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. 2023 Aug 22;13(17):2723.
doi: 10.3390/diagnostics13172723.

Artificial Intelligence-Enabled Electrocardiography Detects B-Type Natriuretic Peptide and N-Terminal Pro-Brain Natriuretic Peptide

Pang-Yen Liu  1 , Chin Lin  2   3   4 , Chin-Sheng Lin  1   2 , Wen-Hui Fang  4   5 , Chia-Cheng Lee  6   7 , Chih-Hung Wang  8   9 , Dung-Jang Tsai  2   3   4   10
Affiliations

Artificial Intelligence-Enabled Electrocardiography Detects B-Type Natriuretic Peptide and N-Terminal Pro-Brain Natriuretic Peptide

Pang-Yen Liu et al. Diagnostics (Basel). .

Abstract

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Keywords: B-type natriuretic peptide; N-terminal pro-brain natriuretic peptide; artificial intelligence; deep learning; electrocardiogram.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Development, tuning, internal validation, and external validation set generation and ECG labeling of BNP/pBNP. Schematic of the dataset creation and analysis strategy, which was devised to assure a robust and reliable dataset for the training, validating, and testing of the network. Once a patient’s data were placed in one of the datasets, that individual’s data were used only in that set, avoiding ‘cross-contamination’ among the training, validation, and test datasets. The details of the flow chart and how each of the datasets were used are described in the Methods.
Figure 2
Figure 2
The ROC curve of DLM predictions based on ECG to detect mild and severe abnormal BNP/pBNP. Mild and severe abnormal BNP/pBNP were defined as actual BNP/pBNP of ≥500 and ≥1000, respectively. The operating point was selected based on the maximum Youden’s index in the tuning set and are presented using a circle mark; it was then used to calculate the area under the ROC curve (AUC), sensitivity (Sens.), specificity (Spec.), positive predictive value (PPV), and negative predictive value (NPV).
Figure 3
Figure 3
Scatter plots of predicted BNP/pBNP (ECG-BNP/ECG-pBNP) and actual BNP/pBNP. The x-axis and the y-axis are presented on a log scale. Red and blue colors represent BNP and pBNP, respectively. We presented the Pearson correlation coefficients (r) on a log scale to demonstrate the accuracy of the DLMs. The black lines with 95% conference intervals were fitted via simple linear regression on the log scale.
Figure 4
Figure 4
Relationship between the most important ECG features and predicted BNP (ECG-BNP). The related importance is based on the information gain of the XGB model, and the R-square (R-sq) is the coefficient of determination to use selected ECG features for predicting ECG-BNP on a log scale. The analyses were conducted in both the internal and external validation sets. (***: p for trend < 0.001).
Figure 5
Figure 5
Relationship between the most important ECG features and predicted pBNP (ECG-pBNP). The related importance is based on the information gain of the XGB model, and the R-square (R-sq) is the coefficient of determination to use selected ECG features for predicting ECG-pBNP on a log scale. The analyses were conducted in both the internal and external validation sets. (***: p for trend < 0.001).
Figure 6
Figure 6
Long-term incidence of developing mortality events stratified by ECG-BNP or ECG-pBNP. The analyses were conducted in both the internal and external validation sets. The table shows the at-risk population and cumulative risk for the given time intervals in each risk stratification.
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