Sherif Gonem
Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease
Gonem, Sherif; Taylor, Adam; Figueredo, Grazziela; Forster, Sarah; Quinlan, Philip; Garibaldi, Jonathan M.; McKeever, Tricia M.; Shaw, Dominick
Authors
Adam Taylor
Grazziela Figueredo
Sarah Forster
Philip Quinlan
Prof. JONATHAN GARIBALDI JON.GARIBALDI@NOTTINGHAM.AC.UK
Provost and Pvc Unnc
TRICIA MCKEEVER tricia.mckeever@nottingham.ac.uk
Professor of Epidemiology and Medical Statistics
Dominick Shaw
Abstract
Background: The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop dynamic early warning scores (DEWS) to address these deficiencies, and tested their accuracy in patients with respiratory disease for predicting (1) death or intensive care unit admission, occurring within 24h (D/ICU), and (2) clinically significant deterioration requiring urgent intervention, occurring within 4h (CSD). Methods: Clinical observations data were extracted from electronic records for 31,590 respiratory in-patient episodes from April 2015 to December 2020 at a large acute NHS Trust. The timing of D/ICU was extracted for all episodes. 1100 in-patient episodes were annotated manually to record the timing of CSD, defined as a specific event requiring a change in treatment. Time series features were entered into logistic regression models to derive DEWS for each of the clinical outcomes. Area under the receiver operating characteristic curve (AUROC) was the primary measure of model accuracy. Results: AUROC (95% confidence interval) for predicting D/ICU was 0.857 (0.852–0.862) for NEWS-2 and 0.906 (0.899–0.914) for DEWS in the validation data. AUROC for predicting CSD was 0.829 (0.817–0.842) for NEWS-2 and 0.877 (0.862–0.892) for DEWS. NEWS-2 ≥ 5 had sensitivity of 88.2% and specificity of 54.2% for predicting CSD, while DEWS ≥ 0.021 had higher sensitivity of 93.6% and approximately the same specificity of 54.3% for the same outcome. Using these cut-offs, 315 out of 347 (90.8%) CSD events were detected by both NEWS-2 and DEWS, at the time of the event or within the previous 4h; 12 (3.5%) were detected by DEWS but not by NEWS-2, while 4 (1.2%) were detected by NEWS-2 but not by DEWS; 16 (4.6%) were not detected by either scoring system. Conclusion: We have developed DEWS that display greater accuracy than NEWS-2 for predicting clinical deterioration events in patients with respiratory disease. Prospective validation studies are required to assess whether DEWS can be used to reduce missed deteriorations and false alarms in real-life clinical settings.
Citation
Gonem, S., Taylor, A., Figueredo, G., Forster, S., Quinlan, P., Garibaldi, J. M., McKeever, T. M., & Shaw, D. (2022). Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease. Respiratory Research, 23, Article 203. https://doi.org/10.1186/s12931-022-02130-6
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 31, 2022 |
Online Publication Date | Aug 11, 2022 |
Publication Date | Aug 11, 2022 |
Deposit Date | Jun 17, 2024 |
Publicly Available Date | Aug 14, 2024 |
Journal | Respiratory Research |
Electronic ISSN | 1465-9921 |
Publisher | Springer Verlag |
Peer Reviewed | Peer Reviewed |
Volume | 23 |
Article Number | 203 |
DOI | https://doi.org/10.1186/s12931-022-02130-6 |
Keywords | Research, Early warning score, Risk prediction, Clinical deterioration |
Public URL | https://nottingham-repository.worktribe.com/output/10072583 |
Publisher URL | https://respiratory-research.biomedcentral.com/articles/10.1186/s12931-022-02130-6 |
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Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
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