A Clinical Score for Predicting Atrial Fibrillation in Patients with Cryptogenic Stroke or Transient Ischemic Attack

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.

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.

Value of HAVOC and Brown ESUS-AF scores for atrial fibrillation on implantable cardiac monitors after embolic stroke of undetermined source

OBJECTIVES

Up to 20 % of ischemic strokes are associated with overt atrial fibrillation (AF). Furthermore, silent AF was detected by an implantable cardiac monitor (ICM) in 1 in 3 cryptogenic strokes in the CRYSTAL AF study. An ESC position paper has suggested a HAVOC score ≥ 4 or a Brown ESUS-AF score ≥ 2 as criteria for ICM implantation after cryptogenic stroke, but neither of these criteria has been developed or validated in ICM populations. We assessed the performance of HAVOC and Brown ESUS-AF scores in a cohort of ICM patients implanted after embolic stroke of undetermined source (ESUS).

METHODS

All patients implanted with an ICM for ESUS between February 2016 and February 2022 at two French University Hospitals were retrospectively included. Demographic data, cardiovascular risk factors, and clinical and biological data were collected after a review of electronic medical records. HAVOC and Brown ESUS-AF scores were calculated for all patients.

FINDINGS

Among the 384 patients included, 106 (27 %) developed AF during a mean follow-up of 33 months. The scores performances for predicting AF during follow-up were: HAVOC= AUC: 68.5 %, C-Index: 0.662, and Brown ESUS-AF=AUC: 72.9 %, C-index 0.712. Compared with the CHA2DS2-VASc score, only the Brown ESUS-AF score showed significant improvement in NRI/IDI. Furthermore, classifying patients according to the suggested HAVOC and Brown ESUS-AF thresholds, only 24 % and 31 % of the cohort, respectively, would have received an ICM, and 58 (55 %) and 47 (44 %) of the AF patients, respectively, would not have been implanted with an ICM.

CONCLUSION

HAVOC and Brown ESUS-AF scores showed close and moderate performance in predicting AF on ICM after cryptogenic stroke, with a significant lack of sensitivity. Specific risk scores should be developed and validated in large ICM cohorts.

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.

Advances in Cardiac Workup for Transient Ischemic Attack: Improving Diagnostic Yield and Reducing Recurrent Stroke Risk

Transient ischemic attack (TIA) is a warning sign for an impending stroke, with a 10-20% chance of a stroke occurring within 90 days of the initial event. Current clinical practice for cardiac workup in TIA includes cardiac enzymes, with 12-lead electrocardiogram, transthoracic echocardiography, and 24-hour Holter monitoring. However, the diagnostic yield of these investigations is variable, and there is a need for better diagnostic approaches to increase the detection of cardiac abnormalities in a cost-effective way. This review article examines the latest research on emerging diagnostic tools and strategies and discusses the potential benefits and challenges of using these advanced diagnostic approaches in clinical practice. Novel biomarkers, imaging techniques, and prolonged rhythm monitoring devices have shown great promise in enhancing the diagnostic yield of cardiac workup in TIA patients. Echocardiography, Transcranial Doppler ultrasound, cardiac MRI, and cardiac CT are among the promising diagnostic tools being studied. We conclude the article with a suggested diagnostic algorithm for cardiac workup in TIA. Further research is necessary to enhance their usefulness and to outline future directions for research and clinical practice in this field.

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.

Detection of Atrial Fibrillation after Ischemic Stroke with an Insertable Cardiac Monitor: A Systematic Review and Individual Patient Data Meta-Analysis of Randomized Clinical Trials

INTRODUCTION

We compared the use of insertable cardiac monitor (ICM) versus non-ICM methods of cardiac monitoring in ischemic stroke patients on the detection of atrial fibrillation (AF) and other clinical outcomes. Current guidelines recommend the routine use of 12-lead electrocardiography or Holter monitoring for AF detection after ischemic stroke. Recent randomized controlled trials have investigated the impact of ICM versus non-ICM methods of cardiac monitoring for AF detection in this population. However, precise recommendations for monitoring poststroke AF are lacking, including the optimal timing, duration, and method of electrocardiography monitoring.

METHODS

A systematic search was conducted on Embase and PubMed from database inception until October 27, 2022, to include randomized controlled trials that compared ICM with non-ICM methods of cardiac monitoring for poststroke AF detection. This yielded 3 randomized controlled trials with a combined cohort of 1,233 patients with a recent ischemic stroke. Individual patient data (IPD) were then reconstructed from Kaplan-Meier curves and analyzed using the shared-frailty Cox model. An aggregate data meta-analysis was conducted for 1,233 patients across all 3 studies for outcomes that could not be reconstructed using IPD.

