Artificial Intelligence Algorithm for Screening Heart Failure with Reduced Ejection Fraction Using Electrocardiography.

Although heart failure with reduced ejection fraction (HFrEF) is a common clinical syndrome and can be modified by the administration of appropriate medical therapy, there is no adequate tool available to perform reliable, economical, early-stage screening. To meet this need, we developed an interpretable artificial intelligence (AI) algorithm for HFrEF screening using electrocardiography (ECG) and validated its performance. This retrospective cohort study included two hospitals. An AI algorithm based on a convolutional neural network was developed using 39,371 ECG results from 17,127 patients. The internal validation included 3,470 ECGs from 2,908 patients. Furthermore, we conducted external validation using 4,362 ECGs from 4,176 patients from another hospital to verify the applicability of the algorithm across different centers. The end-point was to detect HFrEF, defined as an ejection fraction <40%. We also visualized the regions in 12 lead ECG that affected HFrEF detection in the AI algorithm and compared this to the previously documented literature. During the internal and external validation, the areas under the curves of the AI algorithm using a 12 lead ECG for detecting HFrEF were 0.913 (95% confidence interval, 0.902-0.925) and 0.961 (0.951-0.971), respectively, and the areas under the curves of the AI algorithm using a single-lead ECG were 0.874 (0.859-0.890) and 0.929 (0.911-0.946), respectively. The deep learning-based AI algorithm performed HFrEF detection well using not only a 12 lead but also a single-lead ECG. These results suggest that HFrEF can be screened not only using a 12 lead ECG, as is typical of a conventional ECG machine, but also with a single-lead ECG performed by a wearable device employing the AI algorithm, thereby preventing irreversible disease progression and mortality.

Pulse Oximeter Plethysmograph Variation During Hemorrhage in Beta-Blocker-Treated Swine.

BACKGROUND: Beta-blockers blunt the stress response to hemorrhage. Our aim was to investigate the feasibility of noninvasive pulse oximeter plethysmographic waveform variation (PoPV) for predicting blood volume loss in an esmolol-treated swine hemorrhagic shock model. MATERIALS AND METHODS: Controlled hemorrhage was induced in eight male domestic pigs. In four pigs, a total of 15% and 30% blood volume was drawn step-by-step over 10 min in each step (controlled hemorrhage-only pigs). In the other four pigs, the heart rate (HR) was reduced and maintained by 30% from baseline by esmolol infusion before controlled hemorrhage (esmolol-treated pigs). Diagnostic abilities of HR, pulse pressure variation (PPV), PoPV, and mean arterial pressure for 15% and 30% blood volume loss were determined by the area under the receiver operating characteristic curve (AUC). RESULTS: PoPV was well correlated with PPV in controlled hemorrhage-only pigs (r = 0.717) and esmolol-treated pigs (r = 0.532). In controlled hemorrhage-only pigs, HR (AUC = 0.841 and 0.864), PPV (0.878 and 0.843), and PoPV (0.779 and 0.793) accurately predicted 15% and 30% of blood volume loss. In esmolol-treated pigs, the diagnostic ability of HR was decreased (AUC = 0.766 and 0.733). However, diagnostic abilities of PPV (0.848 and 0.804) and PoPV (0.808 and 0.842) were not deteriorated. CONCLUSIONS: The diagnostic ability of HR for blood volume loss was blunted by esmolol. However, those of PPV and PoPV were not altered. PoPV may be considered to be a useful noninvasive tool to predict blood volume loss in injured patients taking beta-blockers.