Clinical Considerations When Applying Vital Signs in Pediatric Korean Triage and Acuity Scale

Streamlining pediatric vital sign assessment: innovations and insights

Accurate assessment of pediatric vital signs is critical for detecting abnormalities and guiding medical interventions, but interpretation is challenging due to age-dependent physiological variations. Therefore, this study aimed to develop age-specific centile curves for blood pressure, heart rate, and respiratory rate in pediatric patients and create a user-friendly web-based application for easy access to these data. We conducted a retrospective cross-sectional observational study analyzing 3,779,482 records from the National Emergency Department Information System of Korea, focusing on patients under 15 years old admitted between January 2016 and December 2017. After applying exclusion criteria to minimize the impact of patients' symptoms on vital signs, 1,369,608 records were used for final analysis. The box-cox power exponential distribution and Lambda-Mu-Sigma (LMS) method were used to generate blood pressure centile charts, while heart rate and respiratory rate values were drawn from previously collected LMS values. We developed comprehensive age-specific centile curves for systolic, diastolic, and mean blood pressure, heart rate, and respiratory rate. These were integrated into a web-based application ( http://centile.research.or.kr ), allowing users to input patient data and promptly obtain centile and z-score information for vital signs. Our study provides an accessible system for pediatric vital sign evaluation, addressing previous limitations and offering a practical solution for clinical assessment. Future research should validate these centile curves in diverse populations.

Predictive validity of resource-adjusted Korean Triage and Acuity Scale in pediatric gastrointestinal tract foreign body patients

Although paediatric patients with gastrointestinal (GI) foreign bodies require multiple resources, they often present with few or no discernible symptoms and are typically assigned a low acuity level during triage. We compared the predictive accuracy of the revised Korean Triage and Acuity Scale (rKTAS), which elevates acuity by one step in relation to anticipated resource utilization, with that of the conventional KTAS for clinical outcomes. This was a retrospective study of National Emergency Department Information System data. Data on patient and ED characteristics, resources used, and clinical outcomes were collected from January 2018 to December 2021 for patients with GI foreign bodies aged under 19. The primary outcome was rKTAS accuracy in predicting hospitalization, ICU admission, operating room (OR) use, and ED length of stay (EDLOS). The AUROC was used to evaluate the performance via of the KTAS and rKTAS. In total, 25,324 paediatric patients visited the ED for GI tract foreign bodies. The mean age was 3.8 years, and 51% (12,923) were between 1 and 4 year old. Although most (23,658; 93.4%) were discharged, 4.9% required hospitalization. Two or more resources were utilized in 2514 (9.9%) cases, and 3,514 individuals had their triage levels increased by one step. Compared with those of the KTAS, the AUROCs of the rKTAS for predicting overall hospitalization (p < 0.05), admission to general wards (p < 0.05), ICU admission (p = 0.01), and admission via the OR (p < 0.05)were higher than KTAS. Compared with that with the KTAS, the EDLOS with the rKTAS was longer at levels 1, 2, and 3. The adjusted rKTAS is a better predictor of clinical outcomes for paediatric patients with GI tract foreign bodies than the KTAS.

Predictive Validity of a New Triage System for Outcomes in Patients Visiting Pediatric Emergency Departments: A Nationwide Study in Korea

Triage is essential for rapid and efficient intervention in patients visiting an emergency department. In Korea, since 2016, the Pediatric Korean Triage and Acuity Scale (PedKTAS) has been implemented nationwide for the triage of patients visiting pediatric emergency departments (PEDs). The aim of this study was to evaluate the validity of the PedKTAS in patients who visit PEDs. This study was a retrospective observational study of national registry data collected from all emergency medical centers and institutions throughout Korea. We analyzed data from patients aged <15 years who visited emergency departments nationwide from January 2016 to December 2019. The hospitalization and intensive care unit (ICU) admission rates were analyzed on the basis of triage level. In total, 5,462,964 pediatric patients were included in the analysis. The hospitalization rates for PedKTAS Levels 1-5, were 63.5%, 41.1%, 17.0%, 6.5%, and 3.7%, respectively, and were significantly different (p < 0.001). The ICU admission rates for PedKTAS Levels 1-5 were 14.4%, 6.0%, 0.3%, 0.1%, and 0.1%, respectively, and were significantly different (p < 0.001). The hospitalization and ICU admission rates were highest for PedKTAS Level 1, and differences were significant based on the level. We identified that the PedKTAS is suitable for predicting the emergency status of pediatric patients who visit PEDs.

