Clinical Usefulness of Artificial Intelligence–Enabled Electrocardiography for Pulmonary Hypertension Detection: A Meta-analysis Based on Pre- and Post-Test Probability
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Pulmonary hypertension is frequently diagnosed late because its early manifestations are nonspecific. Artificial intelligence-enabled electrocardiography may provide a scalable method for identifying patients who warrant further evaluation. We searched eight databases from inception through May 2026 without language restrictions for studies evaluating artificial intelligence or machine-learning algorithms applied to standard 12-lead electrocardiograms against invasive hemodynamic or echocardiographic reference standards. Two reviewers independently selected studies, extracted data, and assessed risk of bias. Sensitivity and specificity were pooled using a bivariate random-effects model. The protocol was prospectively registered in PROSPERO (CRD420251149532). Five retrospective studies comprising 103,884 individuals were included. Pooled sensitivity was 0.81 (95% CI, 0.79-0.82), pooled specificity was 0.80 (95% CI, 0.76-0.83), and the summary area under the curve was 0.85 (95% CI, 0.81-0.88). The positive likelihood ratio was 4.03 (95% CI, 3.38-4.80), and the negative likelihood ratio was 0.24 (95% CI, 0.23-0.26). At a pretest probability of 20%, positive and negative results corresponded to posttest probabilities of approximately 50% and 6%, respectively. Heterogeneity was substantial, but leave-one-out analyses did not materially alter pooled estimates. Artificial intelligence-enabled electrocardiography showed good performance for detecting pulmonary hypertension and may support targeted case finding. Prospective studies should determine whether its use improves diagnostic pathways and patient outcomes.
Author summary
Pulmonary hypertension is a serious condition that is often recognized only after symptoms have progressed. The standard electrocardiogram is inexpensive and widely available, but visual interpretation alone may miss early or subtle changes. We examined whether artificial intelligence can improve the ability of the electrocardiogram to identify pulmonary hypertension. We combined results from five studies involving more than 103,000 people. The artificial intelligence models correctly identified approximately 81% of people with pulmonary hypertension and correctly classified approximately 80% of people without it. In a setting where one in five patients has pulmonary hypertension, a positive result would raise the estimated probability to about one in two, while a negative result would lower it to about one in sixteen. These findings suggest that artificial intelligence-enabled electrocardiography may help select patients for echocardiography or invasive testing. It should not be used alone to establish or exclude the diagnosis. Future prospective studies should determine whether using these models shortens diagnostic delay and improves care.
