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Another piece in the puzzle of atrial fibrillation risk: clinical, genetic, and electrocardiogram-based artificial intelligence.

作者信息

Kany Shinwan, Ellinor Patrick T, Khurshid Shaan

机构信息

Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany.

Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA.

出版信息

Eur Heart J. 2024 Dec 7;45(46):4935-4937. doi: 10.1093/eurheartj/ehae691.

DOI:10.1093/eurheartj/ehae691
PMID:39495215
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11631109/
Abstract
摘要

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本文引用的文献

1
Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores.使用深度学习、临床模型和多基因评分预测房颤发病情况。
Eur Heart J. 2024 Dec 7;45(46):4920-4934. doi: 10.1093/eurheartj/ehae595.
2
Cross-ancestry genome-wide analysis of atrial fibrillation unveils disease biology and enables cardioembolic risk prediction.跨种族全基因组分析心房颤动揭示疾病生物学并实现心源性栓塞风险预测。
Nat Genet. 2023 Feb;55(2):187-197. doi: 10.1038/s41588-022-01284-9. Epub 2023 Jan 19.
3
A polygenic risk score predicts atrial fibrillation in cardiovascular disease.
多基因风险评分可预测心血管疾病中的心房颤动。
Eur Heart J. 2023 Jan 14;44(3):221-231. doi: 10.1093/eurheartj/ehac460.
4
ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.基于心电图的深度学习与临床危险因素预测心房颤动
Circulation. 2022 Jan 11;145(2):122-133. doi: 10.1161/CIRCULATIONAHA.121.057480. Epub 2021 Nov 8.
5
Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation.临床与遗传风险模型预测心房颤动的准确性。
Circ Genom Precis Med. 2021 Oct;14(5):e003355. doi: 10.1161/CIRCGEN.121.003355. Epub 2021 Aug 31.
6
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.深度神经网络可通过 12 导联心电图预测新发心房颤动，并有助于识别心房颤动相关卒中风险。
Circulation. 2021 Mar 30;143(13):1287-1298. doi: 10.1161/CIRCULATIONAHA.120.047829. Epub 2021 Feb 16.
7
Performance of Atrial Fibrillation Risk Prediction Models in Over 4 Million Individuals.超过 400 万人的房颤风险预测模型表现。
Circ Arrhythm Electrophysiol. 2021 Jan;14(1):e008997. doi: 10.1161/CIRCEP.120.008997. Epub 2020 Dec 9.
8
Early Rhythm-Control Therapy in Patients with Atrial Fibrillation.心房颤动患者的早期节律控制治疗。
N Engl J Med. 2020 Oct 1;383(14):1305-1316. doi: 10.1056/NEJMoa2019422. Epub 2020 Aug 29.
9
Prediction of new-onset atrial fibrillation for general population in Asia: A comparison of C2HEST and HATCH scores.亚洲普通人群新发房颤的预测：C2HEST评分与HATCH评分的比较
Int J Cardiol. 2020 Aug 15;313:60-63. doi: 10.1016/j.ijcard.2020.03.036. Epub 2020 Mar 17.
10
Development and Validation of a Prediction Model for Atrial Fibrillation Using Electronic Health Records.基于电子健康记录的心房颤动预测模型的建立与验证。
JACC Clin Electrophysiol. 2019 Nov;5(11):1331-1341. doi: 10.1016/j.jacep.2019.07.016. Epub 2019 Oct 2.