomitant condition was hypertension (53.3%). Forty patients had atrial cardiopathy, and 15 patients had PAF episodes. Hypertension and the E/e- > 12 were independent predictors of atrial cardiopathy, with p-values of 0.001 and 0.02, respectively. In patients with atrial cardiopathy, multivariable regression analysis was performed; PTFV > 5000 Mv.ms, LA volume index > 34 ml/m2, and ejection fraction < 45% were significant independent predictors of AF with significant p values of 0.001, < 0.001, and 0.001 respectively.

CONCLUSIONS

In ESUS patients, atrial cardiopathy was prevalent. Hypertension and an E/e- ratio greater than 12 were independent predictors for it. Multivariable regression analysis identified PTFV1 > 5000 mV.ms, LA volume index > 34 ml/m2, and ejection fraction < 45% as independent predictors for new-onset atrial fibrillation.

Predicting the Propensity of Atrial Cardiopathy and Paroxysmal Atrial Fibrillation in Patients with Embolic Stroke of Undetermined Source (ESUS)

BACKGROUND

There is still a significant population of patients with embolic stroke of Undetermined Source (ESUS) whose specific attributable cause of the stroke remains unknown.

OBJECTIVES

Our research aimed to assess clinical, electrocardiogram, laboratory, and echocardiographic parameters that may predict the propensity of paroxysmal atrial fibrillation (PAF).

METHODS

We enrolled seventy-five ESUS patients who were in sinus rhythm at the time of stroke diagnosis to undergo in-hospital 7-day Holter monitoring, testing for Pro-BNP, and a standard echocardiographic examination. For statistical analysis, a P-value < 0.05 was considered significant.

RESULTS

The average age of the 75 ESUS patients was 58 years old. 60% of the patients were male, and the most prevalent concomitant condition was hypertension (53.3%). Forty patients had atrial cardiopathy, and 15 patients had PAF episodes. Hypertension and the E/e- > 12 were independent predictors of atrial cardiopathy, with p-values of 0.001 and 0.02, respectively. In patients with atrial cardiopathy, multivariable regression analysis was performed; PTFV > 5000 Mv.ms, LA volume index > 34 ml/m2, and ejection fraction < 45% were significant independent predictors of AF with significant p values of 0.001, < 0.001, and 0.001 respectively.

CONCLUSIONS

In ESUS patients, atrial cardiopathy was prevalent. Hypertension and an E/e- ratio greater than 12 were independent predictors for it. Multivariable regression analysis identified PTFV1 > 5000 mV.ms, LA volume index > 34 ml/m2, and ejection fraction < 45% as independent predictors for new-onset atrial fibrillation.

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.

Cardiac Blood-Based Biomarkers of Myocardial Stress as Predictors of Atrial Fibrillation Development in Patients With Embolic Stroke of Undetermined Source/Cryptogenic Stroke: A Systematic Review and Meta-Analysis

BACKGROUND AND PURPOSE

Undiagnosed atrial fibrillation (AF) is a major risk factor for stroke that can go unnoticed in individuals with embolic stroke of undetermined source (ESUS) or cryptogenic stroke (CS). Early detection is critical for stroke prognosis and secondary prevention. This study aimed to determine if blood biomarkers of myocardial stress can accurately predict AF in patients with ESUS/CS, which would allow the identification of those who would benefit from closer monitoring.

METHODS

In February 2023 we performed a systematic date-unrestricted search of three databases for studies on patients with ESUS/CS who were subsequently diagnosed with AF. We examined the relationships between AF and serum myocardial stress markers such as brain natriuretic peptide (BNP), N-terminal-pro-BNP (NT-proBNP), midregional proatrial natriuretic peptide, and troponin.

RESULTS

Among the 1,527 studies reviewed, 23 eligible studies involving 6,212 participants, including 864 with AF, were analyzed. A meta-analysis of 9 studies indicated that they demonstrated a clear association between higher NT-proBNP levels and an increased risk of AF, with adjusted and raw data indicating 3.06- and 9.03-fold higher AF risks, respectively. Lower NT-proBNP levels had a pooled negative predictive value of 91.7%, indicating the potential to rule out AF with an 8% false-negative rate.

