Abstract
-
Objective
Emergency department (ED) triage systems are used to classify the severity and urgency of emergency patients, and Korean medical institutions use the Korean Triage and Acuity Scale (KTAS). During the COVID-19 pandemic, appropriate treatment for emergency patients was delayed due to various circumstances, such as overcrowding of EDs, lack of medical workforce resources, and increased workload on medical staff. The purpose of this study was to evaluate the accuracy of the KTAS in predicting the urgency of emergency patients during the COVID-19 pandemic.
-
Methods
This study retrospectively reviewed patients who were treated in the ED during the pandemic period from January 2020 to June 2021. Patients were divided into COVID-19–screening negative (SN) and COVID-19–screening positive (SP) groups. We compared the predictability of the KTAS for urgent patients between the two groups.
-
Results
From a total of 107,480 patients, 62,776 patients (58.4%) were included in the SN group and 44,704 (41.6%) were included in the SP group. The odds ratios for severity variables at each KTAS level revealed a more evident discriminatory power of the KTAS for severity variables in the SN group (P<0.001). The predictability of the KTAS for severity variables was higher in the SN group than in the SP group (area under the curve, P<0.001).
-
Conclusion
During the pandemic, the KTAS had low accuracy in predicting patients in critical condition in the ED. Therefore, in future pandemic periods, supplementation of the current ED triage system should be considered in order to accurately classify the severity of patients.
-
Keywords: Emergency departments; Triage; COVID-19
Capsule Summary
What is already known
Early detection of emergency patients is important in overcoming emergency department (ED) overcrowding. Most EDs today implement a triage system to classify the severity and urgency of emergency patients.
What is new in the current study
During the COVID-19 pandemic, the severity classification of patients who visited the hospital with symptoms related to viral diseases had low accuracy in predicting patients in critical condition. Future prospective observational studies will be needed to estimate the ability of the ED triage system to predict urgent patients during a pandemic.
INTRODUCTION
Overcrowding in the emergency department (ED) is an important issue that is associated with complex and multifaceted influential factors [
1,
2]. Congestion in the ED results in delayed care for critical patients, contributes to unfavorable patient outcomes, and wastes ED resources [
3,
4]. Therefore, early detection of emergency patients is important in overcoming ED overcrowding. Most EDs today implement a triage system to classify the severity and urgency of emergency patients [
5]. The performance of triage systems is continuously improving [
6].
During the COVID-19 pandemic beginning in 2019, individuals displaying symptoms of COVID-19, those who had been in contact with the virus without symptoms, and individuals facing other medical issues all converged at the ED. This led to a situation of overcrowding, underscoring the significance of being prepared and implementing efficient triage systems [
7]. The symptoms of patients with respiratory diseases such as COVID-19 can worsen quickly, leading to an increased requirement for medical resources [
8]. As the COVID-19 pandemic dragged on, the timely administration of adequate care to patients within the ED was impeded by a confluence of factors, encompassing ED overcrowding, deficiencies in medical workforce resources, and an augmented burden on the medical staff's responsibilities [
9–
11]. With the escalation in demands placed upon patients and medical resources, the necessity for precise triage systems becomes more pronounced.
Triage is a decision-making process to identify emergency patients requiring immediate treatment [
12]. Moreover, it serves as a tool to mitigate ED overcrowding by effectively categorizing patients within the confined spatial and resource constraints of the ED. Various triage systems are used around the world, including the Emergency Severity Index in the United States, the Manchester Triage Scale in the United Kingdom, the Canadian Triage and Acuity Scale (CTAS), and the Australian Triage Scale [
13–
15].
In 2012, the Korean Ministry of Health and Welfare formulated the Korean Triage and Acuity Scale (KTAS) modeled after CTAS. This initiative aims to establish a harmonized emergency medical framework spanning both prehospital and hospital phases, ensuring the safety of emergency patients and the establishment of a streamlined and effective emergency medical system [
13]. Introduced in 2016, the KTAS has gained widespread adoption across numerous institutions in Korea. Despite a few constraints, research pertaining to the KTAS has predominantly centered on assessing its validity. Notably, studies have revealed a reduction in both length of stay and mortality rates subsequent to the implementation of the KTAS [
16,
17]. In addition, the accuracy of and mistriage by the KTAS has been consistently studied [
18].
