RESUMEN
Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80-90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions.
RESUMEN
3-Dimensional (3D) myocardial deformation analysis (3D-MDA) enables novel descriptions of geometry-independent principal strain (PS). Applied to routine 2D cine cardiovascular magnetic resonance (CMR), this provides unique measures of myocardial biomechanics for disease diagnosis and prognostication. However, healthy reference values remain undefined. This study describes age- and sex-stratified reference values from CMR-based 3D-MDA, including 3D PS. One hundred healthy volunteers were prospectively recruited following institutional ethics approval and underwent CMR imaging. 3D-MDA was performed using validated software. Age- and sex-stratified global and segmental strain measures were derived for conventional geometry-dependent [circumferential (CS), longitudinal (LS), and radial (RS)] and geometry-independent [minimum (minPS) and maximum principal (maxPS)] directions of deformation. Layer-specific contraction angle interactions were determined using local minPS vectors. The average age was 43 ± 15 years and 55% were women. Strain measures were higher in women versus men. 3D PS-based assessment of maximum tissue shortening (minPS) and maximum tissue thickening (maxPS) were greater than corresponding geometry-dependent markers of LS and RS, consistent with improved representation of local tissue deformations. Global maxPS amplitude best discriminated both age and sex. Segmental analyses showed greater strain amplitudes in apical segments. Transmural PS contraction angles were higher in females and showed a heterogeneous distribution across segments. In this study we provided age and sex-based reference values for 3D strain from CMR imaging, demonstrating improved capacity for 3D PS to document maximal local tissue deformations and to discriminate age and sex phenotypes. Novel markers of layer-specific strain angles from 3D PS were also described.