CONCLUSIONS

Further research is required to fully determine the potential of biomarkers for AF detection after stroke, as results from previous studies lack homogeneity. However, lower NT-proBNP levels have potential in ruling out AF in patients with ESUS/CS. Combining them with other relevant biomarkers may enhance the precision of identifying patients who will not benefit from extended monitoring, which would optimize resource allocation and patient care.

Left Atrial Strain Predicts Stroke Recurrence and Death in Patients With Cryptogenic Stroke

Left atrial strain (LAS) has been widely studied as a predictor of atrial fibrillation (AF) after cryptogenic stroke (CS). However, the evidence about its prognostic role in terms of stroke recurrence and death in this setting remains scarce. A total of 92 consecutive patients with ischemic stroke or transient ischemic attack with ABCD2 scale ≥4 of unknown etiology were prospectively recruited. Echocardiography, including LAS was performed during admission. The primary outcome measure was the composite of stroke recurrence or death. The mean age was 77.5 ± 7.7, and 58% of patients were female. After a median follow up of 28 months, the primary outcome measure occurred in 15 patients (16%). The primary outcome was more frequent in patients with diabetes (53% vs 21%, p = 0.02), chronic kidney disease (33% vs 10%, p = 0.034), and a history of heart failure (13% vs 0%, p = 0.025). LAS reservoir (LASr) and LAS conduit (LAScd) were lower in patients developing the primary outcome (21% ± 7% vs 28.8% ± 11%, p = 0.017 and 7.7% ± 3.9% vs 13.7% ± 7%, p = 0.007, respectively). On multivariate analysis, LASr (hazard ratio 0.9, 95% confidence interval 0.85 to 0.99, p = 0.048) and diabetes (hazard ratio 3.3, 95% confidence interval 1.03 to 10.4, p = 0.045) were associated with stroke recurrence or all-cause death after CS. On the log-rank test (using the discriminatory cut-off value of LASr <23%), LASr (p = 0.009) was associated with higher risk of the primary outcome. In conclusion, lower values of the LAS reservoir were associated with a higher risk of stroke recurrence or death after CS. LAS may identify patients at higher risk of thromboembolism and stress conditions.

Risk Stratification Tools to Guide a Personalized Approach for Cardiac Monitoring in Embolic Stroke of Undetermined Source

Current recommendations support a personalized sequential approach for cardiac rhythm monitoring to detect atrial fibrillation after embolic stroke of undetermined source. Several risk stratification scores have been proposed to predict the likelihood of atrial fibrillation after embolic stroke of undetermined source. This systematic review aimed to provide a comprehensive overview of the field by identifying risk scores proposed for this purpose, assessing their characteristics and the cohorts in which they were developed and validated, and scrutinizing their predictive performance. We identified 11 risk scores, of which 4 were externally validated. The most frequent variables included were echocardiographic markers and demographics. The areas under the curve ranged between 0.70 and 0.94. The 3 scores with the highest area under the curve were the Decryptoring (0.94 [95% CI, 0.88-1.00]), newly diagnosed atrial fibrillation (0.87 [95% CI, 0.79-0.94]), and AF-ESUS (Atrial Fibrillation in Embolic Stroke of Undetermined Source) (0.85 [95% CI, 0.80-0.87]), of which only the latter was externally validated. Risk stratification scores can guide a personalized approach for cardiac rhythm monitoring after embolic stroke of undetermined source.

In Situ Physiologic and Behavioral Monitoring With Digital Sensors for Cerebrovascular Disease: A Scoping Review

Cerebrovascular disease (CeVD) is a leading cause of death and disability worldwide. Early detection of behavioral and physiologic changes associated with CeVD may be critical to improving patient outcomes. The growing prevalence of remote monitoring tools, from wearable devices to smartphone applications, which facilitate in situ observation of patients, holds promise for more timely recognition and possible prevention of stroke. The goal of this review was to examine and establish categories of innovation with digital sensors that monitor physiologic and behavioral variables in situ to augment the current CeVD screening and diagnostic processes. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist, a search strategy spanning multiple databases from January 2012 to September 30, 2022, was implemented, aggregating 729 articles, of which 51 (7.0%) met the inclusion criteria. The articles were divided into 2 categories on the basis of their focus: physiologic and behavioral. Physiologic articles were sorted into 1 of the following 6 subcategories according to the signal(s) monitored: motor function, heart rhythm, heart rate, kinematic analysis, physical activity, and blood pressure. Behavioral articles were sorted into the following 3 subcategories: mood, cognitive function, and fatigue. Most studies used a wearable accelerometer, photoplethysmography-enabled smartwatch, or smartphone-based sensors. This scoping review identified disparate methods and conclusions associated with the use of digital sensors for in situ physiologic and behavioral monitoring of patients with CeVD. Although most articles evaluated pilot validation and feasibility trials, the lack of randomized controlled trials was identified as a critical gap specific to this evolving research area.