However, the KTAS has never been evaluated at a time of explosive growth in the number of patients with symptoms associated with a specific virus, such as during the COVID-19 pandemic. If the existing triage systems do not work during such an explosive increase in the number of patients, the ED can become chaotic.
Hence, the primary objective of this study was to assess the accuracy of the KTAS in foreseeing the level of urgency among patients during a pandemic scenario. The accuracy of the KTAS was examined within the context of an ED setting. This evaluation involved categorizing patients presenting with virus-related symptoms and those without such symptoms. The aim was to investigate the extent to which the KTAS accurately gauges the urgency of patient cases. This study will contribute to the improvement of patient safety and the quality of emergency medical care by identifying and evaluating the accuracy of the KTAS for patient classification during an infectious, respiratory disease-related pandemic.
METHODS
Ethics statement
The Institutional Review Board of Yonsei University Health System, Severance Hospital (Seoul, Korea), reviewed and approved this study (No. 4-2022-0321). The requirement for informed consent was waived due to the retrospective study design. Neither patients nor the public were involved in the design, conduct, or reporting of our research.
Study design
This tertiary university hospital is in northwest Seoul, Korea, and is responsible for managing approximately 90,000 patients who visit the ED every year. This study retrospectively reviewed patients who were treated in the ED during the pandemic period from January 2020 to June 2021. This medical facility comprises distinct emergency departments for adults and pediatric patients. Notably, individuals aged 19 years and above receive treatment in the adult ED section. The adult ED is divided into acute (with 17 beds) and recovery (with 26 beds and 10 clinic chairs) zones [
19]. Triage consists of an ambulance triage and a walking patients’ triage room.
Upon the arrival of an emergency patient at the ED, both the KTAS categorization and COVID-19 screening are initiated simultaneously within the designated triage room. Qualified triage nurses use the KTAS to classify patients on a scale of 1 to 5 (1, resuscitation; 2, emergency; 3, urgent; 4, less urgent; and 5, nonurgent). Triage nurses are required to complete the KTAS training program by the KTAS committee under the Korean Society of Emergency Medicine and have at least 4 years of experience working in the ED [
20].
The COVID-19 screening consists of three criteria, and if at least one of them is satisfied, it is considered as screening-positive: whether there is a history of exposure to COVID-19 at a specific time and place; the presence of a series of symptoms suggestive of COVID-19 infection, such as fever/chills or upper respiratory symptoms (cough, sputum, rhinorrhea, sore throat, and dyspnea); and the presence or absence of abnormal vital signs, such as body temperature more than 37.5 °C, desaturation, and oxygen requirement. Based on the COVID-19 screening outcomes, patients are allocated to either isolation or nonisolation areas. Subsequently, they are further assigned into emergency and nonemergency zones based on the KTAS assessment. Following this categorization, the appropriate treatment is initiated. The isolation area consists of a space with a ventilation system that is separated by walls to prevent contact with other patients. A COVID-19 polymerase chain reaction test and chest radiography are performed immediately. Emergency physicians are present in both isolation and nonisolation areas to examine patients, order laboratory and imaging tests, and decide on patient disposition (
Fig. 1).
Patients were divided into COVID-19–screening negative (SN) and COVID-19–screening positive (SP) groups. Patients under the age of 19 years, those with missing data, and those who visited the ED for medical documents were excluded (
Fig. 2).
We extracted basic patient characteristics and medical information automatically through a clinical research analysis portal developed by the medical information department in our hospital. The data contained information on sex, age, vital signs at ED arrival (systolic blood pressure, diastolic blood pressure, pulse rate, respiratory rate, body temperature, and oxygen saturation), past medical history (hypertension, diabetes mellitus, and surgery history), method of ED arrival (ambulance or transfer from another hospital), and KTAS score.
