Detection of Paroxysmal Atrial Fibrillation in Acute Stroke Patients

Rizos, Timolaos; Rasch, Coen R. N.; Jenetzky, Ekkehart; Hametner, Christian; Kathoefer, S.; Reinhardt, Roland; Hepp, Thomas; Hacke, Werner; Veltkamp, Roland

doi:10.1159/000316885

Cited by 68 publications

(72 citation statements)

References 64 publications

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Section: Patient Selection and Duration Of Monitoringmentioning

confidence: 99%

“…42 In comparison with standard telemetry, monitors with software algorithms for automatic detection of AF (5.4% versus 7.7%) 45 or a systematic analysis of saved data from recordings (2.6× increase) seemed to improve detection rates. 25 Higher detection rates were also seen in the selected group (8 studies; Figure 3) compared with the unselected group (6 studies; Figure 2) using 24-hour Holter monitoring.…”

Section: Patient Selection and Duration Of Monitoringmentioning

confidence: 99%

“…[35][36][37][38][39][40][41][42][43][44][45][46][47] Some studies used multiple interventions for detecting new AF. 39,41,42,44,45 PAF was defined in 47% of studies, most frequently as >30 seconds of duration of AF (64%). …”

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confidence: 99%

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Detection of Atrial Fibrillation After Ischemic Stroke or Transient Ischemic Attack

Kishore

Vail

Majid

et al. 2014

Stroke

345

208

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C ardioembolism accounts for 17% to 30% of all ischemic strokes.1,2 Some data suggest that >50% of these are because of atrial fibrillation (AF).3 Paroxysmal AF (PAF) is often undetected because characteristics such as short duration, episodic, and frequently asymptomatic nature make it challenging to diagnose at the bedside, leading to suboptimal secondary prevention. 4 It is likely that a proportion of strokes labeled as cryptogenic are cardioembolic in origin because of occult AF. 5,6 Furthermore, ≥2 factors contributing to stroke risk may coexist: even patients with an identified risk factor for nonembolic stroke may have occult cardioembolism.Detection rate of new AF from a standard 12-lead ECG after ischemic stroke/transient ischemic attack (TIA) is ≈2% to 5% 7,8 and from 24-hour Holter is 2% to 6%. [9][10][11] The European Stroke Organization 12 and the American Heart Association (AHA)/ American Stroke Association 13 recommend that 24-hour Holter monitoring is used to detect occult AF/PAF when suspected, and no other cause for stroke is found. However, the optimum timing, duration, setting (outpatient or inpatient), and method of monitoring to maximize the detection of PAF after stroke/ TIA are unclear. Furthermore, diagnostic criteria used for PAF during monitoring may vary and have implications for risk of recurrence. We therefore undertook a systematic review and meta-analysis with the following objectives:To determine the overall rate of detection of any new AF with cardiac monitoring (invasive and noninvasive) after ischemic stroke/TIA. To evaluate detection rates of AF in selected versus unselected patients with stroke/TIA. To explore the influence of duration of monitoring on detection rates of AF.Background and Purpose-Atrial fibrillation (AF) confers a high risk of recurrent stroke, although detection methods and definitions of paroxysmal AF during screening vary. We therefore undertook a systematic review and meta-analysis to determine the frequency of newly detected AF using noninvasive or invasive cardiac monitoring after ischemic stroke or transient ischemic attack. Methods-Prospective observational studies or randomized controlled trials of patients with ischemic stroke, transient ischemic attack, or both, who underwent any cardiac monitoring for a minimum of 12 hours, were included after electronic searches of multiple databases. The primary outcome was detection of any new AF during the monitoring period. We prespecified subgroup analysis of selected (prescreened or cryptogenic) versus unselected patients and according to duration of monitoring. Results-A total of 32 studies were analyzed. The overall detection rate of any AF was 11.5% (95% confidence interval, 8.9%-14.3%), although the timing, duration, method of monitoring, and reporting of diagnostic criteria used for paroxysmal AF varied. Detection rates were higher in selected (13.4%; 95% confidence interval, 9.0%-18.4%) than in unselected patients (6.2%; 95% confidence interval, 4.4%-8.3%). There was substantial heterogeneity ev...

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Section: Patient Selection and Duration Of Monitoringmentioning

confidence: 99%

Section: Patient Selection and Duration Of Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Atrial Fibrillation After Ischemic Stroke or Transient Ischemic Attack

Kishore

Vail

Majid

et al. 2014

Stroke

345

208

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“…10 Besides its main original goal to detect life-threatening arrhythmia, it was suggested that cardiac telemetry facilitates the diagnosis of paroxysmal AF, too. [15][16][17][18] However, the superiority of continuous monitoring for AF detection over serial ECG assessments was questioned by a study on 150 consecutive patients admitted to a Canadian stroke unit. 11 On average, 2.4 ECGs were conducted during inpatient evaluation.…”

Section: Serial Ecg Assessments Versus Continuous Monitoringmentioning

confidence: 99%

“…Previous investigations, however, indicate that 24-hour Holter ECG has low impact for AF detection during the acute phase of ischemic stroke 12 and that it is clearly inferior to routine stroke unit monitoring. 17 Although Holter recordings have been conducted in clinical routine in a subset of our study cohort, the data did not allow a systematic head-to-head comparison of 24-hour Holter ECG and SEA-AF. Because all steps of SEA-AF were performed and analyzed together, we cannot provide data on performance and value of single steps within the algorithm.…”