The association between prehospital vital signs of children and their critical clinical outcomes at hospitals

Vital signs are important for patient assessment, but little is known about interpreting those of children in prehospital settings. We conducted an observational study to investigate the association between prehospital vital signs of children and their clinical outcomes in hospitals. We plotted the data of patients with critical outcomes on published reference ranges, such as those of healthy children to evaluate the clinical relevance. Of the 18,493 children screened, 4477 transported to tertiary hospitals were included in the analysis. The outcomes 12 h after being transported to a tertiary hospital were as follows: deceased, 41; hospitalization with critical deterioration events, 65; hospitalization without critical deterioration events, 1086; returned home, 3090; and unknown, 195. The reference ranges of the heart rates (sensitivity: 57.7%, specificity: 67.5%) and respiratory rates (sensitivity: 54.5%, specificity: 67.7%) of healthy children worked best to detect the critical outcomes. Therefore, the reference ranges of healthy children were concluded to be suitable in prehospital settings; however, excessive reliance on vital signs carried potential risks due to their limited sensitivities and specificities. Future studies are warranted to investigate indicators with higher sensitivities and specificities.

Deep Learning Algorithm to Predict Need for Critical Care in Pediatric Emergency Departments

BACKGROUND AND OBJECTIVES

Emergency department (ED) overcrowding is a national crisis in which pediatric patients are often prioritized at lower levels. Because the prediction of prognosis for pediatric patients is important but difficult, we developed and validated a deep learning algorithm to predict the need for critical care in pediatric EDs.

METHODS

We conducted a retrospective observation cohort study using data from the Korean National Emergency Department Information System, which collected data in real time from 151 EDs. The study subjects were pediatric patients who visited EDs from 2014 to 2016. The data were divided by date into derivation and test data. The primary end point was critical care, and the secondary endpoint was hospitalization. We used age, sex, chief complaint, symptom onset to arrival time, arrival mode, trauma, and vital signs as predicted variables.

RESULTS

The study subjects consisted of 2,937,078 pediatric patients of which 18,253 were critical care and 375,078 were hospitalizations. For critical care, the area under the receiver operating characteristics curve of the deep learning algorithm was 0.908 (95% confidence interval, 0.903-0.910). This result significantly outperformed that of the pediatric early warning score (0.812 [0.803-0.819]), conventional triage and acuity system (0.782 [0.773-0.790]), random forest (0.881 [0.874-0.890]), and logistic regression (0.851 [0.844-0.858]). For hospitalization, the deep-learning algorithm (0.782 [0.780-0.783]) significantly outperformed the other methods.

CONCLUSIONS

The deep learning algorithm predicted the critical care and hospitalization of pediatric ED patients more accurately than the conventional early warning score, triage tool, and machine learning methods.

Changes in Emergency Department Performance during Strike of Junior Physicians in Korea

Objective

A nationwide strike that took place from August 21 to September 7, 2020, which was led by young doctors represented by residents and interns, resulted in shortages of manpower at almost all university and training hospitals. This study aimed to identify differences in the process and outcomes of emergency department (ED) patient care by comparing the performance over about 2 weeks of the strike with that during the usual ED operations.

Methods

This retrospective observational study evaluated ED flow and performance during the junior doctors' strike and compared it with the usual period in a single tertiary-care academic hospital. The outcome variables were defined as ED length of stay, crude mortality, and hospital mortality and adjusted for demographic and clinical parameters. The effect of the doctors' strike on hospital mortality adjusted for demographic and clinical variables was investigated using logistic regression.

