The performance of the National Early Warning Score and National Early Warning Score 2 in hospitalised patients infected by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2)
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 25557760
- Type
- D - Journal article
- DOI
-
10.1016/j.resuscitation.2020.10.039
- Title of journal
- Resuscitation
- Article number
- 0
- First page
- 150
- Volume
- 159
- Issue
- 0
- ISSN
- 0300-9572
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
7
- Research group(s)
-
A - Centre for Healthcare Modelling and Informatics
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- One of our outputs applying our data science skills to the development of the NHS-mandated National Early Warning Score (NEWS) and its evaluation. Significant in being the first to show that different techniques are not required for monitoring the severity of illness of patients with COVID-19. This has implications that reduce the need for additional staff training or other investments. The journal considered this important enough to commission an editorial (https://dx.doi.org/10.1016%2Fj.resuscitation.2020.12.008) that compared our results favourably with comparable papers on COVID-19 prognosis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
