Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability …

MEH Ong, CH Lee Ng, K Goh, N Liu, ZX Koh… - Critical Care, 2012 - Springer
Introduction A key aim of triage is to identify those with high risk of cardiac arrest, as they
require intensive monitoring, resuscitation facilities, and early intervention. We aim to …

A novel cardiovascular risk stratification model incorporating ECG and heart rate variability for patients presenting to the emergency department with chest pain

MLA Heldeweg, N Liu, ZX Koh, S Fook-Chong, WK Lye… - Critical Care, 2016 - Springer
Background Risk stratification models can be employed at the emergency department (ED)
to evaluate patient prognosis and guide choice of treatment. We derived and validated a …

Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital

MA Dziadzko, PJ Novotny, J Sloan, O Gajic… - Critical Care, 2018 - Springer
Background Acute respiratory failure occurs frequently in hospitalized patients and often
starts before ICU admission. A risk stratification tool to predict mortality and risk for …

Prognostic accuracy of the Hamilton Early Warning Score (HEWS) and the National Early Warning Score 2 (NEWS2) among hospitalized patients assessed by a rapid …

SM Fernando, AE Fox-Robichaud, B Rochwerg… - Critical care, 2019 - Springer
Abstract Background Rapid response teams (RRTs) respond to hospitalized patients
experiencing clinical deterioration and help determine subsequent management and …

Effect of an automated notification system for deteriorating ward patients on clinical outcomes

CP Subbe, B Duller, R Bellomo - Critical Care, 2017 - Springer
Background Delayed response to clinical deterioration of ward patients is common. Methods
We performed a prospective before-and-after study in all patients admitted to two clinical …

Decision-tree model for predicting outcomes after out-of-hospital cardiac arrest in the emergency department

Y Goto, T Maeda, Y Goto - Critical Care, 2013 - Springer
Introduction Estimation of outcomes in patients after out-of-hospital cardiac arrest (OHCA)
soon after arrival at the hospital may help clinicians guide in-hospital strategies, particularly …

Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care

J Johnsson, O Björnsson, P Andersson, A Jakobsson… - Critical care, 2020 - Springer
Background Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and
clinical status on admission are strongly associated with outcome after out-of-hospital …

A deep learning model for real-time mortality prediction in critically ill children

SY Kim, S Kim, J Cho, YS Kim, IS Sol, Y Sung, I Cho… - Critical care, 2019 - Springer
Background The rapid development in big data analytics and the data-rich environment of
intensive care units together provide unprecedented opportunities for medical …

External validation of the 2020 ERC/ESICM prognostication strategy algorithm after cardiac arrest

CS Youn, KN Park, SH Kim, BK Lee, T Cronberg… - Critical Care, 2022 - Springer
Purpose To assess the performance of the post-cardiac arrest (CA) prognostication strategy
algorithm recommended by the European Resuscitation Council (ERC) and the European …

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network …

P Andersson, J Johnsson, O Björnsson, T Cronberg… - Critical Care, 2021 - Springer
Background Prognostication of neurological outcome in patients who remain comatose after
cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain …