RT Journal Article
SR Electronic
T1 Predicting outcome in acute respiratory admissions using patterns of National Early Warning Scores
JF Clinical Medicine
JO Clin Med
FD Royal College of Physicians
SP 409
OP 415
DO 10.7861/clinmed.2022-0074
VO 22
IS 5
A1 Sarah Forster
A1 Tricia M McKeever
A1 Matthew Churpek
A1 Sherif Gonem
A1 Dominick Shaw
YR 2022
UL http://www.rcpjournals.org/content/22/5/409.abstract
AB 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.