Probability of Cardioembolic vs. Atherothrombotic Pathogenesis of Cryptogenic Strokes in Older Patients

Some clinical, laboratory, ECG, and echocardiographic parameters could provide useful indications to assess the probability of cardioembolism or atherothrombosis in cryptogenic strokes. We retrospectively examined 290 patients with ischemic stroke aged ≥60 years, divided into 3 groups: strokes originating from large artery atherothrombosis (n = 92), cardioembolic strokes caused by paroxysmal atrial fibrillation (n = 88) and cryptogenic strokes (n = 110). In addition to echocardiographic and routine clinical-laboratory variables, neutrophil:lymphocyte ratio, red blood cell distribution width, mean platelet volume, P wave and PR interval duration and biphasic inferior P waves, both on admission and after 7 to 10 days, were also considered. By multiple logistic regression, cardioembolic strokes were compared with large artery atherothrombosis strokes, and beta coefficients were rounded to produce a scoring system. Late PR interval ≥188 ms, left atrium ≥4 cm, left ventricular end-diastolic volume <65 ml, and posterior circulation syndrome were associated with paroxysmal atrial fibrillation (positive scores). In contrast, male gender, hypercholesterolemia, and initial platelet count ≥290 × 10⁹/L were associated with atherothrombosis of large arteries (negative scores). The algebraic sum of these scores produced values indicative of cardioembolism if >0 (positive predictive value 89.1%), or of atherothrombosis, if ≤0 (positive predictive value 72.5%). The area under the receiver operating characteristic curve was 0.85. Among cryptogenic strokes, 41.5% had a score >0 (probable atrial fibrillation) and 58.5% had a score ≤0 (possible atherothrombosis). In conclusion, a scoring system based on electrocardiogram, laboratory, clinical and echocardiographic parameters can provide useful guidance for further investigations and secondary prevention in older patients with cryptogenic stroke.

Risk of recurrent stroke and mortality after cryptogenic stroke in diabetic patients

BACKGROUND

Diabetes mellitus is a highly prevalent and growing chronic disease that is associated with increased risk of recurrence among several stroke subtypes. However, evidence on the prognostic role of diabetes in the setting of cryptogenic stroke (CS) remains scarce.

METHODS

From April 2019 to November 2021, we recruited prospectively 78 consecutive patients with CS. Patients were classified according to the presence of diabetes. Main outcome was the composite of stroke recurrence and death. Secondary outcome was stroke recurrence.

RESULTS

Mean age of the cohort was 78 ± 7.7 years and 18 patients (23%) had diabetes. After a median clinical follow-up of 23 months the incidence of stroke recurrence and mortality [HR 5.8 (95% CI 1.9-19), p = 0.002] and the incidence of stroke recurrence [HR 16.6 (95% CI 1.8-149), p = 0.012], were higher in patients with diabetes. After adjusting for potential confounders diabetes was identified as an independent predictor of stroke recurrence and death in patients with CS [HR 33.8 (95% CI 2.1-551), p = 0.013]. Other independent predictors of stroke recurrence and mortality were hypertension [HR 31.4 (95% CI 1.8-550), p = 0.018], NTproBNP [HR 1.002 (95% CI 1.001-1.004), p = 0.013] and chronic kidney disease (CKD) [HR 27.4 (95% CI 1.4-549) p = 0.03]. Furthermore, diabetes was an independent predictor of stroke recurrence [HR 103 (95% CI 1.3-8261), p = 0.038].

CONCLUSION

Diabetic patients with CS are at higher risk of stroke recurrence and death. Hypertension CKD and NTproBNP are also independent predictors of stroke recurrence and death after CS.

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.

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.