Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.

Background: Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not available for use in general wards, and thus, many clinicians usually rely on practical senses to diagnose postoperative infection.

Objective: This study aims to evaluate the performance of continuous monitoring with a deep learning model for early detection of bacteremia for surgical in-patients in the general ward and the intensive care unit (ICU).

Single- and dual-site pace mapping of idiopathic septal intramural ventricular arrhythmias.

Background: Pace mapping (PM) is used to identify the origin of ventricular arrhythmias (VAs). For intramural VAs, the site of origin often cannot be reached and therefore PM is less accurate.

Objective: The purpose of this study was to assess the value of single- and dual-site pace maps to differentiate intramural from nonintramural VAs.

Computerized analysis of the 12-lead electrocardiogram to identify epicardial ventricular tachycardia exit sites.

Background: Twelve-lead electrocardiogram (ECG) criteria for epicardial ventricular tachycardia (VT) origins have been described. In patients with structural heart disease, the ability to predict an epicardial origin based on QRS morphology is limited and has been investigated only for limited regions in the heart.

Objective: The purpose of this study was to determine whether a computerized algorithm is able to accurately differentiate epicardial vs endocardial origins of ventricular arrhythmias.