Gao Zheng, Yang Yuqing, Yang Zhiqiang, Zhang Xinyue, Liu Chao
Department of Cardiology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
Department of Cardiology, Cangzhou Central Hospital, Cangzhou, China.
ESC Heart Fail. 2025 Feb;12(1):631-639. doi: 10.1002/ehf2.15120. Epub 2024 Oct 27.
Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs).
A cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network-long short-term memory (CNN-LSTM) DLM was employed. HFpEF risk was determined by left ventricular end-diastolic pressure (LVEDP) and clinical symptoms. The DLM was trained by ECGs. LVEDP for each patient was collected through invasive left ventricular catheterisation. Cohort A and B comprised data from individuals at high risk for HFpEF (LVEDP > 12 mmHg) and low risk for HFpEF (LVEDP ≤ 12 mmHg). The model was trained on Cohort A and prospectively validated on Cohort B.
A total of 238 patients underwent ECG and left ventricular catheterisation for model training in Cohort A, and 117 patients for validation in Cohort B. The DLM achieved 78% accuracy in assessing HFpEF risk in Cohort A, while in Cohort B, it demonstrated 78% accuracy, 71.9% specificity, and 71.7% sensitivity. In the validation Cohort B, the DLM-identified high-risk HFpEF group exhibited significantly higher prevalence of diabetes (22.03%-11.86%, P < 0.01), higher BMI indices (25.92-24.22 kg/cm, P < 0.01), and lower usage history of calcium channel blockers (CCB) (11.76%-28.81%, P < 0.01) compared with the DLM-identified low-risk HFpEF group. Traditional HFpEF indicators, including B-type natriuretic peptide (BNP) (22-20 pg/mL, P = 0.71) and E/E' (8.25-8.5, P = 0.66), did not exhibit significant differences between the two groups.
The DLM offers an accurate, cost-effective tool for HFpEF risk assessment, potentially facilitating early detection and improved clinical management.
射血分数保留的心力衰竭（HFpEF）需要一种有效的筛查方法。我们开发了一种深度学习模型（DLM），用于利用心电图（ECG）筛查HFpEF风险。
利用队列A和队列B的数据进行了一项队列研究。采用了卷积神经网络-长短期记忆（CNN-LSTM）DLM。HFpEF风险由左心室舒张末期压力（LVEDP）和临床症状确定。DLM通过心电图进行训练。通过有创左心室导管插入术收集每位患者的LVEDP。队列A和队列B包含来自HFpEF高风险个体（LVEDP>12 mmHg）和HFpEF低风险个体（LVEDP≤12 mmHg）的数据。该模型在队列A上进行训练，并在队列B上进行前瞻性验证。
队列A中共有238例患者接受了心电图和左心室导管插入术以进行模型训练，队列B中有117例患者接受验证。DLM在评估队列A中的HFpEF风险时准确率达到78%，而在队列B中，其准确率为78%，特异性为71.9%，敏感性为71.7%。在验证队列B中，与DLM识别的低风险HFpEF组相比，DLM识别的高风险HFpEF组的糖尿病患病率显著更高（22.03%-11.86%，P<0.01），BMI指数更高（25.92-24.22 kg/cm，P<0.01），钙通道阻滞剂（CCB）使用史更低（11.76%-28.81%，P<0.01）。传统的HFpEF指标，包括B型利钠肽（BNP）（22-20 pg/mL，P=0.71）和E/E'（8.25-8.5，P=0.66），在两组之间没有显著差异。
DLM为HFpEF风险评估提供了一种准确、经济高效的工具，可能有助于早期检测和改善临床管理。