The lack of a standard reference indicating urgency has always been a problem in validation studies for triage methods [
21,
22]. Variables were set by referring to previous studies related to triage validity [
19]. In previous studies verifying the validity of the emergency patient triage tool, predictive validity has been used to obtain sensitivity and specificity based on whether the patient was admitted to a general ward or intensive care unit (ICU) and whether or not they received immediate lifesaving intervention [
23,
24]. By presenting significant differences in hospitalization duration, in-hospital mortality, ED occupancy time, and medical cost or medical resource use in the ED, the validity of the triage tool was verified indirectly [
12]. In our study, we employed severity variables as reference variables for urgency, including cardiopulmonary resuscitation (CPR), endotracheal intubation, admission to the ICU, application of oxygen (via nasal cannula, mask, high-flow nasal cannula, and ventilator), continuous renal replacement therapy (CRRT), hemodialysis (HD), and administration of vasoactive agents (norepinephrine, vasopressin, dopamine, and dobutamine). These variables have been frequently utilized in past validation studies for triage methods. These variables were collected during the entire hospital day.
Comparisons between the two groups (SN vs. SP) were performed using chi-squared analysis for categorical variables and t-tests for continuous variables. The distribution of KTAS scores and the variables representing the degree of urgency were compared between groups. Among the patient characteristics in both groups, some variables that may have affected urgency were adjusted for. Adjusted odds ratios (aORs) are presented via multivariate logistic regression. To evaluate the discriminative power of the KTAS, we calculated the aOR of each KTAS level compared to KTAS score 3 for urgent patients. Since the number of patients with KTAS score 3 was the highest, KTAS score 3 was used as the standard [
19,
25]. The receiver operating characteristic curve was used to compare KTAS predictive power between the two groups. Statistical analyses were performed using R ver. 4.0.3 (R Foundation for Statistical Computing).
RESULTS
From January 2020 to June 2021, after excluding patients younger than 19 years, with missing data, without KTAS assessments, and who canceled their application for personal reasons, 107,480 patients were included in the analyses (
Fig. 2). Of these, 62,776 patients (58.4%) were included in the SN group and 44,704 (41.6%) in the SP group.
Table 1 summarizes the patient characteristics, KTAS scores, and severity variables between the two groups. The SN group included more women and younger patients than the SP group. In the SP group, more patients used ambulances (18.6% vs. 29.5%, P<0.001), and fewer patients had nonmedical problems (21.6% vs. 5.0%, P<0.001). The transfer rate from other hospitals was higher in the SN group, and the prevalence of past medical conditions like hypertension and diabetes, as well as a history of surgeries, was observed to be higher in the SP group. In the SP group, blood pressure and oxygen saturation were lower, pulse and respiratory rates were faster, and body temperature was higher (36.7±0.6 °C vs. 37.1±0.9 °C, P<0.001). The number of ED patients who received CPR (0.5% vs. 1.7%, P<0.001) and intubation (0.7% vs. 4.2%, P<0.001) was higher in the SP group. In the SN group, 2.5% of the patients were treated in the ICU compared with 9.1% in the SP group (P<0.001). Patients in the SP group also had higher rates of oxygen supplementation, renal replacement therapy (CRRT and HD), and vasoactive agents.
Distribution of severity variables for each KTAS score
The distributions of severity variables among patients in both groups for each KTAS score were compared (
Table 2). Within KTAS scores 1 to 3, the SP group exhibited a statistically significant increase in the number of urgent patients, as indicated by most of the severity variables: intubation, ICU admission, oxygen supplementation, renal replacement therapy (CRRT and HD), and vasoactive agents. In addition, in patients classified as less urgent (KTAS score 4), there were more urgent patients in the SP group for every severity variable. In KTAS score 5, the same result was obtained, except with CRRT, in which no significant difference was observed.
Fig. 3 presents the results of the correlation between KTAS and severity variables. The OR for the occurrence of urgent patients decreased as KTAS levels increased in both groups. The difference between the ORs for each KTAS level was more evident in the SN group for CPR, intubation, ICU admission, oxygen supplementation, CRRT, and vasoactive agents.
