Electrocardiogram Signal Analysis with a Machine Learning Model Predicts the Presence of Pulmonary Embolism with Accuracy Dependent on Embolism Burden

ObjectiveTo develop an artificial intelligence deep neural network (AI-DNN) algorithm to analyze 12-lead electrocardiogram (ECG) for detection of acute PE and PE categories.Patients and MethodsA cohort of patients seen between January 1, 1999, and December 31, 2020, from across the Mayo Clinic Enterprise with computed tomography pulmonary angiogram (CTPA) and ECG performed +/- 6 hours was identified. Natural language processing algorithms were applied to radiology reports to determine the diagnosis of acute PE, acute PE with right ventricle strain (RVSPE), saddle PE (SADPE), or no PE. Diagnostic performance parameters of the AI-DNN reported were area under the receiver operating characteristics curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).ResultsA cohort of patients with CTPA report and ECG consisted of 79,894 patients including 7,423 (9.3%) with acute PE, among whom 1,138 patients had RVSPE or SADPE. AI-DNN predicted acute PE with a modest accuracy of AUROC 0.69, 95% CI 0.68-0.71, sensitivity of 63.5%, specificity of 64.7%, PPV of 15.6%, and NPV of 94.5%. The AI-DNN prediction using the same algorithm for RVSPE or SADPE was higher (AUROC 0.84, 95% CI 0.81-0.86) with a sensitivity of 80.8%, specificity of 64.7.8%, PPV of 3.5%, and NPV of 99.5%.ConclusionAn AI-based analysis of 12-lead ECG shows modest detection power for acute PE in patients who underwent CTPA, with higher accuracy for high-risk PE. Moreover, with the high NPV, it has the clinical potential to exclude high-risk PE quickly and correctly.