scholarly journals Incorporating Laboratory Values Into a Machine Learning Model Improves In-Hospital Mortality Predictions After Rapid Response Team Call

2019 ◽  
Vol 1 (7) ◽  
pp. e0023
Author(s):  
Peter M. Reardon ◽  
Enea Parimbelli ◽  
Szymon Wilk ◽  
Wojtek Michalowski ◽  
Kyle Murphy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Rapid Response Team ◽  
Learning Model ◽  
Rapid Response ◽  
Laboratory Values ◽  
Machine Learning Model ◽  
Response Team

A novel configuration of a traditional rapid response team decreases non-intensive care unit arrests and overall hospital mortality

2015 ◽  
Vol 10 (6) ◽  
pp. 352-357 ◽  
Author(s):  
Daniel P. Davis ◽  
Steve A. Aguilar ◽  
Patricia G. Graham ◽  
Brenna Lawrence ◽  
Rebecca E. Sell ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Rapid Response Team ◽  
Rapid Response ◽  
Response Team

Abstract 288: Rapid Response Team Implementation Associated with Reductions in In-hospital Mortality Rate and Hospitalization Costs: A Retrospective Analysis

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_4) ◽  
Author(s):  
Jacob Sessim Filho ◽  
Renato P Azevedo ◽  
Antonildes N Assuncao ◽  
Marcia M Sa ◽  
Felipe D Silva ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Hospital Mortality ◽  
Statistical Power ◽  
Early Recognition ◽  
Rapid Response Team ◽  
Rapid Response ◽  
Clinical Deterioration ◽  
Medical Database ◽  
Hospitalization Costs ◽  
Response Team

Introduction: Early recognition of clinical deterioration in inpatient subjects seems to be one of the main factors associated with prevention of in-hospital severe adverse events occurrence. Previous studies failed to demonstrate that the implementation of a rapid response team (RRT) could reduce in-hospital mortality rate. Hypothesis: Could a RRT implementation reduces in-hospital mortality and/or hospitalizations costs in a private general hospital in Brazil? Methods: This is a retrospective cohort built from data of electronic medical database of consecutive adult inpatients admitted to general wards who had to be transferred to an ICU after an acute clinical deterioration between May 1st, 2012 and June 30th, 2016. Subjects were divided into two groups as follows: group 1 (G1) with those admitted to ICU before RRT implementation on June 1st, 2014 and group 2 (G2) with the ones admitted to ICU after the implementation. All patients in G2 received care by the RRT before ICU admittance. In cases in which a patient had more than one hospital admission, only the first admittance was used for analyses. Results: Patients data are shown in table 1.Outcome data are shown in table 2. Conclusions: From these data, it is possible to infer that this RRT implementation at this hospital was associated with improvement in clinical outcomes of inpatients who needed an ICU admittance after an acute clinical deterioration, as well as a significant reduction of their hospitalization costs. These data reinforce the hypotheses that MERIT study was underpowered. Further multicenter randomized trials, with appropriate statistical power, shall be proposed to address these questions.

scholarly journals Reduced frequency of cardiopulmonary arrests by rapid response teams

2012 ◽  
Vol 10 (4) ◽  
pp. 442-448 ◽  
Author(s):  
Paulo David Scatena Gonçales ◽  
Joyce Assis Polessi ◽  
Lital Moro Bass ◽  
Gisele de Paula Dias Santos ◽  
Paula Kiyomi Onaga Yokota ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Rapid Response Team ◽  
Rapid Response ◽  
High Complexity ◽  
Rapid Response Teams ◽  
Cardiorespiratory Arrest ◽  
Reduced Frequency ◽  
Before And After ◽  
Response Team ◽  
The Impact

OBJECTIVE: To evaluate the impact of the implementation of a rapid response team on the rate of cardiorespiratory arrests in mortality associated with cardiorespiratory arrests and on in-hospital mortality in a high complexity general hospital. METHODS: A retrospective analysis of cardiorespiratory arrests and in-hospital mortality events before and after implementation of a rapid response team. The period analyzed covered 19 months before intervention by the team (August 2005 to February 2007) and 19 months after the intervention (March 2007 to September 2008). RESULTS: During the pre-intervention period, 3.54 events of cardiorespiratory arrest/1,000 discharges and 16.27 deaths/1,000 discharges were noted. After the intervention, there was a reduction in the number of cardiorespiratory arrests and in the rate of in-hospital mortality; respectively, 1.69 events of cardiorespiratory arrest/1,000 discharges (p<0.001) and 14.34 deaths/1,000 discharges (p=0.029). CONCLUSION: The implementation of the rapid response team may have caused a significant reduction in the number of cardiorespiratory arrests. It was estimated that during the period from March 2007 to September 2008, the intervention probably saved 67 lives.

IMPROVED HOSPITAL MORTALITY BY INSTITUTION OF A RAPID RESPONSE TEAM IN A UNIVERSITY HOSPITAL

CHEST Journal ◽  
2005 ◽  
Vol 128 (4) ◽  
pp. 182S ◽  
Author(s):  
Alexis Meredith ◽  
Steven Q. Simpson ◽  
Carol Cleek ◽  
Timothy Williamson ◽  
Amy O’Brien-Ladner
Keyword(s):  
Hospital Mortality ◽  
Rapid Response Team ◽  
Rapid Response ◽  
University Hospital ◽  
Response Team

scholarly journals Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Emirena Garrafa ◽  
Marika Vezzoli ◽  
Marco Ravanelli ◽  
Davide Farina ◽  
Andrea Borghesi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Learning Model ◽  
Hospital Death ◽  
Web Based ◽  
X Ray ◽  
Machine Learning Model ◽  
Chest X Ray ◽  
Blood Analytes ◽  
Second Wave

An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validate using a Machine-Learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

scholarly journals Prospective and External Evaluation of a Machine Learning Model to Predict In-Hospital Mortality of Adults at Time of Admission

2020 ◽  
Vol 3 (2) ◽  
pp. e1920733 ◽  
Author(s):  
Nathan Brajer ◽  
Brian Cozzi ◽  
Michael Gao ◽  
Marshall Nichols ◽  
Mike Revoir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Learning Model ◽  
External Evaluation ◽  
Machine Learning Model ◽  
Time Of Admission
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