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Published on: January 16, 2019
Predictive factors for intensive care unit transfer after rapid response system activation: A multicentre study in
Miho Tokito1, Shigehiko Uchino2, Shohei Ono3
1Department of Anesthesiology and Critical Care Medicine, Jichi Medical University Saitama Medical Center, Saitama, Japan; Nurse Practitioner Management Room, Tokyo Medical University Hospital, Tokyo, Japan.
Objective:
The aim of this study was to identify factors that predict admission to the intensive care unit (ICU) after activation of a rapid response system (RRS).
Methods:
We conducted a retrospective observational study using data from 12,306 RRS activations recorded in the In-Hospital Emergency Registry in Japan database between November 2017 and September 2023. Patients aged under 18 years, noninpatients, and those who died or were transferred immediately after RRS activation were excluded. The primary outcome was ICU admission after RRS activation. Predictive factors were identified using multivariable logistic regression models: Model 1 included all available data, while model 2 was restricted to data available at the time of RRS activation.
Results:
We analysed data from 8532 patients; 2298 (26.9%) were admitted to the ICU following RRS activation. Significant factors of ICU admission in model 1 included weekend activation (odds ratio [OR] = 1.17; 95% confidence interval [CI] = 1.02, 1.34), oxygen administration prior to activation (OR = 1.23; 95% CI = 1.08, 1.4), ICU discharge within 72 h before the index event (OR = 1.65; 95% CI = 1.28, 2.11), physician-initiated activation (OR = 2.16; 95% CI = 1.87, 2.50), and multiple abnormal vital signs. Model 2, which was limited to information available at the time of RRS activation, identified a similar pattern of associations.
Conclusion:
This study identified several important factors associated with ICU admission following RRS activation. These findings may support improved clinical decision-making regarding ICU transfers and provide a foundation for future work to develop and validate prediction models tailored to this setting.
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