An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension

Abstract

Background: Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG.

Methods: The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites.

Results: Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis.

Conclusion: The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

Summary of the development and validation of the Pulmonary Hypertension (PH) Early Detection Algorithm (PH-EDA) based on analysis of a standard 12-lead ECG. mPAP: mean pulmonary arterial pressure; RHC: right heart catheterisation; TRV: tricuspid regurgitation velocity; VUMC: Vanderbilt University Medical Center; AUC: area under the curve.

FIGURE 1

Overview of the analytical plan to train and evaluate performance of a deep learning-based ECG algorithm in detection of pulmonary hypertension (PH). It illustrates the steps taken to train and evaluate the algorithm using ECG data from Mayo Clinic. It is a pre-trained algorithm, i.e. when used at other sites, such as Vanderbilt University Medical Center, it is not re-trained on data from those sites. EHR: electronic health record; RHC: right heart catheterisation.

FIGURE 2

Patient flow diagram. Schematic of the dataset creation from Mayo Clinic and Vanderbilt University Medical Center (VUMC). PH: pulmonary hypertension; mPAP: mean pulmonary arterial pressure; RHC: right heart catheterisation; ICD: International Classification of Disease; TRV: tricuspid regurgitation velocity. ^#: dyspnoea ICD code on or before an eligible ECG and the diagnostic RHC/echocardiogram (for PH-likely patients) or the last screening RHC/echocardiogram (for control patients).

FIGURE 3

Receiver operating characteristic curves for the performance of the pulmonary hypertension (PH) early detection algorithm (PH-EDA) to identify patients with PH in ECGs across all four time windows of interest in a, c) all patients and b, d) patients with dyspnoea filter using the test datasets from a, b) Mayo Clinic and c, d) Vanderbilt University Medical Center (VUMC). AUC: area under the curve.

FIGURE 4

Sensitivity and specificity of the pulmonary hypertension (PH) early detection algorithm (PH-EDA) to detect PH across all four time windows of interest, in a) all patients and b) patients with dyspnoea, using test sets from Mayo Clinic and Vanderbilt University Medical Center (VUMC).