Open AccessDevelopment and validation of early warning score systems for COVID‐19 patients
Author(s) -
Youssef Alexey,
Kouchaki Samaneh,
Shamout Farah,
Armstrong Jacob,
ElBouri Rasheed,
Taylor Thomas,
Birrenkott Drew,
Vasey Baptiste,
Soltan Andrew,
Zhu Tingting,
Clifton David A.,
Eyre David W.
Publication year - 2021
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl2.12009
Subject(s) - covid-19 , medicine , intubation , intensive care medicine , warning system , early warning score , emergency medicine , gradient boosting , vital signs , computer science , random forest , machine learning , disease , infectious disease (medical specialty) , surgery , telecommunications
COVID‐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 November 2020. Predicting which patients will need respiratory support is important to guiding individual patient treatment and also to ensuring sufficient resources are available. 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 h is evaluated. It is shown that these scores perform sub‐optimally at this specific task. Therefore, an alternative EWS based on the Gradient Boosting Trees (GBT) algorithm is developed that is able to predict deterioration within the next 24 h with high AUROC 94% and an accuracy, sensitivity, and specificity of 70%, 96%, 70%, respectively. The 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.