Liu Pang-Yen, Lin Chin, Lin Chin-Sheng, Fang Wen-Hui, Lee Chia-Cheng, Wang Chih-Hung, Tsai Dung-Jang
Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan.
Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei 114, Taiwan.
Diagnostics (Basel). 2023 Aug 22;13(17):2723. doi: 10.3390/diagnostics13172723.
The B-type natriuretic peptide (BNP) and N-terminal pro-brain natriuretic peptide (pBNP) are predictors of cardiovascular morbidity and mortality. Since the artificial intelligence (AI)-enabled electrocardiogram (ECG) system is widely used in the management of many cardiovascular diseases (CVDs), patients requiring intensive monitoring may benefit from an AI-ECG with BNP/pBNP predictions. This study aimed to develop an AI-ECG to predict BNP/pBNP and compare their values for future mortality. The development, tuning, internal validation, and external validation sets included 47,709, 16,249, 4001, and 6042 ECGs, respectively. Deep learning models (DLMs) were trained using a development set for estimating ECG-based BNP/pBNP (ECG-BNP/ECG-pBNP), and the tuning set was used to guide the training process. The ECGs in internal and external validation sets belonging to nonrepeating patients were used to validate the DLMs. We also followed-up all-cause mortality to explore the prognostic value. The DLMs accurately distinguished mild (≥500 pg/mL) and severe (≥1000 pg/mL) an abnormal BNP/pBNP with AUCs of ≥0.85 in the internal and external validation sets, which provided sensitivities of 68.0-85.0% and specificities of 77.9-86.2%. In continuous predictions, the Pearson correlation coefficient between ECG-BNP and ECG-pBNP was 0.93, and they were both associated with similar ECG features, such as the T wave axis and correct QT interval. ECG-pBNP provided a higher all-cause mortality predictive value than ECG-BNP. The AI-ECG can accurately estimate BNP/pBNP and may be useful for monitoring the risk of CVDs. Moreover, ECG-pBNP may be a better indicator to manage the risk of future mortality.
B型利钠肽（BNP）和N末端前脑利钠肽（pBNP）是心血管疾病发病率和死亡率的预测指标。由于启用人工智能（AI）的心电图（ECG）系统广泛应用于多种心血管疾病（CVD）的管理，需要强化监测的患者可能会从具有BNP/pBNP预测功能的AI-ECG中获益。本研究旨在开发一种AI-ECG来预测BNP/pBNP，并比较它们对未来死亡率的预测价值。开发集、调整集、内部验证集和外部验证集分别包含47709份、16249份、4001份和6042份心电图。使用开发集训练深度学习模型（DLM）以估计基于心电图的BNP/pBNP（ECG-BNP/ECG-pBNP），并使用调整集指导训练过程。属于非重复患者的内部和外部验证集中的心电图用于验证DLM。我们还对全因死亡率进行随访以探索其预后价值。DLM在内部和外部验证集中能够准确区分轻度（≥500 pg/mL）和重度（≥1000 pg/mL）异常BNP/pBNP，曲线下面积（AUC）≥0.85，敏感性为68.0 - 85.0%，特异性为77.9 - 86.2%。在连续预测中，ECG-BNP与ECG-pBNP之间的Pearson相关系数为0.93，且它们均与类似的心电图特征相关，如T波轴和校正QT间期。ECG-pBNP比ECG-BNP具有更高的全因死亡率预测价值。AI-ECG能够准确估计BNP/pBNP，可能有助于监测CVD风险。此外，ECG-pBNP可能是管理未来死亡风险的更好指标。
