Moon Jose, Kim Jong-Ho, Hong Soon Jun, Yu Cheol Woong, Kim Yong Hyun, Kim Eung Ju, Cha Jung-Joon, Joo Hyung Joon
Department of Medical Informatics, Korea University College of Medicine, Seoul 02841, Republic of Korea.
Korea University Research Institute for Medical Bigdata Science, Korea University College of Medicine, Seoul 02841, Republic of Korea.
Eur Heart J Acute Cardiovasc Care. 2025 Feb 20;14(2):74-82. doi: 10.1093/ehjacc/zuaf001.
Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.
In this retrospective cohort study, we analysed the ECG data of 19 285 patients who visited ERs of three hospitals between 2016 and 2020; 9119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study. We extracted morphological and clinical parameters from ECG data to train and validate four machine learning models: baseline linear regression and more advanced models including XGBoost, Light GBM, and CatBoost. The CatBoost algorithm outperformed other models, showing superior area under the receiver operating characteristic and area under the precision-recall curve diagnostic accuracy across both internal (0.89 ± 0.01 and 0.89 ± 0.01) and external (0.90 and 0.89) validation data sets, respectively. The model demonstrated high accuracy, precision, recall, and f1 score, indicating robust performance in AHF identification.
The developed machine learning model significantly enhanced AHF detection in the ER using conventional 12-lead ECGs combined with clinical data. These findings suggest that ECGs, a common tool in the ER, can effectively help screen for AHF.
急性心力衰竭（AHF）由于其临床表现多样以及传统诊断方法的局限性，在急诊室（ER）中带来了重大的诊断挑战。本研究旨在开发并评估一种使用心电图（ECG）数据的深度学习模型，以加强急诊室中AHF的识别。
在这项回顾性队列研究中，我们分析了2016年至2020年间在三家医院急诊室就诊的19285例患者的心电图数据；其中9119例有可用的左心室射血分数和脑钠肽前体N端水平数据且被诊断为AHF的患者被纳入研究。我们从心电图数据中提取形态学和临床参数，以训练和验证四个机器学习模型：基线线性回归以及包括XGBoost、Light GBM和CatBoost在内的更先进模型。CatBoost算法优于其他模型，在内部（0.89±0.01和0.89±0.01）和外部（0.90和0.89）验证数据集上分别显示出在受试者工作特征曲线下面积和精确召回率曲线下面积方面的卓越诊断准确性。该模型表现出高准确性、精确性、召回率和F1分数，表明在AHF识别方面具有强大的性能。
所开发的机器学习模型使用常规12导联心电图结合临床数据，显著提高了急诊室中AHF的检测能力。这些发现表明，心电图作为急诊室中的常用工具，可以有效地帮助筛查AHF。
