Comparison of the Emergency Severity Index versus the Patient Acuity Category Scale in an emergency setting

Fong, Ru Ying; Glen, Wee Sern Sim; Jamil, Ahmad Khairil Mohamed; Tam, Wilson Wai San; Kowitlawakul, Yanika

doi:10.1016/j.ienj.2018.05.001

Cited by 25 publications

(16 citation statements)

References 23 publications

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“…In contrast, general healthcare emergencies are considered life‐threatening conditions. Urgent conditions are not life threatening; however, failure to address urgent conditions in a timely manner may lead to an emergency 29,30 . Therefore, the DCG chose to define emergencies as potential life‐threatening conditions, and limited procedures to infections with spatial swelling, trismus, and/or trauma (Figure 1).…”

Section: Reportmentioning

confidence: 99%

Modifications of emergency dental clinic protocols to combat COVID‐19 transmission

Long

Ward

Pruett

et al. 2020

Special Care in Dentistry

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During the COVID-19 pandemic, incidence rates for dental diseases will continue unabated. However, the intent to prevent the spread of this lethal respiratory disease will likely lead to reduced treatment access due to restrictions on population movements. These changes have the potential to increase dental-related emergency department visits and subsequently contribute to greater viral transmission. Moreover, dentists experience unique challenges with preventing transmission due to frequent aerosol-producing procedures. This paper presents reviews and protocols implemented by directors and residents at the Dental College of Georgia to manage a dental emergency clinic during the COVID-19 pandemic. The methods presented include committee-based prioritization of dental patients, a multilayered screening process, team rotations with social and temporal spacing, and modified treatment room protocols. These efforts aid in the reduction of viral transmission, conservation of personal protective equipment, and expand provider availability. These protocols transcend a university and hospital-based models and are applicable to private and corporate models.

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Section: Reportmentioning

confidence: 99%

Modifications of emergency dental clinic protocols to combat COVID‐19 transmission

Long

Ward

Pruett

et al. 2020

Special Care in Dentistry

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“…To address the need for a risk tool appropriate to the ED workflow, we developed the Score for Emergency Risk Prediction (SERP) using a general-purpose machine learning–based scoring framework named AutoScore. 11 The resulting tool was compared in a test set to the current triage system used in Singapore, the Patient Acuity Category Scale (PACS), 12 and several established early warning or triage scores.…”

Section: Introductionmentioning

confidence: 99%

Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions

et al. 2021

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IMPORTANCE Triage in the emergency department (ED) is a complex clinical judgment based on the tacit understanding of the patient's likelihood of survival, availability of medical resources, and local practices. Although a scoring tool could be valuable in risk stratification, currently available scores have demonstrated limitations. OBJECTIVES To develop an interpretable machine learning tool based on a parsimonious list of variables available at ED triage; provide a simple, early, and accurate estimate of patients' risk of death; and evaluate the tool's predictive accuracy compared with several established clinical scores. DESIGN, SETTING, AND PARTICIPANTS This single-site, retrospective cohort study assessed all ED patients between January 1, 2009, and December 31, 2016, who were subsequently admitted to a tertiary hospital in Singapore. The Score for Emergency Risk Prediction (SERP) tool was derived using a machine learning framework. To estimate mortality outcomes after emergency admissions, SERP was compared with several triage systems, including Patient Acuity Category Scale, Modified Early Warning Score, National Early Warning Score, Cardiac Arrest Risk Triage, Rapid Acute Physiology Score, and Rapid Emergency Medicine Score. The initial analyses were completed in October 2020, and additional analyses were conducted in May 2021. MAIN OUTCOMES AND MEASURESThree SERP scores, namely SERP-2d, SERP-7d, and SERP-30d, were developed using the primary outcomes of interest of 2-, 7-, and 30-day mortality, respectively.Secondary outcomes included 3-day mortality and inpatient mortality. The SERP's predictive power was measured using the area under the curve in the receiver operating characteristic analysis. RESULTSThe study included 224 666 ED episodes in the model training cohort (mean [SD] patient age, 63.60 [16.90] years; 113 426 [50.5%] female), 56 167 episodes in the validation cohort (mean [SD] patient age, 63.58 [16.87] years; 28 427 [50.6%] female), and 42 676 episodes in the testing cohort (mean [SD] patient age, 64.85 [16.80] years; 21 556 [50.5%] female). The mortality rates inthe training cohort were 0.8% at 2 days, 2.2% at 7 days, and 5.9% at 30 days. In the testing cohort, the areas under the curve of SERP-30d were 0.821 (95% CI, 0.796-0.847) for 2-day mortality, 0.826 (95% CI, 0.811-0.841) for 7-day mortality, and 0.823 (95% CI, 0.814-0.832) for 30-day mortality and outperformed several benchmark scores. CONCLUSIONS AND RELEVANCEIn this retrospective cohort study, SERP had better prediction performance than existing triage scores while maintaining easy implementation and ease of ascertainment in the ED. It has the potential to be widely applied and validated in different circumstances and health care settings.

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“…All public hospitals use PACS to triage patients in the ED [16]. The four main levels of PACS status are: Priority 1 (P1), for patients requiring immediate resuscitation; Priority 2 (P2), for non-ambulant patients requiring critical care; Priority 3 (P3), for ambulant patients with mild to moderate symptoms; and Priority (P4), for patients with non-emergent conditions that are more appropriately managed in primary health care settings.…”

Section: Patient Acuity Category Scale (Pacs)mentioning

confidence: 99%

A Descriptive Analysis of the Impact of COVID-19 on Emergency Department Attendance and Visit Characteristics in Singapore

Lim

Saffari

et al. 2021

COVID

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Background: The coronavirus disease 2019 (COVID-19) has impacted the utilisation of Emergency Department (ED) services worldwide. This study aims to describe the changes in attendance at a single ED and corresponding patient visit characteristics before and during the COVID-19 period. Methods: In a single-centre retrospective cohort study, we used descriptive statistics to compare ED attendance, patient demographics and visit characteristics during the COVID-19 period (1 January–28 June 2020) and its corresponding historical period in 2019 (2 January–30 June 2019). Results: The mean ED attendance decreased from 342 visits/day in the pre-COVID-19 period to 297 visits/day in the COVID-19 period. This was accompanied by a decline in presentations in nearly every ICD-10-CM diagnosis category except for respiratory-related diseases. Notably, we observed reductions in visits by critically ill patients and severe disease presentations during the COVID-19 period. We also noted a shift in the ED patient case-mix from ‘Non-fever’ cases to ‘Fever’ cases, likely giving rise to two distinct trough-to-peak visit patterns during the pre-Circuit Breaker and Circuit Breaker period. Conclusions: This descriptive study revealed distinct ED visit trends across different time periods. The COVID-19 pandemic caused a reduction in ED attendances amongst patients with low-acuity conditions and those with highest priority for emergency care. This raises concern about treatment-seeking delays and the possible impact on health outcomes. The downward trend in low-acuity presentations also presents learning opportunities for ED crowd management planning in a post-COVID-19 era.

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Comparison of the Emergency Severity Index versus the Patient Acuity Category Scale in an emergency setting

Cited by 25 publications

References 23 publications

Modifications of emergency dental clinic protocols to combat COVID‐19 transmission

Modifications of emergency dental clinic protocols to combat COVID‐19 transmission

Development and Assessment of an Interpretable Machine Learning Triage Tool for Estimating Mortality After Emergency Admissions

A Descriptive Analysis of the Impact of COVID-19 on Emergency Department Attendance and Visit Characteristics in Singapore

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