The Korean Triage and Acuity Scale: associations with admission, disposition, mortality and length of stay in the emergency department

Kwon, Hyuksool; Kim, Yu Jin; Jo, You Hwan; Lee, Jae Hyuk; Lee, Jin Hee; Kim, Joonghee; Hwang, Ji Eun; Jeong, Joo; Choi, Yoomi

doi:10.1093/intqhc/mzy184

Cited by 53 publications

(41 citation statements)

References 13 publications

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“…The level of acuity was measured using the Korean Triage and Acuity Scale (KTAS). The KTAS is a five-level triage scale (Level 1 being most severe), which was developed and implemented in all EDs in South Korea since 2015 [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Effectiveness of negative pressure isolation stretcher and rooms for SARS-CoV-2 nosocomial infection control and maintenance of South Korean emergency department capacity

Kim

Kong

Park

et al. 2021

The American Journal of Emergency Medicine

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Objective There are growing concerns regarding the lack of COVID-19 pandemic response capacity in already overwhelmed emergency departments (EDs), and lack of proper isolation facilities. This study evaluated the effectiveness of the negative pressure isolation stretcher (NPIS) and additional negative pressure isolation rooms (NPIRs) on the maintenance of emergency care capacity during the COVID-19 outbreak. Methods A before and after intervention study was performed between February 27, 2020 and March 31, 2020 at the ED of Chungbuk National University Hospital, Cheongju, South Korea. A total of 2455 patients who visited the ED during the study period were included. Interventions included the introduction of the NPIS and additional NPIRs in the ED. The main outcome of the study was frequency of medical cessation. Secondary outcomes were the average number of ED visits and lengths of stay. Results After the intervention, average frequency of medical cessation was significantly decreased from 1.6 times per day (range 0–4) in the pre-intervention period to 0.6 times per day (range 0–3) in the post-intervention period ( p -value <0.01). On the other hand, the number of patients visiting the ED increased significantly from 67.2 persons per day (range 58–79) pre-intervention to 76.3 persons per day (range 61–88) post-intervention ( p value <0.01). However, there were no statistically significant differences in the average ED length of stay across the study phases (p value = 0.50). Conclusions This intervention may provide an effective way to prepare and meet the ED response needs of the COVID-19 pandemic.

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Section: Methodsmentioning

confidence: 99%

Effectiveness of negative pressure isolation stretcher and rooms for SARS-CoV-2 nosocomial infection control and maintenance of South Korean emergency department capacity

Kim

Kong

Park

et al. 2021

The American Journal of Emergency Medicine

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“…It is based on the severity of patients' healthcare problems and the number of resources that is anticipated to require. KTAS was developed in 2012 based on the Canadian Triage and Acuity Scale and has been used nationwide as a triage since 2016 in Korea [11,17,18]. KTAS is a five-level ED triage algorithm that considers symptoms, pain, and physiological values.…”

Section: Performance Test Of Ai Algorithm and Comparison With Conventmentioning

confidence: 99%

“…The AUC is a frequently used metric and shows the sensitivity against 1-specificity [22]. Based on previous studies, we used levels 1-2, levels 1-2, points 3-14, and points 5-20 to predict the critical care with the ESI, KTAS, MEWS, and NEWS, respectively [11,[17][18][19][20][21]. When evaluating the continuous score predicted by the AI algorithm, we fixed the sensitivity as 0.8.…”

Section: Performance Test Of Ai Algorithm and Comparison With Conventmentioning

confidence: 99%

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Kang

Cho²,

Kwon³

et al. 2020

Scand J Trauma Resusc Emerg Med

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Background: In emergency medical services (EMSs), accurately predicting the severity of a patient's medical condition is important for the early identification of those who are vulnerable and at high-risk. In this study, we developed and validated an artificial intelligence (AI) algorithm based on deep learning to predict the need for critical care during EMS. Methods: We conducted a retrospective observation cohort study. The algorithm was established using development data from the Korean national emergency department information system, which were collected during visits in real time from 151 emergency departments (EDs). We validated the algorithm using EMS run sheets from two EDs. The study subjects comprised adult patients who visited EDs. The endpoint was critical care, and we used age, sex, chief complaint, symptom onset to arrival time, trauma, and initial vital signs as the predicted variables. Results: The number of patients in the development data was 8,981,181, and the validation data comprised 2604 EMS run sheets from two hospitals. The area under the receiver operating characteristic curve of the algorithm to predict the critical care was 0.867 (95% confidence interval, [0.864-0.871]). This result outperformed the Emergency Severity Index (0.839 [0.831-0.846]), Korean Triage and Acuity System (0.824 [0.815-0.832]), National Early Warning Score (0.741 [0.734-0.748]), and Modified Early Warning Score (0.696 [0.691-0.699]). Conclusions: The AI algorithm accurately predicted the need for the critical care of patients using information during EMS and outperformed the conventional triage tools and early warning scores.

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“…The KTAS is a triage tool consisting of five levels for classification of patients according to their chief complaints and vital signs. 15…”

Section: Data Collectionmentioning

confidence: 99%

Use of the National Early Warning Score for predicting in-hospital mortality in older adults admitted to the emergency department

et al. 2020

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Objective The National Early Warning Score (NEWS), based on the patients' vital signs, detects clinical deterioration in critically ill patients and is used to reduce the incidence of in-hospital cardiac arrest. However, although mortality prediction based on vital signs may be difficult in older patients, the effectiveness of the NEWS has not yet been evaluated in this population. This study aimed to test the hypothesis that an elevated NEWS at admission increases the mortality risk in older patients admitted to the emergency department (ED). Methods We conducted a single-center retrospective study, including patients admitted to the ED between November 2016 and February 2017. We included patients aged > 65 years who were admitted to the ED for any medical problem. The NEWS was calculated at the time of ED admission. The primary outcome was in-hospital mortality. Results In total, 3,169 patients were included in this study. Median age was 75 years (interquartile range [IQR], 70 to 80 years), and 1,557 (49.1%) patients were male. The in-hospital mortality rate was 5.1% (161 patients). Median NEWS was higher in non-survivors than in survivors (5 [IQR, 3-8] vs. 1 [IQR, 0-3], P < 0.001). Multivariate logistic analysis showed that the NEWS was associated with in-hospital mortality, after adjusting for other confounders. The area under the curve of the NEWS for predicting in-hospital mortality was 0.820 (95% confidence interval, 0.806 to 0.833). Conclusion Our results show that the NEWS at admission is associated with in-hospital mortality among patients aged > 65 years.

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The Korean Triage and Acuity Scale: associations with admission, disposition, mortality and length of stay in the emergency department

Abstract: Implementation of the KTAS changed admission and disposition patterns and reduced the LOS and mortality in the ED.

Cited by 53 publications

References 13 publications

Effectiveness of negative pressure isolation stretcher and rooms for SARS-CoV-2 nosocomial infection control and maintenance of South Korean emergency department capacity

Effectiveness of negative pressure isolation stretcher and rooms for SARS-CoV-2 nosocomial infection control and maintenance of South Korean emergency department capacity

Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services

Use of the National Early Warning Score for predicting in-hospital mortality in older adults admitted to the emergency department

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