The distinction in severity across various indices between KTAS scores 2 and 3 was greater within the SN group compared to the SP group: aOR (95% confidence interval [CI]), 3.85 (2.20–6.75) and 1.43 (1.13–1.84) for CPR; 3.77 (2.69–5.29) and 1.94 (1.70–2.21) for intubation; 2.98 (2.65–3.36) and 2.07 (1.90–2.25) for ICU admission; 1.71 (1.52–1.92) and 1.15 (1.07–1.23) for oxygen supplementation; 3.05 (1.73– 5.38) and 1.34 (1.09–1.63) for CRRT; and 1.97 (1.69–2.30) and 1.58 (1.46–1.71) for vasoactive agents. Also, the variation in severity between KTAS scores 3 and 4 was more pronounced within the SN group compared to the SP group, as evidenced by several indicators: aOR (95% CI), 0.29 (0.14–0.60) and 0.31 (0.22–0.44) for CPR; 0.28 (0.18–0.43) and 0.34 (0.29–0.41) for intubation; 0.12 (0.10–0.14) and 0.31 (0.28–0.34) for ICU admission; 0.31 (0.28–0.34) and 0.37 (0.35–0.40) for oxygen supplementation; 0.22 (0.10–0.52) and 0.38 (0.329–0.49) for CRRT; and 0.34 (0.30–0.40) and 0.49 (0.45–0.53) for inotropes.
Fig. 4 shows the results of the area under the curve (AUC) comparison of the predictive power of KTAS for urgent patients in the two groups. KTAS showed a good level of predictive power with an AUC of 0.8 or higher for all variables in the SN group. The ability of KTAS to predict urgent patients within the SN group was significantly superior in comparison to the SP group; this finding was particularly evident for indicators such as CPR, intubation, ICU admission, CRRT, HD, and inotropic use.
DISCUSSION
The predictive power of the KTAS was higher in the SN group for most of the severity variables. We found that severity levels were less accurate in patients in the SP group. The ideal triage tool should be uniformly evaluated in any situation assessing patient urgency, and it is important that patients in the same group have a similar level of urgency. Especially during a pandemic, it is important to prioritize the provision of limited medical resources to urgent patients. Besides, a number of factors can be attributed to overclassification during the pandemic. There is no standardized method to objectively evaluate the symptoms and risk factors associated with most viral infections. Therefore, it is necessary to establish a reasonable criterion for the symptoms and signs of patients associated with a specific virus so that medical personnel can perform consistent evaluations.
It is difficult for patients to objectively evaluate their own symptoms due to inaccurate medical knowledge about emerging new viral diseases. Thus, it is important to discuss the signs and symptoms of infections with the patient as well as decide on a specific technique to query the patient about their symptoms. However, during a pandemic, it is difficult to appropriately evaluate patients in a situation where classification must be performed in a short time. Therefore, further research on this aspect is also necessary. To reduce potential surges in emergency medical systems, we will need to engage experts in medicine, public health, nursing, information technology, and other fields to design and test pandemic-specific triage algorithms.
The development of a triage system enables ED medical staff to quickly identify and classify patients, which shortens time to initiate treatment and enables efficient use of medical resources. Pandemic viral diseases can spread suddenly, even to healthy people without immunity, resulting in many unexpected cases [
26,
27]. Notably, respiratory illnesses like COVID-19 exhibit high contagiousness in their initial phases, and the patients' conditions can deteriorate rapidly owing to the severity of the disease. This circumstance places substantial demands on potential medical resources. Therefore, efficient distribution of medical resources is important in such a situation.
In the ED of each medical institution, a triage system categorizes patients by integrating their symptoms and contact history in order to treat patients appropriately and effectively with limited medical resources [
28]. However, during the COVID-19 pandemic, individuals displaying flu-like symptoms sought care in the ED, and asymptomatic patients with a history of contact also presented themselves. Consequently, the task of triaging patients using established tools like the KTAS became challenging. In addition, even if a patient's symptoms are just a cold, if a large number of patients develop symptoms in a short period of time, and if patients develop serious illness, the demand for medical resources can increase rapidly; therefore, a more specialized triage system is required.
To date, no study has determined the efficacy of current triage tools in accurately categorizing emergency patients during catastrophic events like the COVID-19 pandemic. Moreover, the existing severity classifications do not possess the specialization required to differentiate and classify patients exhibiting flu-like symptoms. Previous studies that engaged in triage assessments of ED patients were conducted during periods distinct from a pandemic scenario such as COVID-19. Studies that aimed to compare triage practices during a pandemic did not yield meaningful results due to the small number of patients [
29,
30].