Results

A total of 1,121 and 1,496 patients visited the ED during the strike and control periods (both 17 days), respectively. The care usually provided by four or six physicians, including one specialist, was replaced with that by one or two specialists at any one time. During the trainee doctors' strike, EM specialists managed patients with fewer consultations. However, the proportion of patients who underwent laboratory and radiologic tests did not change significantly. The median ED length of stay significantly decreased from 359 minutes (interquartile range, IQR: 147–391) in the control period to 326 minutes (IQR: 123–318) during the strike period (P < 0.001). The doctors' strike was not found to have a significant effect on mortality after adjustments with other variables.

Conclusion

During the junior doctors' strike in 2020 in Korea, EM specialists efficiently managed the care of emergency patients with higher levels of acuity without compromising the survival rate, through fewer consultations and faster disposition.

Distribution of Pediatric Vital Signs in the Emergency Department: A Nationwide Study

To effectively use vital signs as indicators in children, the magnitude of deviation from expected vital sign distribution should be determined. The purpose of this study is to derive age-specific centile charts for the heart rate and respiratory rate of the children who visited the emergency department. This study used the Korea’s National Emergency Department Information System dataset. Patients aged <16 years visiting the emergency department between 1 January 2016 and 31 December 2017 were included. Heart rate and respiratory rate centile charts were derived from the population with normal body temperature (36 to <38 °C). Of 1,901,816 data points retrieved from the database, 1,454,372 sets of heart rates and 1,458,791 sets of respiratory rates were used to derive centile charts. Age-specific centile charts and curves of heart rates and respiratory rates showed a decline in heart rate and respiratory rate from birth to early adolescence. There were substantial discrepancies in the reference ranges of Advanced Paediatric Life Support and Pediatric Advanced Life Support guidelines. Age-based heart rate and respiratory rate centile charts at normal body temperature, derived from children visiting emergency departments, serve as new evidence-based data and can be used in follow-up studies to improve clinical care for children.

Factors Associated with Triage Modifications Using Vital Signs in Pediatric Triage: a Nationwide Cross-Sectional Study in Korea

BACKGROUND

Previous studies on inter-rater reliability of pediatric triage systems have compared triage levels classified by two or more triage providers using the same information about individual patients. This overlooks the fact that the evaluator can decide whether or not to use the information provided. The authors therefore aimed to analyze the differences in the use of vital signs for triage modification in pediatric triage.

METHODS

This was an observational cross-sectional study of national registry data collected in real time from all emergency medical services beyond the local emergency medical centers (EMCs) throughout Korea. Data from patients under the age of 15 who visited EMC nationwide from January 2016 to December 2016 were analyzed. Depending on whether triage modifications were made using respiratory rate or heart rate beyond the normal range by age during the pediatric triage process, they were divided into down-triage and non-down-triage groups. The proportions in the down-triage group were analyzed according to the triage provider's profession, mental status, arrival mode, presence of trauma, and the EMC class.

RESULTS

During the study period, 1,385,579 patients' data were analyzed. Of these, 981,281 patients were eligible for triage modification. The differences in down-triage proportions according to the profession of the triage provider (resident, 50.5%; paramedics, 47.7%; specialist, 44.9%; nurses, 44.2%) was statistically significant (P < 0.001). The triage provider's professional down-triage proportion according to the medical condition of the patients showed statistically significant differences except for the unresponsive mental state (P = 0.502) and the case of air transport (P = 0.468).

CONCLUSION

Down-triage proportion due to abnormal heart rates and respiratory rates was significantly different according to the triage provider's condition. The existing concept of inter-rater reliability of the pediatric triage system needs to be reconsidered.

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

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.

Admission Decisions Made by Emergency Physicians Can Reduce the Emergency Department Length of Stay for Medical Patients

Background

Emergency department (ED) overcrowding is a worldwide problem that poses a threat to patient safety by causing treatment delays and increasing mortality. Consultations are common and important in the emergency medicine profession and are associated with longer ED length of stay (LOS). The purpose of this study was to evaluate the impact of admission decisions by emergency physicians without consultations on the ED LOS and other quality indicators.