RESULTS

One-stage meta-analysis demonstrated an increase in the hazard ratio (6.00 95% CI: 3.40-10.58; 195 p < 0.001) of AF detection in patients undergoing monitoring via ICM compared to standard care. The cumulative incidence curves of AF events in patients undergoing ICM and non-ICM separated significantly at 24 days. Aggregate data meta-analysis revealed a significant increase in initiation of anticoagulation (RR: 2.76, 95% CI: 1.89-4.02, p < 0.00001) in the ICM group. However, no significant differences in the incidence of recurrent ischemic stroke, transient ischemic attack, or death were found.

CONCLUSIONS

In this meta-analysis, we found that the use of ICM increased the detection rate of poststroke AF and the rate of anticoagulation initiation in ischemic stroke patients. However, this did not translate into a reduced incidence of recurrent ischemic stroke.

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.

Use of the insertable Holter with remote detection in the etiological diagnosis of cryptogenic stroke: Analysis of 73 patients

INTRODUCTION

Cryptogenic stroke constitutes 25% of all ischemic strokes, of which 20-30% are due to atrial fibrillation (AF). With the aim of increasing the detection rate, implantable long-term monitoring devices have emerged. The study of the profile of the ideal candidate subsidiary to such monitoring would provide a better understanding of the mechanisms underlying this subtype of stroke.

OBJECTIVE

To determine which variables are related and can predict the detection of silent AF in patients with cryptogenic stroke.

PATIENTS AND METHODS

This is a longitudinal cohort with recruitment from March 2017 to May 2022. They are patients with an implantable monitoring device and cryptogenic stroke with a minimum monitoring of one year.

RESULTS

The total number of patients included was 73, with a mean age of 58.8 years, 56.2% were male. AF was detected in 21 patients (28.8%). The most frequent cardiovascular risk factors were hypertension (47.9%) and dyslipidemia (45.2%). The most frequent topography was cortical (52%). Regarding the echocardiographic parameters, 22% had a dilated left atrium, 19% had a patent foramen ovale, and 22% had high-density supraventricular tachycardia (>1%) on Holter monitoring. In the multivariate analysis, the only variable that predicts AF is the presence of high-density supraventricular tachycardia, with an area under the curve of 0.726 (CI 0.57-0.87, p=0.04), sensitivity of 47.6%, specificity of 97.5%, positive predictive value of 90.9%, negative predictive value of 78.8%, and accuracy of 80.9%.

CONCLUSIONS

The presence of high-density supraventricular tachycardia can be indicative for predicting silent AF. No other variables have been observed that allow us to predict detection of AF in these patients.

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.

Atrial Fibrillation Risk Assessment after Embolic Stroke of Undetermined Source

OBJECTIVE

Approximately 20% of strokes are embolic strokes of undetermined source (ESUS). Undetected atrial fibrillation (AF) remains an important cause. Yet, oral anticoagulation in unselected ESUS patients failed in secondary stroke prevention. Guidance on effective AF detection is lacking. Here, we introduce a novel, non-invasive AF risk assessment after ESUS.

METHODS

Catch-Up ESUS is an investigator-initiated, observational cohort study conducted between 2018 and 2019 at the Munich University Hospital. Besides clinical characteristics, patients received ≥72 h digital electrocardiogram recordings to generate the rhythm irregularity burden. Uni- and multivariable regression models predicted the primary endpoint of incident AF, ascertained by standardized follow-up including implantable cardiac monitors. Predictors included the novel rhythm irregularity burden constructed from digital electrocardiogram recordings. We independently validated our model in ESUS patients from the University Hospital Tübingen, Germany.

RESULTS

A total of 297 ESUS patients were followed for 15.6 ± 7.6 months. Incident AF (46 patients, 15.4%) occurred after a median of 105 days (25th to 75th percentile 31-33 days). Secondary outcomes were recurrent stroke in 7.7% and death in 6.1%. Multivariable-adjusted analyses identified the rhythm irregularity burden as the strongest AF-predictor (hazard ratio 3.12, 95% confidence interval 1.62-5.80, p < 0001) while accounting for the known risk factors age, CHA₂ DS₂ -VASc-Score, and NT-proBNP. Independent validation confirmed the rhythm irregularity burden as the most significant AF-predictor (hazard ratio 2.20, 95% confidence interval 1.45-3.33, p < 0001).

INTERPRETATION

The novel, non-invasive, electrocardiogram-based rhythm irregularity burden may help adjudicating AF risk after ESUS, and subsequently guide AF-detection after ESUS. Clinical trials need to clarify if high-AF risk patients benefit from tailored secondary stroke prevention. ANN NEUROL 2023;93:479-488.

