scholarly journals Development and validation of early warning score systems for COVID‐19 patients

2021 ◽  
Author(s):  
Alexey Youssef ◽  
Samaneh Kouchaki ◽  
Farah Shamout ◽  
Jacob Armstrong ◽  
Rasheed El‐Bouri ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning Score ◽  
Development And Validation ◽  
Score Systems

scholarly journals Development and Validation of Early Warning Score Systems for COVID-19 Patients

2020 ◽  
Author(s):  
Alexey Youssef ◽  
Samaneh Kouchaki ◽  
Farah Shamout ◽  
Jacob Armstrong ◽  
Rasheed El-Bouri ◽  
...  
Keyword(s):  
Early Warning ◽  
Vital Signs ◽  
Early Warning Score ◽  
Respiratory Support ◽  
Patient Treatment ◽  
Gradient Boosting ◽  
Non Invasive ◽  
Non Invasive Ventilation ◽  
Development And Validation ◽  
Score Systems

AbstractCOVID-19 is a major, urgent, and ongoing threat to global health. Globally more than 24 million have been infected and the disease has claimed more than a million lives as of October 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. We evaluated the ability of six common Early Warning Scores (EWS) to identify respiratory deterioration defined as the need for advanced respiratory support (high-flow nasal oxygen, continuous positive airways pressure, non-invasive ventilation, intubation) within a prediction window of 24 hours. We show these scores perform sub-optimally at this specific task. Therefore, we develop an alternative Early Warning Score based on a Gradient Boosting Trees (GBT) algorithm that is able to predict deterioration within the next 24 hours with high AUROC 94% and an accuracy, sensitivity and specificity of 70%, 96%, 70%, respectively. Our GBT model outperformed the best EWS (LDTEWS:NEWS), increasing the AUROC by 14%. Our GBT model makes the prediction based on the current and baseline measures of routinely available vital signs and blood tests.

scholarly journals Development and validation of early warning score system: A systematic literature review

2020 ◽  
Vol 105 ◽  
pp. 103410 ◽  
Author(s):  
Li-Heng Fu ◽  
Jessica Schwartz ◽  
Amanda Moy ◽  
Chris Knaplund ◽  
Min-Jeoung Kang ◽  
...  
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Early Warning ◽  
Early Warning Score ◽  
Score System ◽  
System A ◽  
Development And Validation

scholarly journals Predictive accuracy of computer-aided versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study

2020 ◽  
Author(s):  
Muhammad Faisal ◽  
Mohammed A Mohammed ◽  
Donald Richardson ◽  
Ewout W. Steyerberg ◽  
Massimo Fiori ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Model Validation ◽  
Validation Study ◽  
Early Warning ◽  
Model Development ◽  
Early Warning Score ◽  
Validation Dataset ◽  
Unplanned Admission ◽  
National Early Warning Score ◽  
Development And Validation

AbstractObjectivesTo consider the potential of the National Early Warning Score (NEWS2) for COVID-19 risk prediction on unplanned admission to hospital.DesignLogistic regression model development and validation study using a cohort of unplanned emergency medical admission to hospital.SettingYork Hospital (YH) as model development dataset and Scarborough Hospital (SH) as model validation dataset.ParticipantsUnplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020) from two hospitals (YH for model development; SH for external model validation) based on admission NEWS2 electronically recorded within ±24 hours of admission. We used logistic regression modelling to predict the risk of COVID-19 using NEWS2 (Model M0’) versus enhanced cNEWS models which included age + sex (model M1’) + subcomponents (including diastolic blood pressure + oxygen flow rate + oxygen scale) of NEWS2 (model M2’). The ICD-10 code ‘U071’ was used to identify COVID-19 admissions. Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥5.ResultsThe prevalence of COVID-19 was higher in SH (11.0%=277/2520) than YH (8.7%=343/3924) with higher index NEWS2 (3.2 vs 2.8) but similar in-hospital mortality (8.4% vs 8.2%). The c-statistics for predicting COVID-19 for cNEWS models (M1’,M2’) was substantially better than NEWS2 alone (M0’) in development (M2’: 0.78 (95%CI 0.75-0.80) vs M0’ 0.71 (95%CI 0.68-0.74)) and validation datasets (M2’: 0.72 (95%CI 0.69-0.75) vs M0’ 0.65 (95%CI 0.61-0.68)). Model M2’ had better calibration than Model M0’ with improved sensitivity (M2’: 57% (95%CI 51%-63%) vs M0’ 44% (95%CI 38%-50%)) and similar specificity (M2’: 76% (95%CI 74%-78%) vs M0’ 75% (95%CI 73%-77%)) for validation dataset at NEWS2≥5.ConclusionsModel M2’ is reasonably accurate for predicting the on-admission risk of COVID-19. It may be clinically useful for an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.

Pediatric Early Warning Score Systems, Nurses Perspective – A Focus Group Study

2018 ◽  
Vol 41 ◽  
pp. e16-e22 ◽  
Author(s):  
Claus Sixtus Jensen ◽  
Pia Bonde Nielsen ◽  
Hanne Vebert Olesen ◽  
Hans Kirkegaard ◽  
Hanne Aagaard
Keyword(s):  
Focus Group ◽  
Early Warning ◽  
Early Warning Score ◽  
Focus Group Study ◽  
Score Systems

Development and validation of a Paediatric Early Warning Score for use in the emergency department: a multicentre study

2020 ◽  
Vol 4 (8) ◽  
pp. 583-591 ◽  
Author(s):  
Joany M Zachariasse ◽  
Daan Nieboer ◽  
Ian K Maconochie ◽  
Frank J Smit ◽  
Claudio F Alves ◽  
...  
Keyword(s):  
Emergency Department ◽  
Multicentre Study ◽  
Early Warning ◽  
Early Warning Score ◽  
Paediatric Early Warning Score ◽  
Development And Validation
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