Experimental algorithms are used for conducting a triage during influenza pandemics, but are limited in their usefulness and validity for use in the ED [
31]. Failure of the initial triage classification in the ED adversely affects the distribution of emergency patients and medical resources due to changes in the patient treatment policy. This can lead to delays in patient treatment as well as an unnecessary waste of medical resources and cost burdens in a disaster situation. In addition, the patient’s stay in the ED is likely to be prolonged. Long emergency patient stay time leads to overcrowding of the ED, which adversely affects the patient’s clinical outcome and wastes limited medical resources.
This study has some limitations. First, this was a single-center study. Our findings are difficult to generalize due to the single-center design. Since this study was conducted in a tertiary university hospital with a large number of severely ill patients and severe overcrowding, it cannot be regarded as representative of a general ED situation. Second, the period in which this study was conducted did not reflect the entire pandemic period, even though it was the period when screening was most strictly applied. Additionally, because COVID-19 virus characteristics were reflected in the results, there are limitations in the generalizability of these findings to other viral diseases. Third, the clinical outcomes evaluated in our study did not include outcomes addressing patient safety, such as mortality or urgency-related complications. Therefore, additional studies to verify the association between the KTAS and urgency should be considered. Fourth, medical records were retrospectively analyzed; thus, we did not examine detailed information about the patients' symptoms and signs to determine classification. Because these data were excluded from the analysis, it was not possible to determine the impact of the excluded data on the results. Therefore, additional studies to validate specific methods for improving triage during the pandemic should be considered.
During the pandemic, the KTAS had low accuracy in predicting patients in critical condition in the ED. Therefore, in future pandemic periods, supplementation of the ED triage system or use of a customized triage system should be considered in order to accurately classify the severity of patients. Future prospective observational studies are needed to estimate the ability of the ED triage system to predict urgent patients during a pandemic.
NOTES
-
Author contributions
Conceptualization: all authors; Data curation: all authors; Formal analysis: all authors; Methodology: all authors; Writing–original draft: all authors; Writing–review & editing: all authors. All authors read and approved the final manuscript.
-
Conflicts of interest
The authors have no conflicts of interest to declare.
-
Funding
The authors received no financial support for this study.
-
Data availability
Data analyzed in this study are available from the corresponding author upon reasonable request.
Fig. 1.Emergency department (ED) patient flow and triage. KTAS, Korean Triage and Acuity Scale; SN, COVID-19–screening negative; SP, COVID-19–screening positive.
Fig. 2.Study flowchart. ED, emergency department; SN, COVID-19–screening negative; SP, COVID-19–screening positive.
Fig. 3.Adjusted odds ratios for urgent patients in COVID-19–screening negative (SN) and COVID-19–screening positive (SP) groups using the Korean Triage and Acuity Scale (KTAS). Each plot represents the odds ratio and 95% confidence interval compared with KTAS score 3. The severity variables are as follows: (A) cardiopulmonary resuscitation; (B) intubation; (C) intensive care unit admission; (D) oxygen supplementation; (E) continuous renal replacement therapy; (F) hemodialysis; and (G) vasoactive agents.
Fig. 4.Comparison of the receiver operating characteristic curve of Korean Triage and Acuity Scale (KTAS) predictability for patient urgency between the COVID-19–screening negative (SN) and COVID-19–screening positive (SP) groups. The severity variables are as follows: (A) cardiopulmonary resuscitation; (B) intubation; (C) intensive care unit admission; (D) oxygen supplementation; (E) continuous renal replacement therapy; (F) hemodialysis; and (G) vasoactive agents. AUC, area under the curve.
Table 1.Comparison of characteristics between the two groups of patients (n=107,480)
Table 1.
