Improved inpatient deterioration detection in general wards by using time-series vital signs

Su, Chang-Fu; Chiu, Shu-I; Jang, Jyh-Shing Roger; Lai, Feipei

doi:10.1038/s41598-022-16195-2

Cited by 6 publications

(4 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, lack of demographic data and time lags between events and laboratory tests can lower their predictive performance and make them difficult to apply in real-world settings. In 2022, a time-series early warning score (TEWS) for predicting IHCA using only basic vital signs was validated [ 21 ]. The predictive performance of the TEWS for IHCA was superior to that of the MEWS.…”

Section: Discussionmentioning

confidence: 99%

Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards

Cho,

Kim,

Lee

et al. 2023

Crit Care

View full text Add to dashboard Cite

Background Retrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice. Methods This prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems. Results Among 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems. Conclusion The DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov (NCT04951973) on June 30, 2021.

show abstract

Section: Discussionmentioning

confidence: 99%

Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards

Cho,

Kim,

Lee

et al. 2023

Crit Care

View full text Add to dashboard Cite

show abstract

“…The study data set [21] included the EHRs of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least 2 to 3 times per day during the day, night, and early morning.…”

Section: System Implementationmentioning

confidence: 99%

“…Then, the machine learning pipeline exports the vital signs data from the Observation resource to the FHIR server. We integrated a long short-term memory network-based model [21] using vital signs data to predict IHCA. It used the time series early warning score, which used heart rate, systolic blood pressure, and respiratory data.…”

Section: System Implementationmentioning

confidence: 99%

“…In this paper, we piloted the use of an implementation guide that combines IHCA with vital signs, which have been widely adopted in IHCA assessment [4,21] and play an important role in inpatient deterioration detection. Many health care institutions have developed early warning score systems to identify hospitalized patients that are at risk of deterioration, and in recent years, they have begun to incorporate machine learning-based models into this process.…”

Section: Principal Findingsmentioning

confidence: 99%

See 1 more Smart Citation

Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study

Tseng¹,

Su²,

Lai³

2022

JMIR Med Inform

View full text Add to dashboard Cite

Background Vital signs have been widely adopted in in-hospital cardiac arrest (IHCA) assessment, which plays an important role in inpatient deterioration detection. As the number of early warning systems and artificial intelligence applications increases, health care information exchange and interoperability are becoming more complex and difficult. Although Health Level 7 Fast Healthcare Interoperability Resources (FHIR) have already developed a vital signs profile, it is not sufficient to support IHCA applications or machine learning–based models. Objective In this paper, for IHCA instances with vital signs, we define a new implementation guide that includes data mapping, a system architecture, a workflow, and FHIR applications. Methods We interviewed 10 experts regarding health care system integration and defined an implementation guide. We then developed the FHIR Extract Transform Load to map data to FHIR resources. We also integrated an early warning system and machine learning pipeline. Results The study data set includes electronic health records of adult inpatients who visited the En-Chu-Kong hospital. Medical staff regularly measured these vital signs at least 2 to 3 times per day during the day, night, and early morning. We used pseudonymization to protect patient privacy. Then, we converted the vital signs to FHIR observations in the JSON format using the FHIR Extract Transform Load application. The measured vital signs include systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature. According to clinical requirements, we also extracted the electronic health record information to the FHIR server. Finally, we integrated an early warning system and machine learning pipeline using the FHIR RESTful application programming interface. Conclusions We successfully demonstrated a process that standardizes health care information for inpatient deterioration detection using vital signs. Based on the FHIR definition, we also provided an implementation guide that includes data mapping, an integration process, and IHCA assessment using vital signs. We also proposed a clarifying system architecture and possible workflows. Based on FHIR, we integrated the 3 different systems in 1 dashboard system, which can effectively solve the complexity of the system in the medical staff workflow.

show abstract

Comparison of time-series models for predicting physiological metrics under sedation

Tu,

Jeffries,

Morse

et al. 2024

J Clin Monit Comput

View full text Add to dashboard Cite

Improved inpatient deterioration detection in general wards by using time-series vital signs

Cited by 6 publications

References 39 publications

Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards

Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards

Fast Healthcare Interoperability Resources for Inpatient Deterioration Detection With Time-Series Vital Signs: Design and Implementation Study

Comparison of time-series models for predicting physiological metrics under sedation

Contact Info

Product

Resources

About