Evaluation of Huawei smart wearables for detection of atrial fibrillation in patients following ischemic stroke: The Liverpool-Huawei stroke study

BACKGROUND

Atrial fibrillation (AF) often remains undetected following stroke. Documenting AF is critical to initiate oral anticoagulation, which has proven benefit in reducing recurrent stroke and mortality in patients with AF. The accuracy and acceptability of using smart wearables to detect AF in patients following stroke is unknown.

METHODS

The aims of the Liverpool-Huawei Stroke Study are to determine the effectiveness, cost-effectiveness and patient and staff acceptability of using Huawei smart wearables to detect AF following ischemic stroke. The study plans to recruit 1,000 adults aged ≥18 years following ischemic stroke from participating hospitals over 12 months. All participants will be asked to wear a Huawei smart band for 4 weeks postdischarge. If participants do not have access to a compatible smartphone required for the study, they will be provided with a smartphone for the 4-week AF monitoring period.

RESULTS

Participants with suspected AF detected by the smart wearables, without previous known AF, will be referred for further evaluation. To determine the effectiveness of the Huawei smart wearables to detect AF, the positive predictive value will be determined. Patient acceptability of using this technology will also be examined. Additional follow-up assessments will be conducted at 6 and 12 months, and clinical outcomes recorded in relation to prevalent and incident AF post-stroke. The study opened for recruitment on May 30, 2022, and is currently open at 4 participating hospitals; the first 106 participants have been recruited. One further hospital is preparing to open for recruitment.

CONCLUSIONS

This prospective study will examine the effectiveness and acceptability of the use of smart wearables in patients following ischemic stroke. This could have important implications for detection of AF and therefore, earlier prophylaxis for recurrent stroke. The study is registered on https://www.isrctn.com/ (Identifier ISRCTN30693819).

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.

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.

Determining the etiology of cerebral stroke: from the most prevalent to rare causes

Cerebral stroke remains the leading cause of death and disability worldwide as well as in Ukraine. After a cerebral stroke, there is an increased risk of a new cerebral stroke (9‒15 % within 1 year), and about a quarter of all cerebral stroke are recurrent. Up to 80 % of recurrent cerebral stroke can be avoided through lifestyle modifications (healthy diet, sufficient amount of physical activity, normalization of body weight, cessation of smoking and alcohol abuse) and control of chronic diseases such as hypertension, diabetes, hyperlipidemia and atrial fibrillation. The key to effective secondary prevention is determining the etiology of cerebral stroke, which requires a primary examination in all cases and a number of additional tests as needed. The most common causes of ischemic cerebral stroke are cardiogenic embolism, atherosclerosis of the large cerebral arteries (macroangiopathy), and brain small vessels disease (microangiopathy), but approximately 1/3 of cerebral stroke have other, rear, determined cause or the cause remains unknown despite the appropriate workup (cryptogenic cerebral stroke). In the review, we discuss modern approaches to ischemic cerebral stroke classification and determination of their etiology, from the most prevalent to the rarest causes. A careful search for the cause of cerebral stroke is particularly important in young patients (aged 18 to 50 years) with a high life expectancy. We have reviewed in detail the possibilities of screening for subclinical atrial fibrillation by long-term cardiac monitoring with implantable devices and the diagnosis of monogenetic causes of cerebral stroke, with a particular focus on Fabry disease, for which there is an effective treatment.

Prolonged cardiac monitoring for stroke prevention: A systematic review and meta-analysis of randomized-controlled clinical trials

Introduction:

Prolonged cardiac monitoring (PCM) substantially improves the detection of subclinical atrial fibrillation (AF) among patients with history of ischemic stroke (IS), leading to prompt initiation of anticoagulants. However, whether PCM may lead to IS prevention remains equivocal.

Patients and methods:

In this systematic review and meta-analysis, randomized-controlled clinical trials (RCTs) reporting IS rates among patients with known cardiovascular risk factors, including but not limited to history of IS, who received PCM for more than 7 days versus more conservative cardiac rhythm monitoring methods were pooled.

Results:

Seven RCTs were included comprising a total of 9048 patients with at least one known cardiovascular risk factor that underwent cardiac rhythm monitoring. PCM was associated with reduction of IS occurrence compared to conventional monitoring (Risk Ratio: 0.76; 95% CI: 0.59–0.96; I2 = 0%). This association was also significant in the subgroup of RCTs investigating implantable cardiac monitoring (Risk Ratio: 0.75; 95% CI: 0.58–0.97; I2 = 0%). However, when RCTs assessing PCM in both primary and secondary prevention settings were excluded or when RCTs investigating PCM with a duration of 7 days or less were included, the association between PCM and reduction of IS did not retain its statistical significance. Regarding the secondary outcomes, PCM was related to higher likelihood for AF detection and anticoagulant initiation. No association was documented between PCM and IS/transient ischemic attack occurrence, all-cause mortality, intracranial hemorrhage, or major bleeding.

