OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation. DESIGN: Retrospective cohort study. SETTING: This study was conducted at a hospital in which deep learning-based early warning system was implemented. PATIENTS: We reviewed the records of adult patients who were admitted to the general ward of our hospital from April 2018 to March 2019. INTERVENTIONS: The study population included 8,039 adult patients. A total 83 events of deterioration occurred during the study period. The outcome was events of deterioration, defined as cardiac arrest and unexpected ICU admission. We defined a true alarm as an alarm occurring within 0.5-24 hours before a deteriorating event. MEASUREMENTS AND MAIN RESULTS: We used the area under the receiver operating characteristic curve, area under the precision-recall curve, number needed to examine, and mean alarm count per day as comparative measures. The deep learning-based early warning system (area under the receiver operating characteristic curve, 0.865; area under the precision-recall curve, 0.066) outperformed the modified early warning score (area under the receiver operating characteristic curve, 0.682; area under the precision-recall curve, 0.010) and reduced the number needed to examine and mean alarm count per day by 69.2% and 59.6%, respectively. At the same specificity, deep learning-based early warning system had up to 257% higher sensitivity than conventional methods. CONCLUSIONS: The developed artificial intelligence based on deep-learning, deep learning-based early warning system, accurately predicted deterioration of patients in a general ward and outperformed conventional methods. This study showed the potential and effectiveness of artificial intelligence in an rapid response system, which can be applied together with electronic health records. This will be a useful method to identify patients with deterioration and help with precise decision-making in daily practice.
OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning-based early warning system. The purpose of this study was to compare the performance of an artificial intelligence-based early warning system with that of conventional methods in a real hospital situation. DESIGN: Retrospective cohort study. SETTING: This study was conducted at a hospital in which deep learning-based early warning system was implemented. PATIENTS: We reviewed the records of adult patients who were admitted to the general ward of our hospital from April 2018 to March 2019. INTERVENTIONS: The study population included 8,039 adult patients. A total 83 events of deterioration occurred during the study period. The outcome was events of deterioration, defined as cardiac arrest and unexpected ICU admission. We defined a true alarm as an alarm occurring within 0.5-24 hours before a deteriorating event. MEASUREMENTS AND MAIN RESULTS: We used the area under the receiver operating characteristic curve, area under the precision-recall curve, number needed to examine, and mean alarm count per day as comparative measures. The deep learning-based early warning system (area under the receiver operating characteristic curve, 0.865; area under the precision-recall curve, 0.066) outperformed the modified early warning score (area under the receiver operating characteristic curve, 0.682; area under the precision-recall curve, 0.010) and reduced the number needed to examine and mean alarm count per day by 69.2% and 59.6%, respectively. At the same specificity, deep learning-based early warning system had up to 257% higher sensitivity than conventional methods. CONCLUSIONS: The developed artificial intelligence based on deep-learning, deep learning-based early warning system, accurately predicted deterioration of patients in a general ward and outperformed conventional methods. This study showed the potential and effectiveness of artificial intelligence in an rapid response system, which can be applied together with electronic health records. This will be a useful method to identify patients with deterioration and help with precise decision-making in daily practice.
Authors: Franck Verdonk; Dorien Feyaerts; Rafael Badenes; Julie A Bastarache; Adrien Bouglé; Wesley Ely; Brice Gaudilliere; Christopher Howard; Katarzyna Kotfis; Alexandre Lautrette; Matthieu Le Dorze; Babith Joseph Mankidy; Michael A Matthay; Christopher K Morgan; Aurélien Mazeraud; Brijesh V Patel; Rajyabardhan Pattnaik; Jean Reuter; Marcus J Schultz; Tarek Sharshar; Gentle S Shrestha; Charles Verdonk; Lorraine B Ware; Romain Pirracchio; Matthieu Jabaudon Journal: Anaesth Crit Care Pain Med Date: 2022-06-30 Impact factor: 7.025
Authors: Z Yuan; B Zhou; S Meng; J Jiang; S Huang; X Lu; N Wu; Z Xie; J Deng; X Chen; J Liu; J Zhang; F Wu; H Liang; L Ye Journal: Epidemiol Infect Date: 2020-04-01 Impact factor: 2.451