|
Characteristic |
SN group (n=62,776) |
SP group (n=44,704) |
P-value |
|
Female sex |
33,574 (53.5) |
23,057 (51.6) |
<0.001 |
|
Age (yr) |
51.7±19.7 |
57.3±20.1 |
<0.001 |
|
Ambulance arrival |
11,651 (18.6) |
13,168 (29.5) |
<0.001 |
|
Nonmedical problem |
13,551 (21.6) |
2,234 (5.0) |
<0.001 |
|
Transfer from other hospitals |
5,789 (9.2) |
3,868 (8.7) |
<0.001 |
|
Past medical history |
|
|
|
|
Hypertension |
16,394 (26.1) |
15,183 (34.0) |
<0.001 |
|
Diabetes mellitus |
9,084 (14.5) |
9,063 (20.3) |
<0.001 |
|
Previous operation |
16,337 (26.0) |
13,810 (30.9) |
<0.001 |
|
Vital sign |
|
|
|
|
Systolic blood pressure (mmHg) |
137.4±24.8 |
132.9±26.7 |
<0.001 |
|
Diastolic blood pressure (mmHg) |
82.2±13.7 |
78.6±14.9 |
<0.001 |
|
Pulse rate (beats/min) |
86.0±17.8 |
94.7±21.1 |
<0.001 |
|
Respiratory rate (breaths/min) |
17.4±2.3 |
18.4±3.2 |
<0.001 |
|
Body temperature (°C) |
36.7±0.6 |
37.1±0.9 |
<0.001 |
|
Saturation of percutaneous oxygen (%) |
97.7±2.3 |
97.0±3.4 |
<0.001 |
|
ED triage KTAS score |
|
|
|
|
1 |
696 (1.1) |
1,159 (2.6) |
<0.001 |
|
2 |
4,478 (7.1) |
6,066 (13.6) |
<0.001 |
|
3 |
17,126 (27.3) |
16,802 (37.6) |
<0.001 |
|
4 |
31,118 (49.6) |
17,307 (38.7) |
<0.001 |
|
5 |
9,358 (14.9) |
3,370 (7.5) |
<0.001 |
|
Severity variable |
|
|
|
|
Cardiopulmonary resuscitation |
304 (0.5) |
744 (1.7) |
<0.001 |
|
Intubation |
446 (0.7) |
1,856 (4.2) |
<0.001 |
|
Intensive care unit admission |
1,552 (2.5) |
4,052 (9.1) |
<0.001 |
|
Oxygen supplementationa)
|
2,543 (4.1) |
10,440 (23.4) |
<0.001 |
|
Continuous renal replacement therapy |
77 (0.1) |
692 (1.5) |
<0.001 |
|
Hemodialysis |
515 (0.8) |
1,240 (2.8) |
<0.001 |
|
Vasoactive agent |
1,355 (2.2) |
5,981 (13.4) |
<0.001 |
Table 2.Comparison of severity variables within each KTAS scores between the two groups
Table 2.
|
Severity variable |
No. of patients (%)
|
P-value |
|
SN group |
SP group |
|
CPR (KTAS score) |
|
|
|
|
1 |
237/696 (34.05) |
369/1,159 (31.84) |
0.018 |
|
2 |
30/4,478 (0.67) |
133/6,066 (2.19) |
0.001 |
|
3 |
25/17,126 (0.15) |
191/16,802 (1.14) |
<0.001 |
|
4 |
10/31,118 (0.03) |
44/17,307 (0.25) |
<0.001 |
|
5 |
2/9,358 (0.02) |
7/3,370 (0.21) |
0.018 |
|
Intubation (KTAS score) |
|
|
|
|
1 |
259/696 (37.21) |
581/1,159 (50.13) |
<0.001 |
|
2 |
77/4,478 (1.72) |
492/6,066 (8.11) |
<0.001 |
|
3 |
71/17,126 (0.41) |
600/16,802 (3.57) |
<0.001 |
|
4 |
30/31,118 (0.1) |
166/17,307 (0.96) |
<0.001 |
|
5 |
9/9,358 (0.1) |
17/3,370 (0.5) |
<0.001 |
|
ICU admission (KTAS score) |
|
|
|
|
1 |
59/696 (8.48) |
371/1,159 (32.01) |
<0.001 |
|
2 |
555/4,478 (12.39) |
1253/6,066 (20.66) |
<0.001 |
|
3 |
758/17,126 (4.43) |
1815/16,802 (10.8) |
<0.001 |
|
4 |
150/31,118 (0.48) |
544/17,307 (3.14) |
<0.001 |
|
5 |
30//9,358 (0.31) |
69/3,370 (2.05) |
<0.001 |
|
Oxygen supplementationa) (KTAS score) |
|
|
|
|
1 |
127/696 (18.25) |
668/1,159 (57.64) |
<0.001 |
|
2 |
543/4,478 (12.13) |
