A machine learning model for predicting short‐term outcomes after rapid response system activation
Maintaining rapid response team (RRT) response quality is difficult. A system that supports RRT assessment could potentially contribute to medical safety. Although rapid response system (RRS) triggers have been well-studied, studies on the prediction models of short-term prognosis after RRS activati...
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| Veröffentlicht in: | Acute medicine & surgery Jg. 12; H. 1; S. e70083 |
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John Wiley & Sons, Inc
12.08.2025
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| Abstract | Maintaining rapid response team (RRT) response quality is difficult. A system that supports RRT assessment could potentially contribute to medical safety. Although rapid response system (RRS) triggers have been well-studied, studies on the prediction models of short-term prognosis after RRS activation are scarce. We aimed to develop a model to predict short-term outcomes after RRS activation using machine learning.
This retrospective cohort study used the In-Hospital Emergency Registry in Japan, a multicentre RRS online registry. We collected data on patient demographics, treatment before RRS, RRT calls, and physiological parameters. The outcome was death within 24 h after RRS calls or unplanned transfers to an intensive care unit. To develop the eXtreme Gradient Boosted Tree Classifier (XGB) and Random Forest (RF) algorithms, a logistic regression (LR) algorithm was used. For model comparison, receiver-operating area under the curve (AUC) was evaluated and compared with those of the National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS).
5414 cases were included in the study. The outcome occurred in 28.4% of the cases. The XGB model showed the highest AUC (0.798) compared to the RF model (0.796), LR model (0.785), NEWS (0.696), and MEWS (0.660). The most weighted feature in the XGB model was doctor activation, followed by hypotension as the activation criteria and usage of oxygen.
We developed the first machine learning model for short-term prognosis after RRS. This model has the potential to support decision-making by RRT. |
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| AbstractList | Maintaining rapid response team (RRT) response quality is difficult. A system that supports RRT assessment could potentially contribute to medical safety. Although rapid response system (RRS) triggers have been well-studied, studies on the prediction models of short-term prognosis after RRS activation are scarce. We aimed to develop a model to predict short-term outcomes after RRS activation using machine learning.
This retrospective cohort study used the In-Hospital Emergency Registry in Japan, a multicentre RRS online registry. We collected data on patient demographics, treatment before RRS, RRT calls, and physiological parameters. The outcome was death within 24 h after RRS calls or unplanned transfers to an intensive care unit. To develop the eXtreme Gradient Boosted Tree Classifier (XGB) and Random Forest (RF) algorithms, a logistic regression (LR) algorithm was used. For model comparison, receiver-operating area under the curve (AUC) was evaluated and compared with those of the National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS).
5414 cases were included in the study. The outcome occurred in 28.4% of the cases. The XGB model showed the highest AUC (0.798) compared to the RF model (0.796), LR model (0.785), NEWS (0.696), and MEWS (0.660). The most weighted feature in the XGB model was doctor activation, followed by hypotension as the activation criteria and usage of oxygen.
We developed the first machine learning model for short-term prognosis after RRS. This model has the potential to support decision-making by RRT. In this study, we developed a machine learning model for short‐term prognosis after RRS activation. The XGBoost model showed better prediction than widely used EWSs. The most weighted feature in the XGB model was doctor activation, followed by hypotension as the activation criteria and usage of oxygen. Maintaining rapid response team (RRT) response quality is difficult. A system that supports RRT assessment could potentially contribute to medical safety. Although rapid response system (RRS) triggers have been well-studied, studies on the prediction models of short-term prognosis after RRS activation are scarce. We aimed to develop a model to predict short-term outcomes after RRS activation using machine learning.AimMaintaining rapid response team (RRT) response quality is difficult. A system that supports RRT assessment could potentially contribute to medical safety. Although rapid response system (RRS) triggers have been well-studied, studies on the prediction models of short-term prognosis after RRS activation are scarce. We aimed to develop a model to predict short-term outcomes after RRS activation using machine learning.This retrospective cohort study used the In-Hospital Emergency Registry in Japan, a multicentre RRS online registry. We collected data on patient demographics, treatment before RRS, RRT calls, and physiological parameters. The outcome was death within 24 h after RRS calls or unplanned transfers to an intensive care unit. To develop the eXtreme Gradient Boosted Tree Classifier (XGB) and Random Forest (RF) algorithms, a logistic regression (LR) algorithm was used. For model comparison, receiver-operating area under the curve (AUC) was evaluated