Literature DB >> 28941601

Refining Prediction of Atrial Fibrillation Risk in the General Population With Analysis of P-Wave Axis (from the Atherosclerosis Risk in Communities Study).

Ankit Maheshwari1, Faye L Norby2, Elsayed Z Soliman3, Ryan Koene4, Mary Rooney2, Wesley T O'Neal5, Alvaro Alonso5, Lin Y Chen4.   

Abstract

Adverse atrial remodeling is associated with increased risk of atrial fibrillation (AF) and can be detected by a shift in P-wave axis. We aimed to determine whether an analysis of P-wave axis can be used to improve risk prediction of AF. We included 15,102 Atherosclerosis Risk in Communities Study participants who were free of AF at baseline. Abnormal P-wave axis (aPWA) was defined as any value outside 0 to 75 degrees on study visit 12-lead electrocardiograms. AF was determined using study visit electrocardiograms, death certificates, and hospital discharge records. Multivariable Cox regression was used to estimate hazard ratios and 95% confidence intervals (CIs) for the association of aPWA with AF. The Cohorts for Heart and Aging Research in Genomic Epidemiology-AF (CHARGE-AF) risk prediction model variables served as our benchmark. Improvement in 10-year AF prediction was assessed by C-statistic, category-based net reclassification improvement, and relative integrated discrimination improvement. During a mean follow-up of 20.2 years, there were 2,618 incident AF cases. aPWA was independently associated with a 2.34-fold (95% CI 2.12 to 2.58) increased risk of AF after adjusting for CHARGE-AF risk score variables. The use of aPWA improved the C-statistic from 0.719 (95% CI 0.702 to 0.736) to 0.722 (95% CI 0.705 to 0.739), which corresponded with a net reclassification improvement of 0.021 (95% CI 0.001, 0.040) and relative integrated discrimination improvement of 0.043 (95% CI 0.018, 0.069). In conclusion, aPWA is independently associated with AF in the general population. The use of this maker modestly improves AF prediction.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2017        PMID: 28941601     DOI: 10.1016/j.amjcard.2017.08.015

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  9 in total

Review 1.  The Atrium in Atrial Fibrillation - A Clinical Review on How to Manage Atrial Fibrotic Substrates.

Authors:  Pedro Silva Cunha; Sérgio Laranjo; Jordi Heijman; Mário Martins Oliveira
Journal:  Front Cardiovasc Med       Date:  2022-07-04

2.  Refining Prediction of Atrial Fibrillation-Related Stroke Using the P2-CHA2DS2-VASc Score.

Authors:  Ankit Maheshwari; Faye L Norby; Nicholas S Roetker; Elsayed Z Soliman; Ryan J Koene; Mary R Rooney; Wesley T O'Neal; Amil M Shah; Brian L Claggett; Scott D Solomon; Alvaro Alonso; Rebecca F Gottesman; Susan R Heckbert; Lin Y Chen
Journal:  Circulation       Date:  2019-01-08       Impact factor: 29.690

Review 3.  Atrial Conduction Disorders.

Authors:  Bryce Alexander; Gary Tse; Manuel Martinez-Selles; Adrian Baranchuk
Journal:  Curr Cardiol Rev       Date:  2021

Review 4.  P Wave Parameters and Indices: A Critical Appraisal of Clinical Utility, Challenges, and Future Research-A Consensus Document Endorsed by the International Society of Electrocardiology and the International Society for Holter and Noninvasive Electrocardiology.

Authors:  Lin Yee Chen; Antonio Luiz Pinho Ribeiro; Pyotr G Platonov; Iwona Cygankiewicz; Elsayed Z Soliman; Bulent Gorenek; Takanori Ikeda; Vassilios P Vassilikos; Jonathan S Steinberg; Niraj Varma; Antoni Bayés-de-Luna; Adrian Baranchuk
Journal:  Circ Arrhythm Electrophysiol       Date:  2022-03-25

5.  Atrial Cardiopathy and Sympatho-Vagal Imbalance in Cryptogenic Stroke: Pathogenic Mechanisms and Effects on Electrocardiographic Markers.

Authors:  Maurizio Acampa; Pietro E Lazzerini; Giuseppe Martini
Journal:  Front Neurol       Date:  2018-06-19       Impact factor: 4.003

6.  Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis.

Authors:  Jelle C L Himmelreich; Lieke Veelers; Wim A M Lucassen; Renate B Schnabel; Michiel Rienstra; Henk C P M van Weert; Ralf E Harskamp
Journal:  Europace       Date:  2020-05-01       Impact factor: 5.214

7.  Long-term impact of new-onset atrial fibrillation complicating acute myocardial infarction on heart failure.

Authors:  Jiachen Luo; Siling Xu; Hongqiang Li; Zhiqiang Li; Baoxin Liu; Xiaoming Qin; Mengmeng Gong; Beibei Shi; Yidong Wei
Journal:  ESC Heart Fail       Date:  2020-06-23

8.  CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening.

Authors:  Jelle C L Himmelreich; Wim A M Lucassen; Ralf E Harskamp; Claire Aussems; Henk C P M van Weert; Mark M J Nielen
Journal:  Open Heart       Date:  2021-01

9.  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

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.