2368/6,066 (39.04) |
<0.001 |
|
3 |
1217/17,126 (7.11) |
5414/16,802 (32.22) |
<0.001 |
|
4 |
582/31,118 (1.87) |
1771/17,307 (10.23) |
<0.001 |
|
5 |
74/9,358 (0.77) |
219/3,370 (6.5) |
<0.001 |
|
CRRT (KTAS score) |
|
|
|
|
1 |
14/696 (2.01) |
131/1,159 (11.3) |
0.004 |
|
2 |
27/4,478 (0.6) |
182/6,066 (3) |
<0.001 |
|
3 |
27/17,126 (0.16) |
298/16,802 (1.77) |
<0.001 |
|
4 |
7/31,118 (0.02) |
76/17,307 (0.44) |
<0.001 |
|
5 |
2/9,358 (0.02) |
5/3,370 (0.14) |
0.080 |
|
Hemodialysis (KTAS score) |
|
|
|
|
|
1 |
7/696 (1.01) |
65/1,159 (5.61) |
0.045 |
|
2 |
58/4,478 (1.3) |
243/6,066 (4.01) |
<0.001 |
|
3 |
238/17,126 (1.39) |
629/16,802 (3.74) |
<0.001 |
|
4 |
171/31,118 (0.55) |
258/17,307 (1.49) |
<0.001 |
|
5 |
41/9,358 (0.44) |
45/3,370 (1.34) |
<0.001 |
|
Vasoactive agent (KTAS score) |
|
|
|
|
1 |
141/696 (20.26) |
713/1,159 (61.52) |
<0.001 |
|
2 |
306/4,478 (6.83) |
1,528/6,066 (25.19) |
<0.001 |
|
3 |
580/17,126 (3.39) |
2,588/16,802 (15.4) |
<0.001 |
|
4 |
283/31,118 (0.91) |
1,020/17,307 (5.89) |
<0.001 |
|
5 |
45/9,358 (0.48) |
132/3,370 (3.92) |
<0.001 |
REFERENCES
- 1. Hoot NR, Aronsky D. Systematic review of emergency department crowding: causes, effects, and solutions. Ann Emerg Med 2008;52:126-36.
- 2. Di Somma S, Paladino L, Vaughan L, Lalle I, Magrini L, Magnanti M. Overcrowding in emergency department: an international issue. Intern Emerg Med 2015;10:171-5.
- 3. McCarthy ML, Zeger SL, Ding R, et al. Crowding delays treatment and lengthens emergency department length of stay, even among high-acuity patients. Ann Emerg Med 2009;54:492-503.
- 4. Pines JM, Hollander JE. Emergency department crowding is associated with poor care for patients with severe pain. Ann Emerg Med 2008;51:1-5.
- 5. Fitzgerald G, Jelinek GA, Scott D, Gerdtz MF. Republished paper: emergency department triage revisited. Postgrad Med J 2010;86:502-8.
- 6. Zachariasse JM, van der Hagen V, Seiger N, Mackway-Jones K, van Veen M, Moll HA. Performance of triage systems in emergency care: a systematic review and meta-analysis. BMJ Open 2019;9:e026471.
- 7. Af Ugglas B, Skyttberg N, Wladis A, Djarv T, Holzmann MJ. Emergency department crowding and hospital transformation during COVID-19: a retrospective, descriptive study of a university hospital in Stockholm, Sweden. Scand J Trauma Resusc Emerg Med 2020;28:107.
- 8. Tolchin B, Latham SR, Bruce L, et al. Developing a triage protocol for the COVID-19 pandemic: allocating scarce medical resources in a public health emergency. J Clin Ethics 2020;31:303-17.
- 9. Kim JH, Ahn C, Namgung M. Epidemiology and outcome of out-of-hospital cardiac arrests during the COVID-19 pandemic in South Korea: a systematic review and meta-analyses. Yonsei Med J 2022;63:1121-9.
- 10. Kim C, Lee J, Cho Y, et al. The impact of the COVID-19 pandemic on in-hospital mortality in patients admitted through the emergency department. Clin Exp Emerg Med 2023;10:92-8.