and compared with those of the National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS).MethodsThis retrospective cohort study used the In-Hospital Emergency Registry in Japan, a multicentre RRS online registry. We collected data on patient demographics, treatment before RRS, RRT calls, and physiological parameters. The outcome was death within 24 h after RRS calls or unplanned transfers to an intensive care unit. To develop the eXtreme Gradient Boosted Tree Classifier (XGB) and Random Forest (RF) algorithms, a logistic regression (LR) algorithm was used. For model comparison, receiver-operating area under the curve (AUC) was evaluated and compared with those of the National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS).5414 cases were included in the study. The outcome occurred in 28.4% of the cases. The XGB model showed the highest AUC (0.798) compared to the RF model (0.796), LR model (0.785), NEWS (0.696), and MEWS (0.660). The most weighted feature in the XGB model was doctor activation, followed by hypotension as the activation criteria and usage of oxygen.Results5414 cases were included in the study. The outcome occurred in 28.4% of the cases. The XGB model showed the highest AUC (0.798) compared to the RF model (0.796), LR model (0.785), NEWS (0.696), and MEWS (0.660). The most weighted feature in the XGB model was doctor activation, followed by hypotension as the activation criteria and usage of oxygen.We developed the first machine learning model for short-term prognosis after RRS. This model has the potential to support decision-making by RRT.ConclusionsWe developed the first machine learning model for short-term prognosis after RRS. This model has the potential to support decision-making by RRT. Aim Maintaining rapid response team (RRT) response quality is difficult. A system that supports RRT assessment could potentially contribute to medical safety. Although rapid response system (RRS) triggers have been well‐studied, studies on the prediction models of short‐term prognosis after RRS activation are scarce. We aimed to develop a model to predict short‐term outcomes after RRS activation using machine learning. Methods This retrospective cohort study used the In‐Hospital Emergency Registry in Japan, a multicentre RRS online registry. We collected data on patient demographics, treatment before RRS, RRT calls, and physiological parameters. The outcome was death within 24 h after RRS calls or unplanned transfers to an intensive care unit. To develop the eXtreme Gradient Boosted Tree Classifier (XGB) and Random Forest (RF) algorithms, a logistic regression (LR) algorithm was used. For model comparison, receiver‐operating area under the curve (AUC) was evaluated and compared with those of the National Early Warning Score (NEWS) and Modified Early Warning Score (MEWS). Results 5414 cases were included in the study. The outcome occurred in 28.4% of the cases. The XGB model showed the highest AUC (0.798) compared to the RF model (0.796), LR model (0.785), NEWS (0.696), and MEWS (0.660). The most weighted feature in the XGB model was doctor activation, followed by hypotension as the activation criteria and usage of oxygen. Conclusions We developed the first machine learning model for short‐term prognosis after RRS. This model has the potential to support decision‐making by RRT. |
| Author | Naito, Takaki Fujitani, Shigeki Li, Micheal |
| AuthorAffiliation | 1 Enterprise Analytics Thomas Jefferson University Hospital Philadelphia Pennsylvania USA 2 Department of Emergency and Critical Care Medicine St. Marianna University School of Medicine Kawasaki‐shi Kanagawa Japan |
| AuthorAffiliation_xml | – name: 2 Department of Emergency and Critical Care Medicine St. Marianna University School of Medicine Kawasaki‐shi Kanagawa Japan – name: 1 Enterprise Analytics Thomas Jefferson University Hospital Philadelphia Pennsylvania USA |
| Author_xml | – sequence: 1 givenname: Takaki orcidid: 0000-0001-8333-0141 surname: Naito fullname: Naito, Takaki organization: Enterprise Analytics Thomas Jefferson University Hospital Philadelphia Pennsylvania USA, Department of Emergency and Critical Care Medicine St. Marianna University School of Medicine Kawasaki‐shi Kanagawa Japan – sequence: 2 givenname: Micheal orcidid: 0000-0003-0351-2637 surname: Li fullname: Li, Micheal organization: Enterprise Analytics Thomas Jefferson University Hospital Philadelphia Pennsylvania USA – sequence: 3 givenname: Shigeki orcidid: 0000-0001-6327-682X surname: Fujitani fullname: Fujitani, Shigeki organization: Department of Emergency and Critical Care Medicine St. Marianna University School of Medicine Kawasaki‐shi Kanagawa Japan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40800661$$D View this record in MEDLINE/PubMed |
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| Snippet | Maintaining rapid response team (RRT) response quality is difficult. A system that supports RRT assessment could potentially contribute to medical safety.... Aim Maintaining rapid response team (RRT) response quality is difficult. A system that supports RRT assessment could potentially contribute to medical safety.... In this study, we developed a machine learning model for short‐term prognosis after RRS activation. The XGBoost model showed better prediction than widely used... |
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| Title | A machine learning model for predicting short‐term outcomes after rapid response system activation |
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