Section: Limitationsmentioning

confidence: 99%

A Structured Reading Algorithm Improves Telemetric Detection of Atrial Fibrillation After Acute Ischemic Stroke

Kallmünzer¹,

Breuer²,

Hering³

et al. 2012

Stroke

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Background and Purpose-Anticoagulation is a highly effective secondary prevention in patients with cardioembolic stroke and atrial fibrillation/flutter (AF). However, the condition remains underdiagnosed, because paroxysmal AF may be missed by diagnostic tests in the acute phase. In this study, the sensitivity of AF detection was assessed for serial electrocardiographic recordings and continuous stroke unit telemetric monitoring with or without a structured algorithm to analyze telemetric data (SEA-AF). Methods-Three hundred forty-six consecutive patients with acute ischemic stroke were prospectively included and subjected to standard telemetric monitoring. In addition, telemetric data were separately analyzed following SEA-AF, consisting of a structured evaluation of episodes with high risk for AF and a chronological beat-to-beat screening of the full registration. Serial electrocardiograms were conducted in 24-hour intervals. Results-Median effective telemetry monitoring time was 75.5 hours (interquartile range 64 -86 hours). Overall, AF was diagnosed in 119 of 346 patients (34.4%). The structured reading algorithm was the most sensitive method to detected AF. Conventional telemetry and serial electrocardiographic assessments were less effective. However, only 35% of patients with previously documented paroxysmal AF and negative baseline electrocardiogram demonstrated AF episodes during monitoring. Conclusions-Continuous stroke unit telemetry using SEA-AF shows a significantly higher detection rate for AF compared with daily electrocardiographic assessments and standard telemetry without structured reading. The low overall probability to detect paroxysmal AF with either method during the first days after stroke demonstrates the urgent need for complementary diagnostic strategies such as long-term monitoring and frequent follow-up assessments. Clinical Trial Registration-URL: www.clinicaltrials.gov. Unique identifier: NCT01177748.(Stroke. 2012;43:994-999.)

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Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation

Yuan,

Duffy,

Dhruva

et al. 2023

JAMA Cardiol

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ImportanceEarly detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases.ObjectiveTo determine whether deep learning models applied to outpatient ECGs in sinus rhythm can predict AF in a large and diverse patient population.Design, Setting, and ParticipantsThis prognostic study was performed on ECGs acquired from January 1, 1987, to December 31, 2022, at 6 US Veterans Affairs (VA) hospital networks and 1 large non-VA academic medical center. Participants included all outpatients with 12-lead ECGs in sinus rhythm.Main Outcomes and MeasuresA convolutional neural network using 12-lead ECGs from 2 US VA hospital networks was trained to predict the presence of AF within 31 days of sinus rhythm ECGs. The model was tested on ECGs held out from training at the 2 VA networks as well as 4 additional VA networks and 1 large non-VA academic medical center.ResultsA total of 907 858 ECGs from patients across 6 VA sites were included in the analysis. These patients had a mean (SD) age of 62.4 (13.5) years, 6.4% were female, and 93.6% were male, with a mean (SD) CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) score of 1.9 (1.6). A total of 0.2% were American Indian or Alaska Native, 2.7% were Asian, 10.7% were Black, 4.6% were Latinx, 0.7% were Native Hawaiian or Other Pacific Islander, 62.4% were White, 0.4% were of other race or ethnicity (which is not broken down into subcategories in the VA data set), and 18.4% were of unknown race or ethnicity. At the non-VA academic medical center (72 483 ECGs), the mean (SD) age was 59.5 (15.4) years and 52.5% were female, with a mean (SD) CHA2DS2-VASc score of 1.6 (1.4). A total of 0.1% were American Indian or Alaska Native, 7.9% were Asian, 9.4% were Black, 2.9% were Latinx, 0.03% were Native Hawaiian or Other Pacific Islander, 74.8% were White, 0.1% were of other race or ethnicity, and 4.7% were of unknown race or ethnicity. A deep learning model predicted the presence of AF within 31 days of a sinus rhythm ECG on held-out test ECGs at VA sites with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.85-0.86), accuracy of 0.78 (95% CI, 0.77-0.78), and F1 score of 0.30 (95% CI, 0.30-0.31). At the non-VA site, AUROC was 0.93 (95% CI, 0.93-0.94); accuracy, 0.87 (95% CI, 0.86-0.88); and F1 score, 0.46 (95% CI, 0.44-0.48). The model was well calibrated, with a Brier score of 0.02 across all sites. Among individuals deemed high risk by deep learning, the number needed to screen to detect a positive case of AF was 2.47 individuals for a testing sensitivity of 25% and 11.48 for 75%. Model performance was similar in patients who were Black, female, or younger than 65 years or who had CHA2DS2-VASc scores of 2 or greater.Conclusions and RelevanceDeep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.

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Detection of Paroxysmal Atrial Fibrillation in Acute Stroke Patients

Cited by 68 publications

References 64 publications

Detection of Atrial Fibrillation After Ischemic Stroke or Transient Ischemic Attack

Detection of Atrial Fibrillation After Ischemic Stroke or Transient Ischemic Attack

A Structured Reading Algorithm Improves Telemetric Detection of Atrial Fibrillation After Acute Ischemic Stroke

Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation

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