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内容記述 |
Although the rapid response system (RRS) is a standard system for preventing: adverse event, low activation rate and high mortality were reported in Japan. National: Early Warning score (NEWS) is expected as one of the solutions, but it is not: validated in he Japanese population. This study aimed to validate NEWS among: Japanese population.: Methods: This was retrospective observational study. Adult patients registered in the: In-Hospital Emergency Registry in Japan between January 2014 and March 2018: were eligible for this study. The primary outcome was the mortality rate at after 30: days of RRS activation. First, accuracy of NEWS was analyzed with the correlation: coefficient and area under the receiver operating characteristics curve (AUC). Second,: NEWS parameters were analyzed for its weight for prediction, using multiple logistic: regression and CART (Classification and regression trees, a machine learning: method).: Results: There were 2,255 cases from 33 facilities included for this study. Correlation: coefficient of NEWS for 30 days mortality was 0.95 (95%CI, 0.88-0.98) and AUC: 3: was 0.668 (95%CI, 0.642-0.693). Sensitivity and specificity with seven as cut-off: score were 89.8% and 45.1%, respectively. In terms of the prediction value of the: parameters, oxygen saturation showed highest odds ratio of 1.36 (95%CI, 1.25-1.48),: followed by altered mental status (AMS) 1.23 (95%CI, 1.14-1.32), heart rate 1.21: (95%CI, 1.09-1.34), systolic blood pressure 1.12 (95%CI, 1.04-1.22), respiratory rate: 1.03 (95%CI, 1.05-1.26). Body temperature and oxygen supplement were not: significantly associated. CART showed that oxygen saturation was the most weighted: parameter, followed by AMS, and respiratory rate.: Conclusions: NEWS could stratify the risk of 30 days mortality after RRS activation in Japanese: population. Oxygen saturation was most weighted parameter for predicting cardiac: arrest. This work was supported by JSPS KAKENHI (#JP18K16548).: Keywords: in-hospital cardiac arrest, medical emergency team, rapid response team,: rapid response system, national early warning score, statistical evaluation, machine: learning |
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論文要旨 (79.3 kB)
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