Literature DB >> 33673209

Clinical Factors Associated with Atrial Fibrillation Detection on Single-Time Point Screening Using a Hand-Held Single-Lead ECG Device.

Giuseppe Boriani1, Pietro Palmisano2, Vincenzo Livio Malavasi1, Elisa Fantecchi1, Marco Vitolo1,3, Niccolo' Bonini1, Jacopo F Imberti1, Anna Chiara Valenti1, Renate B Schnabel4, Ben Freedman5.   

Abstract

Our aim was to assess the prevalence of unknown atrial fibrillation (AF) among adults during single-time point rhythm screening performed during meetings or social recreational activities organized by patient groups or volunteers. A total of 2814 subjects (median age 68 years) underwent AF screening by a handheld single-lead ECG device (MyDiagnostick). Overall, 56 subjects (2.0%) were diagnosed with AF, as a result of 12-lead ECG following a positive/suspected recording. Screening identified AF in 2.9% of the subjects ≥ 65 years. None of the 265 subjects aged below 50 years was found positive at AF screening. Risk stratification for unknown AF based on a CHA2DS2VASc > 0 in males and >1 in females (or CHA2DS2VA > 0) had a high sensitivity (98.2%) and a high negative predictive value (99.8%) for AF detection. A slightly lower sensitivity (96.4%) was achieved by using age ≥ 65 years as a risk stratifier. Conversely, raising the threshold at ≥75 years showed a low sensitivity. Within the subset of subjects aged ≥ 65 a CHA2DS2VASc > 1 in males and >2 in females, or a CHA2DS2VA > 1 had a high sensitivity (94.4%) and negative predictive value (99.3%), while age ≥ 75 was associated with a marked drop in sensitivity for AF detection.

Entities:  

Keywords:  age; atrial fibrillation; risk stratification; stroke

Year:  2021        PMID: 33673209     DOI: 10.3390/jcm10040729

Source DB:  PubMed          Journal:  J Clin Med        ISSN: 2077-0383            Impact factor:   4.241


  5 in total

Review 1.  Optimizing indices of atrial fibrillation susceptibility and burden to evaluate atrial fibrillation severity, risk and outcomes.

Authors:  Giuseppe Boriani; Marco Vitolo; Igor Diemberger; Marco Proietti; Anna Chiara Valenti; Vincenzo Livio Malavasi; Gregory Y H Lip
Journal:  Cardiovasc Res       Date:  2021-06-16       Impact factor: 13.081

2.  Validating risk models versus age alone for atrial fibrillation in a young Dutch population cohort: should atrial fibrillation risk prediction be expanded to younger community members?

Authors:  Jelle C L Himmelreich; Ralf E Harskamp; Bastiaan Geelhoed; Saverio Virdone; Wim A M Lucassen; Ron T Gansevoort; Michiel Rienstra
Journal:  BMJ Open       Date:  2022-02-16       Impact factor: 2.692

3.  Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection.

Authors:  Szymon Buś; Konrad Jędrzejewski; Przemysław Guzik
Journal:  J Clin Med       Date:  2022-07-11       Impact factor: 4.964

4.  Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection.

Authors:  Szymon Buś; Konrad Jędrzejewski; Przemysław Guzik
Journal:  J Clin Med       Date:  2022-09-27       Impact factor: 4.964

5.  Predicting Silent Atrial Fibrillation in the Elderly: A Report from the NOMED-AF Cross-Sectional Study.

Authors:  Katarzyna Mitrega; Gregory Y H Lip; Beata Sredniawa; Adam Sokal; Witold Streb; Karol Przyludzki; Tomasz Zdrojewski; Lukasz Wierucki; Marcin Rutkowski; Piotr Bandosz; Jaroslaw Kazmierczak; Tomasz Grodzicki; Grzegorz Opolski; Zbigniew Kalarus
Journal:  J Clin Med       Date:  2021-05-26       Impact factor: 4.241

  5 in total

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