Prediction of new-onset atrial fibrillation after first-ever ischemic stroke: A comparison of CHADS 2 , CHA 2 DS 2 -VASc and HATCH scores and the added value of stroke severity

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 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.

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.

Atrial Fibrillation Related Coronary Embolism: Diagnosis in the Focus

Atrial fibrillation (AF) is the most common arrhythmia in myocardial infarction (MI). AF can be caused by ischemia, and MI can be caused by AF. Additionally, 4-5% of MI cases are related to coronary embolism (CE), and one-third of cases are attributed to AF. Our aim was to investigate the prevalence of AF-related CE cases among 3 consecutive years of STEMI cases. We also aimed to reveal the diagnostic accuracy of the Shibata criteria scoring system and the role of thrombus aspiration. Among 1181 STEMI patients, 157 had AF (13.2%). By using the Shibata's diagnostic criteria, 10 cases were classified as 'definitive' and 31 as 'probable' CE. After re-evaluation, a further five cases were classified as 'definitive'. Further analysis of the 15 CE cases revealed that CE was more prevalent in patients with previously known (n = 10) compared to those with new-onset (n = 5) AF (16.7% vs. 5.1%, p = 0.024). A PubMed search was performed, and 40 AF-related cases were found where the Shibata's criteria could be applied. Further, 31 cases could be classified as 'definitive', 4 as 'probable' and, in 5 cases, the embolic origin could be excluded. In 40% of reported cases and in 47% of our cases, thrombus aspiration helped in diagnosis.

Atrial Fibrillation Occurring During Acute Hospitalization: A Scientific Statement From the American Heart Association

Acute atrial fibrillation is defined as atrial fibrillation detected in the setting of acute care or acute illness; atrial fibrillation may be detected or managed for the first time during acute hospitalization for another condition. Atrial fibrillation after cardiothoracic surgery is a distinct type of acute atrial fibrillation. Acute atrial fibrillation is associated with high risk of long-term atrial fibrillation recurrence, warranting clinical attention during acute hospitalization and over long-term follow-up. A framework of substrates and triggers can be useful for evaluating and managing acute atrial fibrillation. Acute management requires a multipronged approach with interdisciplinary care collaboration, tailoring treatments to the patient's underlying substrate and acute condition. Key components of acute management include identification and treatment of triggers, selection and implementation of rate/rhythm control, and management of anticoagulation. Acute rate or rhythm control strategy should be individualized with consideration of the patient's capacity to tolerate rapid rates or atrioventricular dyssynchrony, and the patient's ability to tolerate the risk of the therapeutic strategy. Given the high risks of atrial fibrillation recurrence in patients with acute atrial fibrillation, clinical follow-up and heart rhythm monitoring are warranted. Long-term management is guided by patient substrate, with implications for intensity of heart rhythm monitoring, anticoagulation, and considerations for rhythm management strategies. Overall management of acute atrial fibrillation addresses substrates and triggers. The 3As of acute management are acute triggers, atrial fibrillation rate/rhythm management, and anticoagulation. The 2As and 2Ms of long-term management include monitoring of heart rhythm and modification of lifestyle and risk factors, in addition to considerations for atrial fibrillation rate/rhythm management and anticoagulation. Several gaps in knowledge related to acute atrial fibrillation exist and warrant future research.

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.

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.

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.

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.

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.

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.

Long-term PM2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation

Clinical impact of fine particulate matter (PM_2.5) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM_2.5 for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837-0.853]) than existing traditional regression models using CHA₂DS₂-VASc (0.654 [0.646-0.661]), CHADS₂ (0.652 [0.646-0.657]), or HATCH (0.669 [0.661-0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM_2.5 as a highly important variable, we applied PM_2.5 for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM_2.5 (c-indices: boosted ensemble ML, 0.954 [0.949-0.959]; PM-CHA₂DS₂-VASc, 0.859 [0.848-0.870]; PM-CHADS₂, 0.823 [0.810-0.836]; or PM-HATCH score, 0.849 [0.837-0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM_2.5 data was found to predict incident AF better than models without PM_2.5 or even established risk prediction approaches in the general population exposed to high air pollution levels.

Determinants of Use of Long-term Continuous Electrocardiographic Monitoring for Acute Ischemic Stroke Patients without Atrial Fibrillation at Baseline

BACKGROUND

Atrial fibrillation (AF) is the most common cardiac rhythm disorder associated with stroke. Increased risk of stroke is the same regardless of whether the AF is permanent or paroxysmal. However, detecting paroxysmal AF is challenging and resource intensive. We aimed to develop a predictive model for AF in patients with acute ischemic stroke, which could improve the detection rate of paroxysmal AF.

METHODS

We analyzed 10,034 adult patients with acute ischemic stroke. Differences in clinical characteristics between the patients with and without AF were analyzed in order to develop a predictive model of AF. The associated factors for AF were analyzed using multivariate logistic regression and classification and regression tree (CART) analyses. We used another dataset, which enrolled 860 acute ischemic stroke patients without AF at baseline, to test whether the developed model could improve the detection rate of paroxysmal AF. Among the study population, 1,658 patients (16.5%) had AF.

RESULTS

Multivariate logistic regression revealed that sex, age, body weight, hypertension, diabetes mellitus, hyperlipidemia, pulse rate at admission, respiratory rate at admission, systolic blood pressure at admission, diastolic blood pressure at admission, National Institute of Health Stroke Scale (NIHSS) score at admission, total cholesterol level, triglyceride level, aspartate transaminase level, and sodium level were major factors associated with AF. CART analysis identified NIHSS score at admission, age, triglyceride level, and aspartate transaminase level as important factors for AF to classify the patients into subgroups.

