Dataset Information

Performance of a three-level triage scale in live triage encounters in an emergency department in Hong Kong.

ABSTRACT:

Background

Despite its continued use in many low-volume emergency departments (EDs), 3-level triage systems have not been extensively studied, especially on live triage cases. We have modified from the Australasian Triage Scale and developed a 3-level triage scale, and sought to evaluate its validity, reliability, and over- and under-triage rates in real patient encounters in our setting.

Method

This was a cross-sectional study in a single ED with 24,000 attendances per year. At triage, each patient was simultaneously assessed by a triage nurse, an adjudicator (the "criterion standard"), and a study nurse independently. Predictive validity was determined by comparing clinical outcomes, such as hospitalization, across triage levels. The discriminating performance of the triage tool in identifying patients requiring earlier medical attention was determined. Inter-observer reliability between the triage nurse and criterion standard, and across providers were determined using kappa statistics.

Results

In total, 453 triage ratings of 151 triage cases, involving 17 ED triage nurses and 57 nurse pairs, were analysed. The proportion of hospital admission significantly increased with a higher triage rating. The performance of the scale in identifying patients requiring earlier medical attention was as follows: sensitivity, 68.2% (95% CI 45.1-86.1%); specificity, 99.2% (95% CI 95.8-100%); positive predictive value, 93.8% (95% CI 67.6-99.1%); and negative predictive value, 94.8% (95% CI 90.8-97.1%). The over-triage and under-triage rates were 0.7% and 4.6%, respectively. Agreement between the triage nurse and criterion standard was substantial (quadratic-weighted kappa = 0.76, 95% CI, 0.60-0.92, p < 0.001), so was the agreement across nurses (quadratic-weighted kappa = 0.81, 95% CI 0.65-0.97, p < 0.001).

Conclusions

The 3-level triage system appears to have good validity and reasonable reliability in a low-volume ED setting. Further studies comparing 3-level and prevailing 5-level triage scales in live triage encounters and different ED settings are warranted.

SUBMITTER: Lam RPK

PROVIDER: S-EPMC7288528 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Performance of a three-level triage scale in live triage encounters in an emergency department in Hong Kong.

Lam Rex Pui Kin RPK Kwok Shing Lam SL Chaang Vi Ka VK Chen Lujie L Lau Eric Ho Yin EHY Chan Kin Ling KL

International journal of emergency medicine 20200610 1

<h4>Background</h4>Despite its continued use in many low-volume emergency departments (EDs), 3-level triage systems have not been extensively studied, especially on live triage cases. We have modified from the Australasian Triage Scale and developed a 3-level triage scale, and sought to evaluate its validity, reliability, and over- and under-triage rates in real patient encounters in our setting.<h4>Method</h4>This was a cross-sectional study in a single ED with 24,000 attendances per year. At t ...[more]

PMID: 32522272

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Project description:BackgroundThe aim of the emergency department (ED) triage is to recognize critically ill patients and to allocate resources. No strong evidence for accuracy of the current triage instruments, especially for the older adults, exists. We evaluated the National Early Warning Score 2 (NEWS2) and a 3-level triage assessment as risk predictors for frail older adults visiting the ED.MethodsThis prospective, observational study was performed in a Finnish ED. The data were collected in a six-month period and included were ≥ 75-year-old residents with Clinical Frailty Scale score of at least four. We analyzed the predictive values of NEWS2 and the three-level triage scale for 30-day mortality, hospital admission, high dependency unit (HDU) and intensive care unit (ICU) admissions, a count of 72-h and 30-day revisits, and ED length-of-stay (LOS).ResultsA total of 1711 ED visits were included. Median for age, CFS, LOS and NEWS2 were 85 years, 6 points, 6.2 h and 1 point, respectively. 30-day mortality was 96/1711. At triage, 69, 356 and 1278 of patients were assessed as red, yellow and green, respectively. There were 1103 admissions, of them 31 to an HDU facility, none to ICU. With NEWS2 and triage score, AUCs for 30-day mortality prediction were 0.70 (0.64-0.76) and 0.62 (0.56-0.68); for hospital admission prediction 0.62 (0.60-0.65) and 0.55 (0.52-0.56), and for HDU admission 0.72 (0.61-0.83) and 0.80 (0.70-0.90), respectively. The NEWS2 divided into risk groups of low, medium and high did not predict the ED LOS (p = 0.095). There was a difference in ED LOS between the red/yellow and as red/green patient groups (p < 0.001) but not between the yellow/green groups (p = 0.59). There were 48 and 351 revisits within 72 h and 30 days, respectively. With NEWS2 AUCs for 72-h and 30-day revisit prediction were 0.48 (95% CI 0.40-0.56) and 0.47 (0.44-0.51), respectively; with triage score 0.48 (0.40-0.56) and 0.49 (0.46-0.52), respectively.ConclusionsThe NEWS2 and a local 3-level triage scale are statistically significant, but poor in accuracy, in predicting 30-day mortality, and HDU admission but not ED LOS or revisit rates for frail older adults. NEWS2 also seems to predict hospital admission.

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Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

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Dataset Information

Performance of a three-level triage scale in live triage encounters in an emergency department in Hong Kong.

Background

Method

Results

Conclusions

Publications

Performance of a three-level triage scale in live triage encounters in an emergency department in Hong Kong.

Similar Datasets

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