Wadforth Brandon, Salari Shahrbabaki Sobhan, Strong Campbell, Karnon Jonathan, Goh Jing Soong, O'Loughlin Luke Phillip, Tonchev Ivaylo, Mitchell Lewis, Strube Taylor, Lorensini Scott, Chapman Darius, Jenkins Evan, Ganesan Anand N
College of Medicine and Public Health, Flinders University, Flinders Drive, Bedford Park, Adelaide, SA 5042, Australia.
Department of Medicine, Cardiac and Critical Care, Flinders Medical Centre, Adelaide, Australia.
Eur Heart J Digit Health. 2025 Aug 5;6(5):969-978. doi: 10.1093/ehjdh/ztaf081. eCollection 2025 Sep.
Spontaneous cardioversion (SCV) is commonly observed in patients presenting to emergency departments (EDs) with primary atrial fibrillation (AF). Predicting SCV could facilitate timely discharge and avoid costly admissions. We sought to evaluate whether SCV could be predicted using artificial intelligence-enabled electrocardiograms (AI-ECGs) and whether this could produce cost savings.
We recruited patients presenting to EDs with primary AF throughout 2022-23. Patients were excluded if the outcome of their AF episode was unclear, or the ECG was not accessible. Spontaneous cardioversion prediction was attempted using ResNet50, EfficientNet, and DenseNet convolutional neural network (CNN) architectures and subsequently an ensemble learning model. We then performed a cost-minimization analysis to estimate the cost effect of a prediction-guided 'wait-and-see' protocol. There were 1159 presentations to the ED, of which 502 had sufficient data for inclusion. The median age was 74.0 years and 54.0% were women. Spontaneous cardioversion occurred in 227 (45.2%) patients and was more frequent in younger patients ( < 0.001). The ensemble learning model outperformed individual CNNs, achieving an accuracy of 69.7% (SD 5.91) and a receiver operating characteristic area under the curve (ROC AUC) of 0.742 (SD 0.037) with a sensitivity and specificity of 0.736 (SD 0.068) and 0.657 (SD 0.150), respectively. The per patient cost was $4681 if all patients were admitted, which reduced to $3398 with a prediction-guided 'wait-and-see' protocol with a 33.3% reduction in overall hospitalization.
Artificial intelligence-enabled electrocardiogram can predict SCV in patients presenting to EDs with primary AF, and a prediction-guided 'wait-and-see' protocol utilizing AI-ECG could lead to substantial cost savings and reduced hospitalization.
在因原发性心房颤动（AF）就诊于急诊科（ED）的患者中，常可观察到自发复律（SCV）。预测SCV有助于及时出院并避免高昂的住院费用。我们试图评估是否可以使用人工智能心电图（AI-ECG）预测SCV，以及这是否能节省成本。
我们招募了2022年至2023年期间因原发性AF就诊于ED的患者。如果AF发作的结果不明确或无法获取心电图，则将患者排除。尝试使用ResNet50、EfficientNet和DenseNet卷积神经网络（CNN）架构以及随后的集成学习模型进行自发复律预测。然后，我们进行了成本最小化分析，以估计预测指导的“观察等待”方案的成本效益。共有1159例患者就诊于ED，其中502例有足够的数据纳入研究。中位年龄为74.0岁，女性占54.0%。227例（45.2%）患者发生了自发复律，且在年轻患者中更常见（<0.001）。集成学习模型优于单个CNN，准确率为69.7%（标准差5.91），曲线下面积（ROC AUC）为0.742（标准差0.037），敏感性和特异性分别为0.736（标准差0.068）和0.657（标准差0.150）。如果所有患者都住院，每位患者的成本为4681美元，采用预测指导的“观察等待”方案后降至3398美元，总体住院率降低了33.3%。
人工智能心电图可以预测因原发性AF就诊于ED的患者的SCV，利用AI-ECG的预测指导“观察等待”方案可大幅节省成本并减少住院时间。