Conclusion:

PCM may represent an effective stroke prevention strategy in selected patients. Additional RCTs are warranted to validate the robustness of the reported associations.

A nomogram for predicting atrial fibrillation detected after acute ischemic stroke

Background

Atrial fibrillation detected after stroke (AFDAS) is associated with an increased risk of ischemic stroke (IS) recurrence and death. Early diagnosis can help identify strategies for secondary prevention and improve prognosis. However, there are no validated predictive tools to assess the population at risk for AFDAS. Therefore, this study aimed to develop and validate a predictive model for assessing the incidence of AFDAS after acute ischemic stroke (AIS).

Methods

This study was a multicenter retrospective study. We collected clinical data from 5332 patients with AIS at two hospitals between 2014.01 and 2021.12 and divided the development and validation of clinical prediction models into a training cohort (n = 3173) and a validation cohort (n = 2159). Characteristic variables were selected from the training cohort using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariable logistic regression analysis. A nomogram model was developed, and its performance was evaluated regarding calibration, discrimination, and clinical utility.

Results

We found the best subset of risk factors based on clinical characteristics and laboratory variables, including age, congestive heart failure (CHF), previous AIS/transient ischemia attack (TIA), national institutes of health stroke scale (NIHSS) score, C-reactive protein (CRP), and B-type natriuretic peptide (BNP). A predictive model was developed. The model showed good calibration and discrimination, with calibration values of Hosmer-Lemeshow χ² = 4.813, P = 0.732 and Hosmer-Lemeshow χ² = 4.248, P = 0.834 in the training and validation cohorts, respectively. The area under the ROC curve (AUC) was 0.815, 95% CI (0.777–0.853) and 0.808, 95% CI (0.770–0.847). The inclusion of neuroimaging variables significantly improved the performance of the integrated model in both the training cohort (AUC. 0.846 (0.811–0.882) vs. 0.815 (0.777–0.853), P = 0.001) and the validation cohort (AUC: 0.841 (0.804–0.877) vs. 0.808 (0.770–0.847), P = 0.001). The decision curves showed that the integrated model added more net benefit in predicting the incidence of AFDAS.

Conclusion

Predictive models based on clinical characteristics, laboratory variables, and neuroimaging variables showed good calibration and high net clinical benefit, informing clinical decision-making in diagnosing and treating patients with AFDAS.

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.

Non-conducted premature atrial complexes: A new independent predictor of atrial fibrillation in cryptogenic stroke

INTRODUCTION

Atrial fibrillation (AF) is the main cardiac cause of stroke, but it frequently remains undetected. In patients with cryptogenic stroke an Holter electrocardiogram (ECG) monitoring for AF is recommended.

OBJECTIVE

To evaluate the prognostic role of Non-Conducted Premature Atrial Complexes (ncPACs) recorded on Holter ECG.

METHODS

We prospectively enrolled consecutive patients admitted to the Stroke Unit of our hospital with a diagnosis of cryptogenic stroke between December 2018 and January 2020; all patients underwent 24-h Holter ECG monitoring during hospitalization. Two follow-up visits were scheduled, including a 24-h Holter ECG at 3 and 6 months to detect AF.

RESULTS

Among 112 patients, 58% were male with an average age of 72.2 ± 12.2 years. At follow-up, AF was diagnosed in 21.4% of the population. The baseline 24-h Holter ECG burden of ncPACs and Premature Atrial Complexes (PACs) was higher in patients with AF detected on follow-up (13.5 vs 2, p = 0.001; 221.5 vs 52; p = 0.01). ROC analysis showed that ncPACs had the best diagnostic accuracy in predicting AF (AUC:0.80; 95% CI 0.68-0.92). Cut-off value of ≥7 for ncPACs burden showed the highest accuracy with sensitivity of 62.5% and specificity 97.7% to predict AF onset at follow-up. Moreover, at multivariate Cox-proportional hazard analysis ncPACs burden ≥7 was a powerful independent predictor of AF onset (HR 12.4; 95% CI 4.8-32.8; p < 0.0001).

CONCLUSIONS

NcPACs burden ≥7 represents a new predictor of AF that could guide the screening of this arrhythmia in cryptogenic stroke patients.

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.

ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients : In collaboration with the Council on Stroke, the European Heart Rhythm Association, and the Digital Health Committee

The role of subclinical atrial fibrillation as a cause of cryptogenic stroke is unambiguously established. Long-term electrocardiogram (ECG) monitoring remains the sole method for determining its presence following a negative initial workup. This position paper of the European Society of Cardiology Working Group on e-Cardiology first presents the definition, epidemiology, and clinical impact of cryptogenic ischaemic stroke, as well as its aetiopathogenic association with occult atrial fibrillation. Then, classification methods for ischaemic stroke will be discussed, along with their value in providing meaningful guidance for further diagnostic efforts, given disappointing findings of studies based on the embolic stroke of unknown significance construct. Patient selection criteria for long-term ECG monitoring, crucial for determining pre-test probability of subclinical atrial fibrillation, will also be discussed. Subsequently, the two major classes of long-term ECG monitoring tools (non-invasive and invasive) will be presented, with a discussion of each method's pitfalls and related algorithms to improve diagnostic yield and accuracy. Although novel mobile health (mHealth) devices, including smartphones and smartwatches, have dramatically increased atrial fibrillation detection post ischaemic stroke, the latest evidence appears to favour implantable cardiac monitors as the modality of choice; however, the answer to whether they should constitute the initial diagnostic choice for all cryptogenic stroke patients remains elusive. Finally, institutional and organizational issues, such as reimbursement, responsibility for patient management, data ownership, and handling will be briefly touched upon, despite the fact that guidance remains scarce and widespread clinical application and experience are the most likely sources for definite answers.

European Stroke Organisation (ESO) guideline on screening for subclinical atrial fibrillation after stroke or transient ischaemic attack of undetermined origin

We aimed to provide practical recommendations for the screening of subclinical atrial fibrillation (AF) in patients with ischaemic stroke or transient ischaemic attack (TIA) of undetermined origin. These guidelines are based on the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. Five relevant Population, Intervention, Comparator, Outcome questions were defined by a multidisciplinary module working group (MWG).

Longer duration of cardiac rhythm monitoring increases the detection of subclinical AF, but the optimal monitoring length is yet to be defined. We advise longer monitoring to increase the rate of anticoagulation, but whether longer monitoring improves clinical outcomes needs to be addressed. AF detection does not differ from in- or out-patient ECG-monitoring with similar monitoring duration, so we consider it reasonable to initiate in-hospital monitoring as soon as possible and continue with outpatient monitoring for more than 48h. Although insertable loop recorders (ILR) increase AF detection based on their longer monitoring duration, comparison with non-implantable ECG devices for similar monitoring time is lacking. We suggest the use of implantable devices, if feasible, for AF detection instead of non- implantable devices to increase the detection of subclinical AF. There is weak evidence of a useful role for blood, ECG, and brain imaging biomarkers for the identification of patients at high risk of AF. In patients with patent foramen ovale, we found insufficient evidence from RCT, but prolonged cardiac monitoring in patients >55 years is advisable for subclinical AF detection. To conclude, in adult patients with ischaemic stroke or TIA of undetermined origin, we recommend longer duration of cardiac rhythm monitoring of more than 48h and if feasible with IRL to increase the detection of subclinical AF.

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).

Comparisons of HATCH, HAVOC and CHA2DS2-VASc scores for all-cause mortality prediction in atrial fibrillation: a real-world evidence study

OBJECTIVE

This study focused on the predictive ability of the 3 scores for all-cause mortality in 6444 patients with atrial fibrillation (AF).

METHODS

To assess the predictive accuracy of risk of death modelled by HATCH, HAVOC and CHA2DS2-VASc scores, the area under the curve of receiver operating characteristics (AUROC) was applied.

RESULTS

Over follow-up time, the cumulative incidence of death was clearly associated with the three scores (log-rank test, p<0.001). The AUROC for the HATCH (0.6618) was significantly higher than HAVOC Score (0.5733) and CHA2DS2-VAScs Score (0.6423).

CONCLUSIONS

HATCH score has better ability in predicting mortality in comparison to other two scores in patients with AF.

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.

HAT2CH2 Score Predicts Systemic Thromboembolic Events in Elderly After Cardiac Electronic Device Implantation

Background: The HAT₂CH₂ score has been evaluated for predicting new onset atrial fibrillation, but never for adverse systemic thromboembolic events (STE) in elderly. We aimed to evaluate the HAT₂CH₂ score and comparing to atrial high rate episodes (AHRE) ≥24 h for predicting STE in older patients with cardiac implantable electronic devices (CIED) implantation.

Methods: We retrospective enrolled 219 consecutive patients ≥ 65 years of age undergoing CIED implantation. The primary endpoint was subsequent STE. For all patients in the cohort, the CHA₂DS₂-VASc, C₂HEST, mC₂HEST, HAVOC, HAT₂CH₂ scores and AHRE ≥ 24 h were determined. AHRE was defined as > 175 bpm lasting ≥ 30 s. Multivariate Cox regression analysis with time-dependent covariates was used to determine variables associated with independent risk of STE.

