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

AIMS: Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing effic...

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Autores principales: Himmelreich, Jelle C L, Veelers, Lieke, Lucassen, Wim A M, Schnabel, Renate B, Rienstra, Michiel, van Weert, Henk C P M, Harskamp, Ralf E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526764/
https://www.ncbi.nlm.nih.gov/pubmed/32011689
http://dx.doi.org/10.1093/europace/euaa005
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author Himmelreich, Jelle C L
Veelers, Lieke
Lucassen, Wim A M
Schnabel, Renate B
Rienstra, Michiel
van Weert, Henk C P M
Harskamp, Ralf E
author_facet Himmelreich, Jelle C L
Veelers, Lieke
Lucassen, Wim A M
Schnabel, Renate B
Rienstra, Michiel
van Weert, Henk C P M
Harskamp, Ralf E
author_sort Himmelreich, Jelle C L
collection PubMed
description AIMS: Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing efficiency of future primary AF screening efforts. METHODS AND RESULTS: We performed a systematic review on multivariable models derived, validated, and/or augmented for AF prediction in community cohorts using Pubmed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) through 1 August 2019. We performed meta-analysis of model discrimination with the summary C-statistic as the primary expression of associations using a random effects model. In case of high heterogeneity, we calculated a 95% prediction interval. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist for risk of bias assessment. We included 27 studies with a total of 2 978 659 unique participants among 20 cohorts with mean age ranging from 42 to 76 years. We identified 21 risk models used for incident AF risk in community cohorts. Three models showed significant summary discrimination despite high heterogeneity: CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [summary C-statistic 0.71; 95% confidence interval (95% CI) 0.66–0.76], FHS-AF (Framingham Heart Study risk score for AF) (summary C-statistic 0.70; 95% CI 0.64–0.76), and CHA(2)DS(2)-VASc (summary C-statistic 0.69; 95% CI 0.64–0.74). Of these, CHARGE-AF and FHS-AF had originally been derived for AF incidence prediction. Only CHARGE-AF, which comprises easily obtainable measurements and medical history elements, showed significant summary discrimination among cohorts that had applied a uniform (5-year) risk prediction window. CONCLUSION: CHARGE-AF appeared most suitable for primary screening purposes in terms of performance and applicability in older community cohorts of predominantly European descent.
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spelling pubmed-75267642020-10-05 Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis Himmelreich, Jelle C L Veelers, Lieke Lucassen, Wim A M Schnabel, Renate B Rienstra, Michiel van Weert, Henk C P M Harskamp, Ralf E Europace Clinical Research AIMS: Atrial fibrillation (AF) is a common arrhythmia associated with an increased stroke risk. The use of multivariable prediction models could result in more efficient primary AF screening by selecting at-risk individuals. We aimed to determine which model may be best suitable for increasing efficiency of future primary AF screening efforts. METHODS AND RESULTS: We performed a systematic review on multivariable models derived, validated, and/or augmented for AF prediction in community cohorts using Pubmed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) through 1 August 2019. We performed meta-analysis of model discrimination with the summary C-statistic as the primary expression of associations using a random effects model. In case of high heterogeneity, we calculated a 95% prediction interval. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) checklist for risk of bias assessment. We included 27 studies with a total of 2 978 659 unique participants among 20 cohorts with mean age ranging from 42 to 76 years. We identified 21 risk models used for incident AF risk in community cohorts. Three models showed significant summary discrimination despite high heterogeneity: CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology) [summary C-statistic 0.71; 95% confidence interval (95% CI) 0.66–0.76], FHS-AF (Framingham Heart Study risk score for AF) (summary C-statistic 0.70; 95% CI 0.64–0.76), and CHA(2)DS(2)-VASc (summary C-statistic 0.69; 95% CI 0.64–0.74). Of these, CHARGE-AF and FHS-AF had originally been derived for AF incidence prediction. Only CHARGE-AF, which comprises easily obtainable measurements and medical history elements, showed significant summary discrimination among cohorts that had applied a uniform (5-year) risk prediction window. CONCLUSION: CHARGE-AF appeared most suitable for primary screening purposes in terms of performance and applicability in older community cohorts of predominantly European descent. Oxford University Press 2020-05 2020-02-03 /pmc/articles/PMC7526764/ /pubmed/32011689 http://dx.doi.org/10.1093/europace/euaa005 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Clinical Research
Himmelreich, Jelle C L
Veelers, Lieke
Lucassen, Wim A M
Schnabel, Renate B
Rienstra, Michiel
van Weert, Henk C P M
Harskamp, Ralf E
Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis
title Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis
title_full Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis
title_fullStr Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis
title_full_unstemmed Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis
title_short Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis
title_sort prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis
topic Clinical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526764/
https://www.ncbi.nlm.nih.gov/pubmed/32011689
http://dx.doi.org/10.1093/europace/euaa005
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