Literature DB >> 30103243

Risk Score for Prediction of 10-Year Atrial Fibrillation: A Community-Based Study.

Doron Aronson1,2, Varda Shalev3, Rachel Katz3, Gabriel Chodick3, Diab Mutlak1,2.   

Abstract

PURPOSE: We used a large real-world data from community settings to develop and validate a 10-year risk score for new-onset atrial fibrillation (AF) and calculate its net benefit performance.
METHODS: Multivariable Cox proportional hazards model was used to estimate effects of risk factors in the derivation cohort (n = 96,778) and to derive a risk equation. Measures of calibration and discrimination were calculated in the validation cohort (n = 48,404).
RESULTS: Cumulative AF incidence rates for both the derivation and validation cohorts were 5.8% at 10 years. The final models included the following variables: age, sex, body mass index, history of treated hypertension, systolic blood pressure ≥ 160 mm Hg, chronic lung disease, history of myocardial infarction, history of peripheral arterial disease, heart failure and history of an inflammatory disease. There was a 27-fold difference (1.0% vs. 27.2%) in AF risk between the lowest (-1) and the highest (9) sum score. The c-statistic was 0.743 (95% confidence interval [CI], 0.737-0.749) for the derivation cohort and 0.749 (95% CI, 0.741-0.759) in the validation cohort. The risk equation was well calibrated, with predicted risks closely matching observed risks. Decision curve analysis displayed consistent positive net benefit of using the AF risk score for decision thresholds between 1 and 25% 10-year AF risk.
CONCLUSION: We provide a simple score for the prediction of 10-year risk for AF. The score can be used to select patients at highest risk for treatments of modifiable risk factors, monitoring for sub-clinical AF detection or for clinical trials of primary prevention of AF. Georg Thieme Verlag KG Stuttgart · New York.

Entities:  

Mesh:

Year:  2018        PMID: 30103243     DOI: 10.1055/s-0038-1668522

Source DB:  PubMed          Journal:  Thromb Haemost        ISSN: 0340-6245            Impact factor:   5.249


  11 in total

1.  Performance of Atrial Fibrillation Risk Prediction Models in Over 4 Million Individuals.

Authors:  Shaan Khurshid; Uri Kartoun; Jeffrey M Ashburner; Ludovic Trinquart; Anthony Philippakis; Amit V Khera; Patrick T Ellinor; Kenney Ng; Steven A Lubitz
Journal:  Circ Arrhythm Electrophysiol       Date:  2020-12-09

Review 2.  Atrial Fibrillation in Older People: Concepts and Controversies.

Authors:  Zafraan Zathar; Anne Karunatilleke; Ameenathul M Fawzy; Gregory Y H Lip
Journal:  Front Med (Lausanne)       Date:  2019-08-08

3.  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

4.  Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED).

Authors:  Randall W Grout; Siu L Hui; Timothy D Imler; Sarah El-Azab; Jarod Baker; George H Sands; Mohammad Ateya; Francis Pike
Journal:  BMC Med Inform Decis Mak       Date:  2021-04-03       Impact factor: 2.796

5.  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

6.  Atrial Fibrillation in Patients With Cardiomyopathy: Prevalence and Clinical Outcomes From Real-World Data.

Authors:  Benjamin J R Buckley; Stephanie L Harrison; Dhiraj Gupta; Elnara Fazio-Eynullayeva; Paula Underhill; Gregory Y H Lip
Journal:  J Am Heart Assoc       Date:  2021-11-15       Impact factor: 5.501

7.  Development and Validation of 3-Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models.

Authors:  Yunjin Yum; Seung Yong Shin; Hakje Yoo; Yong Hyun Kim; Eung Ju Kim; Gregory Y H Lip; Hyung Joon Joo
Journal:  J Am Heart Assoc       Date:  2022-06-14       Impact factor: 6.106

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 of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis.

Authors:  Ramesh Nadarajah; Eman Alsaeed; Ben Hurdus; Suleman Aktaa; David Hogg; Matthew G D Bates; Campbel Cowan; Jianhua Wu; Chris P Gale
Journal:  Heart       Date:  2022-06-10       Impact factor: 7.365

10.  Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence.

Authors:  Ramesh Nadarajah; Jianhua Wu; Alejandro F Frangi; David Hogg; Campbell Cowan; Chris Gale
Journal:  BMJ Open       Date:  2021-11-02       Impact factor: 2.692

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.