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

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.

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.

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.

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.

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.

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.

Plasma levels of miRNA-1-3p are associated with subclinical atrial fibrillation in patients with cryptogenic stroke

INTRODUCTION AND OBJECTIVES

Identifying biomarkers of subclinical atrial fibrillation (AF) is of most interest in patients with cryptogenic stroke (CrS). We sought to evaluate the circulating microRNA (miRNA) profile of patients with CrS and AF compared with those in persistent sinus rhythm.

METHODS

Among 64 consecutive patients with CrS under continuous monitoring by a predischarge insertable monitor, 18 patients (9 with AF and 9 in persistent sinus rhythm) were selected for high-throughput determination of 754 miRNAs. Nine patients with concomitant stroke and AF were also screened to improve the yield of miRNA selection. Differentially expressed miRNAs were replicated in an independent cohort (n=46). Biological markers were stratified by the median and included in logistic regression analyses to evaluate their association with AF at 6 and 12 months.

RESULTS

Eight miRNAs were differentially expressed between patients with and without AF. In the replication cohort, miR-1-3p, a gene regulator involved in cardiac arrhythmogenesis, was the only miRNA to remain significantly higher in patients with CrS and AF vs those in sinus rhythm and showed a modest association with AF burden. High (= above the median) miR-1-3p plasma values, together with a low left atrial ejection fraction, were independently associated with the presence of AF at 6 and 12 months.

CONCLUSIONS

In this cohort, plasma levels of miR-1-3p were elevated in CrS patients with subsequent AF. Our results preliminarily suggest that miR-1-3p could be a novel biomarker that, together with clinical parameters, could help identify patients with CrS and a high risk of occult AF.

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.

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.

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.

Prospective multicentric validation of a novel prediction model for paroxysmal atrial fibrillation

Background

The early recognition of paroxysmal atrial fibrillation (pAF) is a major clinical challenge for preventing thromboembolic events. In this prospective and multicentric study we evaluated prediction scores for the presence of pAF, calculated from non-invasive medical history and echocardiographic parameters, in patients with unknown AF status.

Methods

The 12-parameter score with parameters age, LA diameter, aortic root diameter, LV,ESD, TDI Aʹ, heart frequency, sleep apnea, hyperlipidemia, type II diabetes, smoker, ß-blocker, catheter ablation, and the 4-parameter score with parameters age, LA diameter, aortic root diameter and TDI A’ were tested. Presence of pAF was verified by continuous electrocardiogram (ECG) monitoring for up to 21 days in 305 patients.

Results

The 12-parameter score correctly predicted pAF in all 34 patients, in which pAF was newly detected by ECG monitoring. The 12- and 4-parameter scores showed sensitivities of 100% and 82% (95%-CI 65%, 93%), specificities of 75% (95%-CI 70%, 80%) and 67% (95%-CI 61%, 73%), and areas under the receiver operating characteristic (ROC) curves of 0.84 (95%-CI 0.80, 0.88) and 0.81 (95%-CI 0.74, 0.87). Furthermore, properties of AF episodes and durations of ECG monitoring necessary to detect pAF were analysed.

Conclusions

The prediction scores adequately detected pAF using variables readily available during routine cardiac assessment and echocardiography. The model scores, denoted as ECHO-AF scores, represent simple, highly sensitive and non-invasive tools for detecting pAF that can be easily implemented in the clinical practice and might serve as screening test to initiate further diagnostic investigations for validating the presence of pAF.

Graphic abstract

Prospective validation of a novel prediction model for paroxysmal atrial fibrillation based on echocardiography and medical history parameters by long-term Holter ECG

Electronic supplementary material

The online version of this article (10.1007/s00392-020-01773-z) contains supplementary material, which is available to authorized users.