- 11. Chun SY, Kim HJ, Kim HB. The effect of COVID-19 pandemic on the length of stay and outcomes in the emergency department. Clin Exp Emerg Med 2022;9:128-33.
- 12. Fernandes CM, Tanabe P, Gilboy N, et al. Five-level triage: a report from the ACEP/ENA Five-level Triage Task Force. J Emerg Nurs 2005;31:39-50.
- 13. Beveridge R, Ducharme J, Janes L, Beaulieu S, Walter S. Reliability of the Canadian Emergency Department Triage and Acuity Scale: interrater agreement. Ann Emerg Med 1999;34:155-9.
- 14. Tanabe P, Travers D, Gilboy N, et al. Refining Emergency Severity Index triage criteria. Acad Emerg Med 2005;12:497-501.
- 15. Marsden J, Windle J, Mackway-Jones K. Emergency triage. Emerg Nurse 2013;21:11.
- 16. Lim YD, Lee DH, Lee BK, Cho YS, Choi G. Validity of the Korean Triage and Acuity Scale for predicting 30-day mortality due to severe trauma: a retrospective single-center study. Eur J Trauma Emerg Surg 2020;46:895-901.
- 17. Kwon H, Kim YJ, Jo YH, et al. The Korean Triage and Acuity Scale: associations with admission, disposition, mortality and length of stay in the emergency department. Int J Qual Health Care 2019;31:449-55.
- 18. Moon SH, Shim JL, Park KS, Park CS. Triage accuracy and causes of mistriage using the Korean Triage and Acuity Scale. PLoS One 2019;14:e0216972.
- 19. Lee JH, Park YS, Park IC, et al. Over-triage occurs when considering the patient’s pain in Korean Triage and Acuity Scale (KTAS). PLoS One 2019;14:e0216519.
- 20. Choi DH, Hong WP, Song KJ, et al. Modification and validation of a complaint-oriented emergency department triage system: a multicenter observational study. Yonsei Med J 2021;62:1145-54.
- 21. Ng CJ, Yen ZS, Tsai JC, et al. Validation of the Taiwan triage and acuity scale: a new computerised five-level triage system. Emerg Med J 2011;28:1026-31.
- 22. Kuriyama A, Urushidani S, Nakayama T. Five-level emergency triage systems: variation in assessment of validity. Emerg Med J 2017;34:703-10.
- 23. Aeimchanbanjong K, Pandee U. Validation of different pediatric triage systems in the emergency department. World J Emerg Med 2017;8:223-7.
- 24. Zachariasse JM, Seiger N, Rood PP, et al. Validity of the Manchester Triage System in emergency care: a prospective observational study. PLoS One 2017;12:e0170811.
- 25. Lee JY, Oh SH, Peck EH, et al. The validity of the Canadian Triage and Acuity Scale in predicting resource utilization and the need for immediate life-saving interventions in elderly emergency department patients. Scand J Trauma Resusc Emerg Med 2011;19:68.
- 26. Dominguez-Cherit G, Lapinsky SE, Macias AE, et al. Critically ill patients with 2009 influenza A(H1N1) in Mexico. JAMA 2009;302:1880-7.
- 27. Louie JK, Acosta M, Winter K, et al. Factors associated with death or hospitalization due to pandemic 2009 influenza A(H1N1) infection in California. JAMA 2009;302:1896-902.
- 28. Zhang J, Zhou L, Yang Y, Peng W, Wang W, Chen X. Therapeutic and triage strategies for 2019 novel coronavirus disease in fever clinics. Lancet Respir Med 2020;8:e11-2.
- 29. Semple MG, Myles PR, Nicholson KG, et al. An evaluation of community assessment tools (CATs) in predicting use of clinical interventions and severe outcomes during the A(H1N1)pdm09 pandemic. PLoS One 2013;8:e75384.
- 30. Myles PR, Nguyen-Van-Tam JS, Lim WS, et al. Comparison of CATs, CURB-65 and PMEWS as triage tools in pandemic influenza admissions to UK hospitals: case control analysis using retrospective data. PLoS One 2012;7:e34428.
- 31. Kellermann AL, Isakov AP, Parker R, Handrigan MT, Foldy S. Web-based self-triage of influenza-like illness during the 2009 H1N1 influenza pandemic. Ann Emerg Med 2010;56:288-94.