Methods

The study was a retrospective observational study comparing the ED LOS of patients admitted to the internal medicine (IM) department before and after the policy change regarding admission decisions that was implemented in October 2016. During and after the policy change, emergency physicians decided how to arrange for and treat medical patients by processing their admission and providing follow-up care without consultations. The ED LOS and other indicators of patients admitted to the IM department were compared between the study period (January to June 2017) and the control period (January to June 2016).

Results

The median ED LOS of patients admitted to the IM department decreased from 673 (IQR: 347-1,369) minutes in the control period to 237 (IQR: 166-364) minutes in the study period. There were no significant differences in the interdepartmental transfer rate or in-hospital mortality between the two periods.

Conclusions

The admission decisions regarding medical patients made by emergency physicians without specialty consultations reduced the ED LOS without a significant negative effect on mortality or hospital LOS.

Transient and Adult Patients with Neurologic Diseases in the Pediatric Emergency Department: Trends and Characteristics

BACKGROUND AND PURPOSE

There is an increasing rate of presentations by transient and adult patients (TAPs) to pediatric emergency departments (PED-EDs). TAPs with neurologic diseases (N-TAPs) comprise most of these patients. We investigated this trend and compared the characteristics of N-TAPs with those of pediatric patients with neurologic diseases (N-PEDs) who presented to the PED-ED of a tertiary-care hospital in Korea.

METHODS

We reviewed the medical records of neurologic patients who presented to the PED-ED of a single tertiary-care hospital from 2013 to 2017. We included patients with neurologic symptoms or diseases and those who were treated in the pediatric neurology department and underwent neurologic evaluations and treatment in the PED-ED.

RESULTS

Presentations by N-TAPs to the PED-ED increased over time, whereas the number of N-PEDs gradually decreased, with a significant difference between the groups (p<0.001). The number of N-TAPs who presented to the PED-ED almost tripled from 2013 to 2017. N-TAPs had significantly more acute symptoms than N-PEDs, and a significantly higher proportion of N-TAPs were insured by Medical Aid compared to N-PEDs (p<0.001). The admission rate was significantly higher (p<0.001) and the mean hospital stay was longer (p=0.046) for N-TAPs. Epilepsy and neurometabolic diseases were mainly responsible for the increased presentations by N-TAPs.

CONCLUSIONS

We have clarified the status of N-TAPs in the PED-ER and the role of pediatric neurologists who manage them. Multidisciplinary treatments focusing on the role of pediatric neurologists should be developed to that systematic long-term care plans are applied to N-TAPs.

Validation of deep-learning-based triage and acuity score using a large national dataset

AIM

Triage is important in identifying high-risk patients amongst many less urgent patients as emergency department (ED) overcrowding has become a national crisis recently. This study aims to validate that a Deep-learning-based Triage and Acuity Score (DTAS) identifies high-risk patients more accurately than existing triage and acuity scores using a large national dataset.

METHODS

We conducted a retrospective observational cohort study using data from the Korean National Emergency Department Information System (NEDIS), which collected data on visits in real time from 151 EDs. The NEDIS data was split into derivation data (January 2014-June 2016) and validation data (July-December 2016). We also used data from the Sejong General Hospital (SGH) for external validation (January-December 2017). We predicted in-hospital mortality, critical care, and hospitalization using initial information of ED patients (age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs and mental status as predictor variables).

RESULTS

A total of 11,656,559 patients were included in this study. The primary outcome was in-hospital mortality. The Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC) of DTAS were 0.935 and 0.264. It significantly outperformed Korean triage and acuity score (AUROC:0.785, AUPRC:0.192), modified early warning score (AUROC:0.810, AUPRC:0.116), logistic regression (AUROC:0.903, AUPRC:0.209), and random forest (AUROC:0.910, AUPRC:0.179).

CONCLUSION

Deep-learning-based Triage and Acuity Score predicted in-hospital mortality, critical care, and hospitalization more accurately than existing triages and acuity, and it was validated using a large, multicenter dataset.