Results: The median patient age was 77 years, and 61.2% of the cohort was male. During follow-up (median, 35 months), 16 STE occurred (incidence rate, 2.51/100 patient-years; 95% CI, 1.65–5.48). Multiple Cox regression analysis showed that the HAT₂CH₂ score (HR, 3.405; 95% CI, 2.272–5.104; p < 0.001) was an independent predictor for STE. The optimal HAT₂CH₂ score cutoff value was 3, with the highest Youden index (AUC, 0.907; 95% CI, 0.853–0.962; p < 0.001). The STE rate increased with increasing HAT₂CH₂ score (p < 0.001).

Conclusions: This study is the first to show the prognostic value of the HAT₂CH₂ score for STE occurrence in older patients with CIEDs.

The HAT2CH2 score predicts neurologic events in patients with cardiac implantable electronic devices without atrial fibrillation

BACKGROUND

The HAT₂CH₂ score has been evaluated for predicting new-onset atrial fibrillation (AF) in several clinical conditions but never for adverse neurologic events. We aimed to evaluate the HAT₂CH₂ score for predicting neurologic events in patients with cardiac implantable electronic devices (CIED).

METHODS AND RESULTS

We retrospectively reviewed 470 consecutive patients who had CIED without a history of AF. The primary endpoint was a neurologic event, i.e. ischemic stroke or transient ischemic attack. Multivariate Cox regression analysis with time-dependent covariates was used to determine variables associated with independent factors of neurologic events. Patients' median age was 76 years, and 58.7% were male. During follow-up (median 29 months), 21 neurologic events occurred (incidence rate 1.85/100 patient-years, 95% CI 1.03-3.83). Multivariable Cox regression analysis revealed that the HAT₂CH₂ score (HR 3.397, 95% CI 2.357-4.895, p < 0.001) was an independent predictor for neurologic events. Optimal HAT₂CH₂ score cut-off value was 3, with highest Youden index (AUC, 0.923; 95% CI, 0.886-0.959; p < 0.001). The rate of neurologic events increased significantly with increasing HAT₂CH₂ score (p < 0.001).

CONCLUSIONS

The HAT₂CH₂ score can predict the occurrence of neurologic events in patients with CIED with no history of AF. Further study of the utility of the HAT₂CH₂ score for the assessment of neurologic event risk and the selection of anti-thrombotic therapy in patients with CIED without prior AF is warranted.

Heart-Brain Relationship in Stroke

The patient presenting with stroke often has cardiac-related risk factors which may be involved in the mechanism of the stroke. The diagnostic assessment is predicated on recognition of this potential relationship. Naturally, an accurate history is of utmost importance in discerning a possible cause and effect relationship. The EKG is obviously an important clue as well as it allows immediate assessment for possible cardiac arrhythmia, such as atrial fibrillation, for possible acute ischemic changes reflective of myocardial ischemia, or there may be indirect factors such as the presence of left ventricular hypertrophy, typically seen with longstanding hypertension, which could be indicative of a hypertensive mechanism for a patient presenting with intracerebral hemorrhage. For all presentations in the emergency room, the vital signs are important. An elevated body temperature in a patient presenting with acute stroke raises concern about possible infective endocarditis. An irregular-irregular pulse is an indicator of atrial fibrillation. A markedly elevated blood pressure is not uncommon in both the acute ischemic and acute hemorrhagic stroke setting. One tends to focus on possible cardioembolic stroke if there is the sudden onset of maximum neurological deficit versus the stepwise progression more characteristic of thrombotic stroke. Because of the more sudden loss of vascular supply with embolic occlusion, seizure or syncope at onset tends to be supportive of this mechanism. Different vascular territory involvement on neuroimaging is also a potential indicator of cardioembolic stroke. Identification of a cardiogenic source of embolus in such a setting certainly elevates this mechanism in the differential. There have been major advances in management of acute cerebrovascular disease in recent decades, such as thrombolytic therapy and endovascular thrombectomy, which have somewhat paralleled the advances made in cardiovascular disease. Unfortunately, the successful limitation of myocardial damage in acute coronary syndrome, with intervention, does not necessarily mirror a similar salutary effect on functional outcome with cerebral infarction. The heart can also affect the brain from a cerebral perfusion standpoint. Transient arrhythmias can result in syncope, while cardiac arrest can result in hypoxic-ischemic encephalopathy. Cardiogenic dementia has been identified as a mechanism of cognitive impairment associated with severe cardiac failure. Structural cardiac abnormalities can also play a role in brain insult, and this can include tumors, such as atrial myxoma, patent foramen ovale, with the potential for paradoxical cerebral embolism, and cardiomyopathies, such as Takotsubo, can be associated with precipitous cardioembolic events.

