Development of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening

"Skip to content\nHome\nProducts\nClinical\nNews\nInvestors\nAbout Us\nContact Us\nINVEST NOW\nPUBLICATIONS\nDevelopment of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening\nNovember 25, 2021\nSulaiman S Somani1, Hossein Honarvar1, Sukrit Narula2, Isotta Landi1, Shawn Lee3, Yeraz Khachatoorian4, Arsalan Rehmani3, Andrew Kim4, Jessica K De Freitas1, Shelly Teng1, Suraj Jaladanki1, Arvind Kumar1, Adam Russak1, Shan P Zhao1, Robert Freeman5, Matthew A Levin6, Girish N Nadkarni1, Alexander C Kagen7, Edgar Argulian3, Benjamin S Glicksberg1\nAims\n\nClinical scoring systems for pulmonary embolism (PE) screening have low specificity and contribute to computed tomography pulmonary angiogram (CTPA) overuse. We assessed whether deep learning models using an existing and routinely collected data modality, electrocardiogram (ECG) waveforms, can increase specificity for PE detection.\n\nMethods & Results\n\nWe create a retrospective cohort of 21 183 patients at moderate- to high suspicion of PE and associate 23 793 CTPAs (10.0% PE-positive) with 320 746 ECGs and encounter-level clinical data (demographics, comorbidities, vital signs, and labs). We develop three machine learning models to predict PE likelihood: an ECG model using only ECG waveform data, an EHR model using tabular clinical data, and a Fusion model integrating clinical data and an embedded representation of the ECG waveform. We find that a Fusion model [area under the receiver-operating characteristic curve (AUROC) 0.81 ± 0.01] outperforms both the ECG model (AUROC 0.59 ± 0.01) and EHR model (AUROC 0.65 ± 0.01). On a sample of 100 patients from the test set, the Fusion model also achieves greater specificity (0.18) and performance (AUROC 0.84 ± 0.01) than four commonly evaluated clinical scores: Wells’ Criteria, Revised Geneva Score, Pulmonary Embolism Rule-Out Criteria, and 4-Level Pulmonary Embolism Clinical Probability Score (AUROC 0.50-0.58, specificity 0.00-0.05). The model is superior to these scores on feature sensitivity analyses (AUROC 0.66-0.84) and achieves comparable performance across sex (AUROC 0.81) and racial/ethnic (AUROC 0.77-0.84) subgroups.\n\nConclusions\n\nSynergistic deep learning of ECG waveforms with traditional clinical variables can increase the specificity of PE detection in patients at least at moderate suspicion for PE\n\nLink to Publication\n\nDevelopment of a machine learning model using electrocardiogram signals to improve acute pulmonary embolism screening – PubMed (nih.gov)\n\n