CONCLUSION

When selecting the high-risk group of patients (with an NIHSS score >12 and age >64.5 years, or with an NIHSS score ≤12, age >71.5 years, and triglyceride level ≤61.5 mg/dL) according to the CART model, the detection rate of paroxysmal AF was approximately double in the acute ischemic stroke patients without AF at baseline.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND AND AIMS

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Stroke occurrence while on antiplatelet therapy may predict atrial fibrillation detected after stroke

BACKGROUND AND AIMS

Stroke occurrence while on antiplatelet therapy, i.e., a breakthrough stroke, is often conveniently attributed to antiplatelet resistance. However, undetected paroxysmal atrial fibrillation (AF) may underlie breakthrough strokes. We hypothesized that a breakthrough stroke may be a clinical marker for patients at risk of having AF detected after stroke (AFDAS).

METHODS

Consecutive patients without known AF hospitalized for ischemic stroke between 2000 and 2013 were identified from nationwide claims data. The independent variable of interest was continued use of antiplatelet therapy within 30 days before stroke. The diagnosis of AF and comorbidities were ascertained using validated algorithms. Stroke severity (National Institutes of Health Stroke Scale [NIHSS]) was estimated using a validated claims-based method. Univariable and multivariable Cox regression analyses were used to determine the effect of breakthrough strokes on the occurrence of AFDAS separately in patients with mild and severe stroke (estimated NIHSS ≤10 versus >10).

RESULTS

Among 17,076 patients (40% female, mean age 69 years), 3314 (19%) were on antiplatelet therapy before stroke. In patients with mild stroke, prior antiplatelet use was significantly associated with the occurrence of AFDAS (adjusted hazards ratio, 1.26; 95% confidence interval, 1.08-1.48). In contrast, no association existed between prior antiplatelet use and the risk of AFDAS in those with severe stroke.

CONCLUSIONS

Patients with a breakthrough stroke of mild severity while on antiplatelet therapy carried an increased risk of AFDAS compared to those not on antiplatelet therapy. Our findings may help prioritize patients for advanced cardiac monitoring in daily practice.

Derivation of a Prediction Rule for Unfavorable Outcome after Ischemic Stroke in the Chinese Population

BACKGROUND

Efficient assessment of patients after ischemic stroke has important reference value for doctors to choose appropriate treatment for patients. Our study aimed to develop a new prognostic model for predicting outcomes 3 months after ischemic stroke among Chinese Population.

METHODS

A prospective observational cohort study among ischemic stroke patients presenting to Emergency Department in the Second Affiliated Hospital of Guangzhou Medical University was conducted from May 2012 to June 2013. Demographic data of ischemic stroke patients, assessment of NIHSS and laboratory results were collected. Based on 3-month modified Rankin Scale (mRS) ischemic stroke patients were divided into either favorable outcome (mRS: 0-2) or unfavorable outcome groups (mRS: 3-6). The variables closely associated with prognosis of ischemic stroke were selected to develop the new prognostic model (NAAP) consisted of 4 parameters: NIHSS, age, atrial fibrillation, and prealbumin. The prognostic value of the modified prognostic model was then compared with NIHSS alone.

RESULTS

A total of 454 patients with suspected stroke were recruited. One hundred eighty-six patients with ischemic stroke were included in the final analysis. A new prognostic model, NAAP was developed. The area under curve (AUC) of NAAP was .861 (95%confidence interval: .803-.907), whilst the AUC of NIHSS was .783 (95%CI: .717-.840), (P = .0048). Decision curve analysis showed that NAAP had a higher net benefit for threshold probabilities of 65% for predictive risk of poor outcomes.

CONCLUSIONS

The modified prognostic model, NAAP may be a better prognostic tool for predicting 3-month unfavorable outcomes for ischemic stroke than NIHSS alone.

A Simple Clinical Risk Score (C2HEST) for Predicting Incident Atrial Fibrillation in Asian Subjects: Derivation in 471,446 Chinese Subjects, With Internal Validation and External Application in 451,199 Korean Subjects

BACKGROUND

The incidence of atrial fibrillation (AF) is increasing, conferring a major health-care issue in Asia. No risk score for predicting incident AF has been specifically developed in Asian subjects. Our aim was to investigate risk factors for incident AF in Asian subjects and to combine them into a simple clinical risk score.

METHODS

Risk factors for incident AF were analyzed in 471,446 subjects from the Chinese Yunnan Insurance Database (internal derivation cohort) and then combined into a simple clinical risk score. External application of the new score was performed in 451,199 subjects from the Korean National Health Insurance Service (external cohort).

RESULTS

In the internal cohort, structural heart disease (SHD), heart failure (HF), age ≥ 75 years, coronary artery disease (CAD), hyperthyroidism, COPD, and hypertension were associated with incident AF. Given the low prevalence and the strong association of SHD with incident AF (hazard ratio, 26.07; 95% CI, 18.22-37.30; P < .001), these patients should be independently considered as high risk for AF and were excluded from the analysis. The remaining predictors were combined into the new simple C₂HEST score: C₂: CAD/COPD (1 point each); H: hypertension (1 point); E: elderly (age ≥ 75 years, 2 points); S: systolic HF (2 points); and T: thyroid disease (hyperthyroidism, 1 point). The C₂HEST score showed good discrimination with the area under the curve (AUC) of 0.75 (95% CI, 0.73-0.77) and had good calibration (P = .774). The score was internally validated by bootstrap sampling procedure, giving an AUC of 0.75 (95% CI, 0.73-0.77). External application gave an AUC of 0.65 (95% CI, 0.65-0.66). The C₂HEST score was superior to CHADS₂ and CHA₂DS₂-VASc scores in both cohorts in predicting incident AF.

CONCLUSIONS

We have developed and validated the C₂HEST score as a simple clinical tool to assess the individual risk of developing AF in the Asian population without SHD.