External Validation of Deep Learning-Based Cardiac Arrest Risk Management System for Predicting In-Hospital Cardiac Arrest in Patients Admitted to General Wards Based on Rapid Response System Operating and Nonoperating Periods: A Single-Center Study.

Objectives: The limitations of current early warning scores have prompted the development of deep learning-based systems, such as deep learning-based cardiac arrest risk management systems (DeepCARS). Unfortunately, in South Korea, only two institutions operate 24-hour Rapid Response System (RRS), whereas most hospitals have part-time or no RRS coverage at all. This study validated the predictive performance of DeepCARS during RRS operation and nonoperation periods and explored its potential beyond RRS operating hours.

Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards.

Background: Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice.

Methods: This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea.

Multicenter validation of a deep-learning-based pediatric early-warning system for prediction of deterioration events.

Background: Early recognition of deterioration events is crucial to improve clinical outcomes. For this purpose, we developed a deep-learning-based pediatric early-warning system (pDEWS) and aimed to validate its clinical performance.

Methods: This is a retrospective multicenter cohort study including five tertiary-care academic children's hospitals.

Quick Sequential Organ Failure Assessment Score and the Modified Early Warning Score for Predicting Clinical Deterioration in General Ward Patients Regardless of Suspected Infection.

Background: The quick sequential organ failure assessment (qSOFA) score is suggested to use for screening patients with a high risk of clinical deterioration in the general wards, which could simply be regarded as a general early warning score. However, comparison of unselected admissions to highlight the benefits of introducing qSOFA in hospitals already using Modified Early Warning Score (MEWS) remains unclear. We sought to compare qSOFA with MEWS for predicting clinical deterioration in general ward patients regardless of suspected infection.

A multicentre validation study of the deep learning-based early warning score for predicting in-hospital cardiac arrest in patients admitted to general wards.

Background: The recently developed deep learning (DL)-based early warning score (DEWS) has shown potential in predicting deteriorating patients. We aimed to validate DEWS in multiple centres and compare the prediction, alarming and timeliness performance with the modified early warning score (MEWS) to identify patients at risk for in-hospital cardiac arrest (IHCA).

Method/research Design: This retrospective cohort study included adult patients admitted to the general wards of five hospitals during a 12-month period.

Development and validation of a deep-learning-based pediatric early warning system: A single-center study.

Background: Early detection and prompt intervention for clinically deteriorating events are needed to improve clinical outcomes. There have been several attempts at this, including the introduction of rapid response teams (RRTs) with early warning scores. We developed a deep-learning-based pediatric early warning system (pDEWS) and validated its performance.

Graph Convolutional Networks-Based Noisy Data Imputation in Electronic Health Record.

Objectives: A deep learning-based early warning system is proposed to predict sepsis prior to its onset.

Design: A novel algorithm was devised to detect sepsis 6 hours prior to its onset based on electronic medical records.

Setting: Retrospective cohorts from three separate hospitals are used in this study.

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

Objectives: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation.

Design: Retrospective cohort study.

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services.

Background: In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS.

Methods: We conducted a retrospective observation cohort study.

Carbon Dioxide Angiography: Scientific Principles and Practice.

Carbon dioxide (CO2) is a colorless, odorless gas which occurs naturally in the atmosphere and human body. With the advent of digital subtraction angiography, the gas has been used as a safe and useful alternative contrast agent in both arteriography and venography. Because of its lack of renal toxicity and allergic potential, CO2 is a preferred contrast agent in patients with renal failure or contrast allergy, and particularly in patients who require large volumes of contrast medium for complex endovascular procedures.