摘要
目的建立一种基于24 h心电图数据开发的集成模型,从而对房颤高危人群发生房颤的风险,及实时预测阵发性房颤患者房颤的发作。方法连续回顾性收集2018年1月1日至2021年12月31日在暨南大学第一附属医院心电报告诊断为阵发性心房颤动的患者共310例,经筛选后共有124例患者作为心房颤动组纳入本研究。同时以1∶4的比例随机选择496例心电图报告正常的患者作为非心房颤动组。两组患者最终一起被随机分配,得到训练集(n=434)、验证集(n=62)和测试集(n=124),比例为7∶1∶2,以进行心电模型训练。建立心电神经网络模型和心率神经网络模型,最后使用逻辑回归将心电神经网络模型和心率神经网络模型结合得到集成模型。结果经过训练、验证和测试,人工智能集成算法的曲线下面积为0.94(95%CI 0.75~0.94),其敏感度、特异度、准确度、精确度和F1分数分别为56.0%、98.0%、90.0%、93.0%和0.70。与临床风险模型和现有的房颤预测模型HARMS2-AF评分相比,AI算法的性能更好(P<0.01)。结论人工智能集成算法可能是预测房颤风险,实时预测房颤发作的有效方法,可以作为一种早期预警工具。这对房颤的筛查,个体化抗凝方案的制定具有重要的临床意义。
Objective To develop an integrated model utilizing 24-hour electrocardiogram data to accurately predict the risk of atrial fibrillation in high-risk populations and provide real-time prediction for the onset of atrial fibrillation in patients with paroxysmal atrial fibrillation.Methods Consecutively,a total of 310 patients diagnosed with paroxysmal atrial fibrillation by electrocardiographic report at the First Affiliated Hospital of Jinan University were retrospectively collected from January 1,2018,to December 31,2021,and a total of 124 patients were enrolled in this study as the atrial fibrillation group after screening.Additionally,a non-atrial fibrillation group consisting of 496 patients with normal ECG reports was randomly selected at a ratio of 1∶4.Subsequently,both groups were randomly divided into three sets for ECG model training:a training set(n=434),a validation set(n=62),and a test set(n=124)in a ratio of 7∶1∶2.The establishment process involved developing an ECG neural network model and heart rate neural network model separately.Finally,logistic regression was employed to combine these models into an integrated model.Results After validation and testing,the AI algorithm achieved an AUC of 0.94(95%CI 0.75-0.94),with sensitivity,specificity,accuracy,precision,and F1score of 56.0%,98.0%,90.0%,93.0%,and 0.70 respectively.Compared with the clinical risk model and the existing AF prediction model,HARMS2-AF score,the artificial intelligence algorithm had a superior performance(P<0.01).Conclusions Artificial intelligence-integrated algorithms seem to be an effective method for predicting AF risk and forecasting AF episodes in real time.This could have important clinical implications for screening for AF and developing personalized anticoagulation plans.
作者
李盼盼
韩宇臣
李峰
陈雨
郭军
LI Pan-pan;HAN Yu-chen;LI Feng;CHEN Yu;GUO Jun(Department of Cardiology,The First Affiliated Hospital of Jinan University,Guangzhou 510630,China;Shenzhen Dawei Medical Technology Development limited company,Shenzhen 518000,China)
出处
《中国心血管病研究》
CAS
2024年第3期196-202,共7页
Chinese Journal of Cardiovascular Research
基金
广州市科技计划项目(重点研发)(202103000010)
广州市泛血管病基础及转化研究实验中心重点实验室(a01937)
广州市无菌动物与微生态转化重点实验室(202201020381)
暨南大学附属第一医院临床前沿新技术项目(JNU1AF-CFTP-2022-a01218)。
关键词
人工智能
24
h动态心电图
阵发性心房颤动
风险预测
Artificial intelligence
24-hour Holter
Paroxysmal atrial fibrillation
Risk prediction
