Efficacy of early warning score systems as prediction tools for detecting critically ill patients in an outpatient setting

Abstract

No systematic methods exist for triaging outpatients with serious conditions. Our previous pilot study showed that the National Early Warning Score (NEWS) could predict admissions and unexpected intensive care unit (ICU) transfers in rapid response system-activated outpatients. The Visensia Score Index (VSI) is another artificial intelligence-based Early Warning Score Systems that automatically collates and analyzes data from bedside monitors. This single-center retrospective cohort study aimed to investigate and compare efficacy of NEWS and VSI as a prediction tool among whole first-visit patients of our internal medicine clinic. From June 1, 2018 to November 30, 2018 at a 350-bed teaching community hospital in Japan. Patient age and sex, and physiological measurements, NEWS, VSI as well as disposition and outcomes were collected. This study included 3301 patients. There were 108 (3.3%), 16 (0.5%), and 5 (0.2%) patients admitted to the general ward, high dependency unit (HDU), and ICU, respectively. The areas under the curve (AUCs) of the NEWS for hospital admission, HDU or ICU admission, and ICU admission were 0.71 (95% CI, 0.66–0.76), 0.88 (95% CI, 0.80–0.97), and 1.00 (95% CI, 0.996–1.0), respectively. The AUCs of the VSI for admission, HDU or ICU admission, and ICU admission were 0.66 (95% CI, 0.60–0.71), 0.82 (95% CI, 0.71–0.93), and 0.97 (95% CI, 0.96–0.98), respectively. The AUC of the NEWS was significantly superior to that of the VSI for hospital (p = 0.03) and ICU admission (p < 0.01). The NEWS could triage patients with serious conditions in an outpatient setting.