Predictors of Atrial Fibrillation Development in Patients With Embolic Stroke of Undetermined Source: An Analysis of the RE-SPECT ESUS Trial

BACKGROUND

A proportion of patients with embolic stroke of undetermined source have silent atrial fibrillation (AF) or develop AF after the initial evaluation. Better understanding of the risk for development of AF is critical to implement optimal monitoring strategies with the goal of preventing recurrent stroke attributable to underlying AF. The RE-SPECT ESUS trial (Randomized, Double-Blind Evaluation in Secondary Stroke Prevention Comparing the Efficacy and Safety of the Oral Thrombin Inhibitor Dabigatran Etexilate Versus Acetylsalicylic Acid in Patients With Embolic Stroke of Undetermined Source) provides an opportunity to assess predictors for developing AF and associated recurrent stroke.

METHODS

RE-SPECT ESUS was a randomized, controlled trial (564 sites, 42 countries) assessing dabigatran versus aspirin for the prevention of recurrent stroke in patients with embolic stroke of undetermined source. Of 5390 patients enrolled and followed for a median of 19 months, 403 (7.5%) were found to develop AF reported as an adverse event or using cardiac monitoring per standard clinical care. Univariable and multivariable regression analyses were performed to define predictors of AF.

RESULTS

In the multivariable model, older age (odds ratio for 10-year increase, 1.99 [95% CI, 1.78-2.23]; P<0.001), hypertension (odds ratio, 1.36 [95% CI, 1.03-1.79]; P=0.0304), diabetes (odds ratio, 0.74 [95% CI, 0.56-0.96]; P=0.022), and body mass index (odds ratio for 5-U increase, 1.29 [95% CI, 1.16-1.43]; P<0.001) were independent predictors of AF during the study. In a sensitivity analysis restricted to 1117 patients with baseline NT-proBNP (N-terminal prohormone of brain natriuretic peptide) measurements, only older age and higher NT-proBNP were significant independent predictors of AF. Performance of several published predictive models was assessed, including HAVOC (AF risk score based on hypertension, age ≥75 years, valvular heart disease, peripheral vascular disease, obesity, congestive heart failure, and coronary artery disease) and CHA₂DS₂-VASc (stroke risk score based on congestive heart failure, hypertension, age ≥75 years [doubled], diabetes, previous stroke, transient ischemic attack or thromboembolism [doubled], vascular disease, age 65 to 74 years, and sex category [female]) scores, and higher scores were associated with higher rates of developing AF.

CONCLUSIONS

Besides age, the most important variable, several other factors, including hypertension, higher body mass index, and lack of diabetes, are independent predictors of AF after embolic stroke of undetermined source. When baseline NT-proBNP was available, only older age and elevation of this biomarker were predictive of subsequent AF. Understanding who is at higher risk of developing AF will assist in identifying patients who may benefit from more intense, long-term cardiac monitoring. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02239120.

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.

Real-world observational study of assessment of CHA2DS2-VASc, C2HEST and HAVOC scores for atrial fibrillation among patients with rheumatological disorders: a nationwide analysis

PURPOSE

This is a nationwide-based retrospective study aiming to compare the three different scoring systems (CHA₂DS₂-VASc, C₂HEST and HAVOC scores) in the prediction of atrial fibrillation (AF) in patients with rheumatological disease.

METHODS

We used the Fine and Gray model to estimate the risk of AF (subhazard ratio and 95% CI). The predictive accuracy and discriminatory ability of the predictive model were evaluated by receiver operating characteristic (ROC) curve.

RESULTS

Among the three predictive models, the model using CHA₂DS₂-VASc score had the better discriminative ability with an ROC of 0.79. The model with C₂HEST score had an ROC of 0.78. The discriminative ability of the HAVOC score was 0.77, estimated by ROC.

CONCLUSION

We concluded the CHA₂DS₂-VASc score has better performance in predicting AF compared with C₂HEST score or HAVOC score.

The significance of the morphology-voltage-P-wave duration (MVP) ECG score for prediction of in-hospital and long-term atrial fibrillation in ischemic stroke

BACKGROUND

Atrial fibrillation (AF) is the most common preventable cause of stroke. Diagnosis of new AF is frequent after acute ischemic stroke (AIS). We aimed to evaluate the predictive value of the recently developed morphology-voltage-P-wave duration (MVP) ECG risk score for in-hospital and long-term AF diagnosis following AIS.

MATERIAL AND METHODS

In this observational investigation, we evaluated the ability of the MVP ECG risk score to predict AF in 266 consecutive patients with AIS. The study population was divided into three groups according to their calculated MVP ECG risk score on admission electrocardiography. The groups were compared in terms of their predictive value for in-hospital and long-term AF diagnosis.

