Refinement of detecting atrial fibrillation in stroke patients: results from the <scp>TRACK</scp>‐<scp>AF</scp> Study

Reinke, Florian; Bettin, Markus; Ross, Linda Sarah; Kochhäuser, Simon; Kleffner, Ilka; Ritter, Martin; Minnerup, Jens; Dechering, Dirk G.; Eckardt, Lars; Dittrich, Ralf

doi:10.1111/ene.13538

Cited by 18 publications

(21 citation statements)

References 21 publications

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“…The mean/median elapsed time from IS/TIA onset to cardiac monitoring implantation ranged from 3 to 174 days, while the mean/median ICM duration ranged from 180 to 1,080 days (Table 1). Included studies were generally found to have a low risk of bias (Supplementary Table 3), except in cases not clearly stating consecutive enrollment of patients [23,26,27,39,44-47], exclusion of AF with electrocardiogram or short-term non-invasive Holter monitoring prior to ICM impantation [21,33], or no adjudication of ICM recordings by experienced cardiologists [26-29,33,41,46,47].…”

Section: Resultsmentioning

confidence: 99%

Duration of Implantable Cardiac Monitoring and Detection of Atrial Fibrillation in Ischemic Stroke Patients: A Systematic Review and Meta-Analysis

et al. 2019

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Background and PurposeCurrent guidelines do not provide firm directions on atrial fibrillation (AF) screening after ischemic stroke (IS). We sought to investigate the association of implantable cardiac monitoring (ICM) duration with the yield of AF detection in IS patients. MethodsWe included studies reporting AF detection rates by ICM in IS patients with negative initial AF screening. We excluded studies reporting prolonged cardiac monitoring with devices other than ICM, not providing AF detection rates or monitoring duration, and reporting overlapping data for the same population. The random-effects model was used for all pooled estimates and meta-regression analyses. ResultsWe included 28 studies (4,531 patients, mean age 65 years). In meta-regression analyses, the proportion of AF detection by ICM was independently associated with monitoring duration (coefficient=0.015; 95% confidence interval [CI], 0.005 to 0.024) and mean patient age (coefficient=0.009; 95% CI, 0.003 to 0.015). No associations were detected with other patient characteristics, including IS subtype (cryptogenic vs. embolic stroke of undetermined source) or time from IS onset to CM implantation. In subgroup analyses, significant differences (P<0.001) in the AF detection rates were found for ICM duration (<6 months: 5% [95% CI, 3% to 6%]; ≥6 and ≤12 months: 21% [95% CI, 16% to 25%]; >12 and ≤24 months: 26% [95% CI, 22% to 31%]; >24 months: 34% [95% CI, 29% to 39%]). ConclusionsExtended duration of ICM monitoring and increased patient age are factors that substantially increase AF detection in IS patients with initial negative AF screening.

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Section: Resultsmentioning

confidence: 99%

Duration of Implantable Cardiac Monitoring and Detection of Atrial Fibrillation in Ischemic Stroke Patients: A Systematic Review and Meta-Analysis

et al. 2019

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“…Other AF detection algorithms have already been tested and seem to be useful, but in our opinion need improvements. 4,[33][34][35] The detection methods for some of these biomarkers are already available and they can potentially be used routinely in the clinical assessment of cryptogenic stroke and TIA patients. 22 The results from our pilot study indicate that multiple proteins may potentially be used as biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Atrial fibrillation in cryptogenic stroke and transient ischaemic attack – The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study: Rationale and design

Ratajczak-Tretel

Lambert

Johansen

et al. 2019

European Stroke Journal

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“…8 Recently, the same AA was also used in patients with cryptogenic stroke to select those who may benefit from prolonged cardiac monitoring afforded by an implantable device. 32 In a comparable approach, a recent study demonstrated that application of an artificial intelligence algorithm on 12-lead ECG in sinus rhythm was able to select patients at high risk for developing AF and might thereby not only decrease the diagnostic workload of ECG analysis but also increase the diagnostic yield of AF. 33 Moreover our data support a more intensified screening for pAF in patients with increased age, previous administration of intravenous thrombolysis or more severe stroke symptoms at admission, as measured by the NIHSS.…”

Section: Discussionmentioning

confidence: 99%

Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke

Gröschel

Lange

Wasser

et al. 2020

Ann Clin Transl Neurol

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Objective: Identification of ischemic stroke patients at high risk for paroxysmal atrial fibrillation (pAF) during 72 hours Holter ECG might be useful to individualize the allocation of prolonged ECG monitoring times, currently not routinely applied in clinical practice. Methods: In a prospective multicenter study, the first analysable hour of raw ECG data from prolonged 72 hours Holter ECG monitoring in 1031 patients with acute ischemic stroke/TIA presenting in sinus rhythm was classified by an automated software (AA) into "no risk of AF" or "risk of AF" and compared to clinical variables to predict AF during 72 hours Holter-ECG. Results: pAF was diagnosed in 54 patients (5.2%; mean age: 78 years; female 56%) and was more frequently detected after 72 hours in patients classified by AA as "risk of AF" (n = 21, 17.8%) compared to "no risk of AF" (n = 33, 3.6%). AA-based risk stratification as "risk of AF" remained in the prediction model for pAF detection during 72 hours Holter ECG (OR3.814, 95% CI 2.024-7.816, P < 0.001), in addition to age (OR1.052, 95% CI 1.021-1.084, P = 0.001), NIHSS (OR 1.087, 95% CI 1.023-1.154, P = 0.007) and prior treatment with thrombolysis (OR2.639, 95% CI 1.313-5.306, P = 0.006). Similarly, risk stratification by AA significantly increased the area under the receiver operating characteristic curve (AUC) for prediction of pAF detection compared to a purely clinical risk score (AS5F alone: AUC 0.751; 95% CI 0.724-0.778; AUC for the combination: 0.789, 95% CI 0.763-0.814; difference between the AUC P = 0.022). Interpretation: Automated software-based ECG risk stratification selects patients with high risk of AF during 72 hours Holter ECG and adds predictive value to common clinical risk factors for AF prediction.

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Refinement of detecting atrial fibrillation in stroke patients: results from the TRACK‐AF Study

Cited by 18 publications

References 21 publications

Duration of Implantable Cardiac Monitoring and Detection of Atrial Fibrillation in Ischemic Stroke Patients: A Systematic Review and Meta-Analysis

Duration of Implantable Cardiac Monitoring and Detection of Atrial Fibrillation in Ischemic Stroke Patients: A Systematic Review and Meta-Analysis

Atrial fibrillation in cryptogenic stroke and transient ischaemic attack – The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study: Rationale and design

Software‐based analysis of 1‐hour Holter ECG to select for prolonged ECG monitoring after stroke

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