A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension

Abstract

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug development in the past decade. The application of Artificial Intelligence for the detection of elevated pulmonary arterial pressure (ePAP) was reported recently. Artificial Intelligence (AI) has demonstrated the capability to identify ePAP and its association with hospitalization due to heart failure when analyzing chest X-rays (CXR). An AI model based on electrocardiograms (ECG) has shown promise in not only detecting ePAP but also in predicting future risks related to cardiovascular mortality. We aimed to develop an AI model integrating ECG and CXR to detect ePAP and evaluate their performance. We developed a deep-learning model (DLM) using paired ECG and CXR to detect ePAP (systolic pulmonary artery pressure > 50 mmHg in transthoracic echocardiography). This model was further validated in a community hospital. Additionally, our DLM was evaluated for its ability to predict future occurrences of left ventricular dysfunction (LVD, ejection fraction < 35%) and cardiovascular mortality. The AUCs for detecting ePAP were as follows: 0.8261 with ECG (sensitivity 76.6%, specificity 74.5%), 0.8525 with CXR (sensitivity 82.8%, specificity 72.7%), and 0.8644 with a combination of both (sensitivity 78.6%, specificity 79.2%) in the internal dataset. In the external validation dataset, the AUCs for ePAP detection were 0.8348 with ECG, 0.8605 with CXR, and 0.8734 with the combination. Furthermore, using the combination of ECGs and CXR, the negative predictive value (NPV) was 98% in the internal dataset and 98.1% in the external dataset. Patients with ePAP detected by the DLM using combination had a higher risk of new-onset LVD with a hazard ratio (HR) of 4.51 (95% CI: 3.54-5.76) in the internal dataset and cardiovascular mortality with a HR of 6.08 (95% CI: 4.66-7.95). Similar results were seen in the external validation dataset. The DLM, integrating ECG and CXR, effectively detected ePAP with a strong NPV and forecasted future risks of developing LVD and cardiovascular mortality. This model has the potential to expedite the early identification of pulmonary hypertension in patients, prompting further evaluation through echocardiography and, when necessary, right heart catheterization (RHC), potentially resulting in enhanced cardiovascular outcomes.

Keywords: Artificial Intelligence; Chest X-ray; Deep learning; Electrocardiogram; Pulmonary arterial hypertension.

Fig. 1

Development, tuning, internal validation, and external validation sets generation and labeling of echocardiography. We designed a schematic for the creation and analysis of the data set to ensure its robustness and reliability during network development, tuning, and validation. Each patient’s data were exclusively assigned to one of the designated data sets, preventing any cross-contamination between sets

Fig. 2

The ROC curve of DLM predictions based on ECG, CXR, and combination to detect ePAP. The cut-off point was chosen based on the maximum value of Yunden’s index in the tuning set and indicated by a circle mark. Subsequently, the area under the ROC curve (AUC), sensitivity (Sens.), specificity (Spec.), positive predictive value (PPV), and negative predictive value (NPV) were calculated based on this selected cut-off point

Fig. 3

The association of subgroups and the DLM diagnostic performance

Fig. 4

Long-term incidence of developing new-onset left ventricular dysfunction (LVD, EF ≤ 35%) and cardiovascular (CV) death, stratifying patients based on the presence of ePAP as determined by DLM. These analyses were performed in both the internal and external validation sets. It is important to note that the analysis for new-onset LVD only included patients with an initial EF > 50%. The table presents information on the at-risk population and cumulative risk for specific time intervals within each risk stratification