RESULTS

After adjustment for confounding baseline variables, MVP ECG risk score 5-6 group had 13.2 times higher rates of in-hospital AF compared to MVP ECG risk score 0-2 group, which was used as the reference group. For long-term follow-up, MVP ECG risk score 5-6 group had 5.2 times higher rates of long-term AF compared to MVP ECG risk score 0-2 group. A ROC analysis showed that the optimal cut-off value of the MVP ECG risk score to predict in-hospital AF was 4 with 78% sensitivity and 76% specificity (AUC: 0.80; 95% CI: 0.64-0.96; p < 0.001), the optimal cut-off value of the MVP ECG risk score to predict long-term AF was 3 with 85% sensitivity and 59% specificity (AUC: 0.81; 95% CI: 0.76-0.86; p < 0.001).

CONCLUSION

The MVP ECG risk score, which can be easily calculated from a surface ECG, can be used to guide who needs stricter monitoring for the diagnosis of long-term AF in patients with AIS.

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.

Rhythm monitoring strategies for atrial fibrillation detection in patients with cryptogenic stroke: A systematic review and meta-analysis

Objective

To summarize data on atrial fibrillation (AF) detection rates and predictors across different rhythm monitoring strategies in patients with cryptogenic stroke (CS) or embolic stroke of undetermined source (ESUS).

Methods

MEDLINE, Embase, and Web of Science were searched to identify all published studies providing relevant data through July 6, 2020. Random-effects meta-analysis method was used to pool estimates.

Results

We included 47 studies reporting on a pooled population of 8,215 patients with CS or ESUS. Using implantable cardiac monitor (ICM), the pooled rate of AF was 12.2% (95% CI 9.4–15.0) at 3 months, 16.0% (95% CI 13.2–18.8) at 6 months, 18.7% (95% CI 15.7–21.7) at 12 months, 22.8% (95% CI 19.1–26.5) at 24 months, and 28.5% (95% CI 17.6–39.3) at 36 months. AF rates were significantly higher in patients with ESUS vs CS (22.0% vs 14.2%; p < 0.001) at 6 months, and in studies using Reveal LINQ vs Reveal XT ICM (19.1% vs 13.0%; p = 0.001) at 12 months. Using mobile cardiac outpatient telemetry (MCOT), the pooled rate of AF was 13.7% (95% CI 10.2–17.2) at 1 month. Predictors of AF detection with ICM included older age, CHA₂DS₂-VASc score, left atrial enlargement, P wave maximal duration and prolonged PR interval.

Conclusion

The yield of ICM increases with the duration of monitoring. More than a quarter of patients with CS or ESUS will be diagnosed with AF during follow-up. About one in seven patients had AF detected within a month of MCOT, suggesting that a non-invasive rhythm monitoring strategy should be considered before invasive monitoring.

Successful detection of covert paroxysmal atrial fibrillation due to insertable cardiac monitor in embolic stroke of undetermined source in a patient with situs inversus totalis

Insertable cardiac monitors in patients with situs inversus totalis can detect atrial arrhythmia as a cause of embolic stroke. It is important to premap the position of ICM in order to clearly visualize the P wave.

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.

Implantable Cardiac Monitoring in the Secondary Prevention of Cryptogenic Stroke

OBJECTIVE

In this study, we sought to evaluate the impact of implantable cardiac monitoring (ICM) in the prevention of stroke recurrence after a cryptogenic ischemic stroke or transient ischemic attack (TIA).

METHODS

We evaluated consecutive patients with cryptogenic ischemic stroke or TIA admitted in a comprehensive stroke center during an 8-year period. We compared the baseline characteristics and outcomes between patients receiving conventional cardiac monitoring with repeated 24-hour Holter-monitoring during the first 5 years in the outpatient setting and those receiving continuous cardiac monitoring with ICM during the last 3 years. Associations on the outcomes of interest were further assessed in multivariable regression models adjusting for potential confounders.

RESULTS

We identified a total of 373 patients receiving conventional cardiac monitoring and 123 patients receiving ICM. Paroxysmal atrial fibrillation (PAF) detection was higher in the ICM cohort compared to the conventional cardiac monitoring cohort (21.1% vs 7.5%, p < 0.001). ICM was independently associated with an increased likelihood of PAF detection during follow-up (hazard ratio [HR] = 1.94, 95% confidence interval [CI] = 1.16-3.24) in multivariable analyses. Patients receiving ICM were also found to have significantly higher rates of anticoagulation initiation (18.7% vs 6.4%, p < 0.001) and lower risk of stroke recurrence (4.1% vs 11.8%, p = 0.013). ICM was independently associated with a lower risk of stroke recurrence during follow-up (HR = 0.32, 95% CI = 0.11-0.90) in multivariable analyses.

INTERPRETATION

ICM appears to be independently associated with a higher likelihood of PAF detection and anticoagulation initiation after a cryptogenic ischemic stroke or TIA. ICM was also independently related to lower risk of stroke recurrence in our cryptogenic stroke / TIA cohort. ANN NEUROL 2020;88:946-955.