Literature DB >> 30064946

Simple risk model and score for predicting of incident atrial fibrillation in Japanese.

Richiro Hamada1, Shigeki Muto2.   

Abstract

BACKGROUND: Investigating regarding a predicted risk score of incident atrial fibrillation (AF) for an Asian general population has not been enough. Whether addition of electrocardiogram (ECG) variables to risk factors improves prediction of incident AF is unclear in a context that ECGs are extensively used at medical check-ups and outpatient clinics in Japan.
METHODS: Participants undergoing periodic health check-ups during 2008-2014 followed-up by December 2015 including 96,841 (65.1% male) aged 40-79 years were pooled to derive prediction models and risk scores for incident AF. Multivariable Cox regression identified clinical risk factors associated with incident AF in 7 years among 65,984 eligible participants including 349 AF cases.
RESULTS: A 7-year prediction model ("Simple-model") including the variables of age, waist circumference, diastolic blood pressure, alcohol consumption, heart rate, and cardiac murmur, had good discrimination (C-statistic, 0.77), requiring no blood sampling. Addition model of the ECGs variables ("Added-model") including left ventricular hypertrophy, atrial enlargement, atrial premature contraction, and ventricular premature contraction, improved significantly the overall model discrimination (C-statistic, 0.78; categorical net reclassification improvement, 0.063; 95%CI, 0.031-0.099). The risk scores derived from the two models respectively showed an approximation of the observed and predicted probability for each score. Participants with score ≤4 or ≥9 points had, respectively, ≤1% and ≥5% predicted probability of incident AF in 7 years. The receiver-operating characteristics curve for the risk score of the added-model was significantly higher than the simple-model (0.769 vs 0.753, p<0.001). Atrial enlargement on ECG and the highest age group were the highest risk points of the significant predictors.
CONCLUSIONS: We developed 7-year risk scores for incident AF using usually available clinical factors including ECGs in primary care. These risk scores could identify individuals with high risk of incident AF at health check-up and outpatient clinics.
Copyright © 2018 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Atrial fibrillation; Epidemiology; Risk factors; Risk prediction; Risk score

Year:  2018        PMID: 30064946     DOI: 10.1016/j.jjcc.2018.06.005

Source DB:  PubMed          Journal:  J Cardiol        ISSN: 0914-5087            Impact factor:   3.159


  10 in total

1.  Risk-Based Disease Surveillance: The Promise of Early Atrial Fibrillation Identification.

Authors:  Marcie G Berger; David Gutterman
Journal:  Chest       Date:  2019-03       Impact factor: 9.410

2.  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

3.  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

4.  Risk prediction for new-onset atrial fibrillation using the Minnesota code electrocardiography classification system.

Authors:  Yu Igarashi; Kotaro Nochioka; Yasuhiko Sakata; Tokiwa Tamai; Shinya Ohkouchi; Toshiya Irokawa; Hiromasa Ogawa; Hideka Hayashi; Takahide Fujihashi; Shinsuke Yamanaka; Takashi Shiroto; Satoshi Miyata; Jun Hata; Shogo Yamada; Toshiharu Ninomiya; Satoshi Yasuda; Hajime Kurosawa; Hiroaki Shimokawa
Journal:  Int J Cardiol Heart Vasc       Date:  2021-03-31

5.  Physical activity and risk of atrial fibrillation in the general population: meta-analysis of 23 cohort studies involving about 2 million participants.

Authors:  Setor K Kunutsor; Samuel Seidu; Timo H Mäkikallio; Richard S Dey; Jari A Laukkanen
Journal:  Eur J Epidemiol       Date:  2021-01-25       Impact factor: 8.082

6.  Utility of risk prediction models to detect atrial fibrillation in screened participants.

Authors:  Michiel H F Poorthuis; Nicholas R Jones; Paul Sherliker; Rachel Clack; Gert J de Borst; Robert Clarke; Sarah Lewington; Alison Halliday; Richard Bulbulia
Journal:  Eur J Prev Cardiol       Date:  2021-05-22       Impact factor: 8.526

Review 7.  Is machine learning the future for atrial fibrillation screening?

Authors:  Pavidra Sivanandarajah; Huiyi Wu; Nikesh Bajaj; Sadia Khan; Fu Siong Ng
Journal:  Cardiovasc Digit Health J       Date:  2022-05-16

8.  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

9.  Prediction model for thyrotoxic atrial fibrillation: a retrospective study.

Authors:  Daria Aleksandrovna Ponomartseva; Ilia Vladislavovich Derevitskii; Sergey Valerevich Kovalchuk; Alina Yurevna Babenko
Journal:  BMC Endocr Disord       Date:  2021-07-11       Impact factor: 2.763

Review 10.  Evidence and Challenges in Left Atrial Appendage Management.

Authors:  Taira Yamamoto; Daisuke Endo; Satoshi Matsushita; Akie Shimada; Keisuke Nakanishi; Tohru Asai; Atsushi Amano
Journal:  Ann Thorac Cardiovasc Surg       Date:  2021-07-31       Impact factor: 1.520

  10 in total

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