Predicting outcome in acute respiratory admissions using patterns of National Early Warning Scores

Forster, Sarah; McKeever, Tricia M.; Churpek, Matthew; Gonem, Sherif; Shaw, Dominick

Abstract

Aims Accurately predicting risk of patient deterioration is vital. Altered physiology in chronic disease affects the prognostic ability of vital signs based early warning score systems. We aimed to assess the potential of early warning score patterns to improve outcome prediction in patients with respiratory disease. Methods Patients admitted under respiratory medicine between April 2015 and March 2017 had their National Early Warning Score 2 (NEWS2) calculated retrospectively from vital sign observations. Prediction models (including temporal patterns) were constructed and assessed for ability to predict death within 24 hours using all observations collected not meeting exclusion criteria. The best performing model was tested on a validation cohort of admissions from April 2017 to March 2019. Results The derivation cohort comprised 7,487 admissions and the validation cohort included 8,739 admissions. Adding the maximum score in the preceding 24 hours to the most recently recorded NEWS2 improved area under the receiver operating characteristic curve for death in 24 hours from 0.888 (95% confidence interval (CI) 0.881-0.895) to 0.902 (95% CI 0.895-0.909) in the overall respiratory population. Conclusion Combining the most recently recorded score and the maximum NEWS2 score from the preceding 24 hours demonstrated greater accuracy than using snapshot NEWS2. This simple inclusion of a scoring pattern should be considered in future iterations of early warning scoring systems.

Citation

Forster, S., McKeever, T. M., Churpek, M., Gonem, S., & Shaw, D. (2022). Predicting outcome in acute respiratory admissions using patterns of National Early Warning Scores. Clinical Medicine, 22(5), 409-415. https://doi.org/10.7861/clinmed.2022-0074

• Journal Article Type: Article
• Acceptance Date: Jun 19, 2022
• Online Publication Date: Sep 23, 2022
• Publication Date: Sep 1, 2022
• Deposit Date: Jul 6, 2022
• Publicly Available Date: Sep 1, 2022
• Journal: Clinical Medicine, Journal of the Royal College of Physicians of London
• Print ISSN: 1470-2118
• Electronic ISSN: 1473-4893
• Publisher: Royal College of Physicians
• Peer Reviewed: Peer Reviewed
• Volume: 22
• Issue: 5
• Pages: 409-415
• DOI: https://doi.org/10.7861/clinmed.2022-0074
• Keywords: General Medicine
• Public URL: https://nottingham-repository.worktribe.com/output/8851762
• Publisher URL: https://www.rcpjournals.org/content/clinmedicine/22/5/409