Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System

Abstract

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.

Abnormal Vital Signs Predict Critical Deterioration in Hospitalized Pediatric Hematology-Oncology and Post-hematopoietic Cell Transplant Patients

“…Our data suggest that systems to improve early identification of deterioration and vital sign changes, including PEWS, may also improve outcomes in these high-risk patients, despite conflicting evidence on the impact of PEWS in general pediatric settings with lower baseline rates of deterioration and mortality (19). Track-andtrigger systems (TTS) based on frequent assessment for and identification of abnormal vital signs in hospitalized children, such as multi-component rule-based systems designed around a scoring tool including vital signs (like PEWS), or more complex machine-learning systems based on the electronic medical record documented vital signs and other clinical data (20)(21)(22), may aid in better identification of patients at risk for deterioration and support clinical decision making around their disposition, particularly in this patient population. Such multi-component systems have consistently been shown to out-perform singleparameter TTS (20,23) at identifying patients with deterioration, and our study suggests these findings hold true in pediatric hematology-oncology patients.…”

“…Track-andtrigger systems (TTS) based on frequent assessment for and identification of abnormal vital signs in hospitalized children, such as multi-component rule-based systems designed around a scoring tool including vital signs (like PEWS), or more complex machine-learning systems based on the electronic medical record documented vital signs and other clinical data (20)(21)(22), may aid in better identification of patients at risk for deterioration and support clinical decision making around their disposition, particularly in this patient population. Such multi-component systems have consistently been shown to out-perform singleparameter TTS (20,23) at identifying patients with deterioration, and our study suggests these findings hold true in pediatric hematology-oncology patients. Importantly, such systems must be associated with a robust response algorithm to assure identified abnormal vital signs are appropriately assessed and managed by the medical team.…”

Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

“…Horng et al demonstrated that accurate triggering of clinical decision support will become increasingly more important as clinical decision support becomes more integrated into EMRs [27]. Cho et al reported that a deep learning-based early warning system, which can be applied with EMRs, accurately predicted the deterioration of patients [41]. We suggest that our qSOFA-based machine-learning model incorporated with real-time clinical variables on the EMR can be utilized by physicians for making clinical decisions for treating sepsis.…”

Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography

“…Several track and trigger systems (TTSs) using discrete numeric values such as vital signs and laboratory results are used in RRSs [9,10]. As conventional TTSs have limitations in detecting deterioration in patients, several researchers have adopted deep learning based algorithms to deal with these numeric values, which performed better than conventional tools [11][12][13][14][15]. However, the performances of these novel TTSs were also not satisfactory, and further improvement is needed to use the algorithms with electrical health records.…”