Dataset Information

Validation of a Modified Triage Scale in a Norwegian Pediatric Emergency Department.

ABSTRACT:

Objective

Triage is a tool developed to identify patients who need immediate care and those who can safely wait. The aim of this study was to assess the validity and interrater reliability of a modified version of the pediatric South African triage scale (pSATS) in a single-center tertiary pediatric emergency department in Norway.

Methods

This prospective, observational study included all patients with medical conditions, referred to the pediatric emergency department of a tertiary hospital in Norway from September 1, 2015, to November 17, 2015. Their assigned triage priority was compared with rate of hospitalization and resource utilization. Validity parameters were sensitivity, specificity, positive and negative predictive value, and percentage of over- and undertriage. Interrater agreement and accuracy of the triage ratings were calculated from triage performed by nurses on written case scenarios.

Results

During the study period, 1171 patients arrived at the hospital for emergency assessment. A total of 790 patients (67 %) were triaged and included in the study. The percentage of hospital admission increased with increasing level of urgency, from 30 % of the patients triaged to priority green to 81 % of those triaged to priority red. The sensitivity was 74 %, the specificity was 48 %, the positive predictive value was 52 %, and the negative predictive value was 70 % for predicting hospitalization. The level of over- and undertriage was 52 % and 26 %, respectively. Resource utilization correlated with higher triage priority. The interrater agreement had an intraclass correlation coefficient of 0.99 by Cronbach's alpha, and the accuracy was 92 %.

Conclusions

The modified pSATS had a moderate sensitivity and specificity but showed good correlation with resource utilization. The nurses demonstrated excellent interrater agreement and accuracy when triaging written case scenarios.

SUBMITTER: Engan M

PROVIDER: S-EPMC6205310 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Validation of a Modified Triage Scale in a Norwegian Pediatric Emergency Department.

Engan Mette M Hirth Asle A Trønnes Håvard H

International journal of pediatrics 20181015

<h4>Objective</h4>Triage is a tool developed to identify patients who need immediate care and those who can safely wait. The aim of this study was to assess the validity and interrater reliability of a modified version of the pediatric South African triage scale (pSATS) in a single-center tertiary pediatric emergency department in Norway.<h4>Methods</h4>This prospective, observational study included all patients with medical conditions, referred to the pediatric emergency department of a tertiar ...[more]

PMID: 30410545

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Project description:BackgroundDespite 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.MethodThis 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.ResultsIn 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).ConclusionsThe 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.

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.

Project description:BACKGROUND:Emergency department (ED) crowding leads to prolonged emergency department length of stay (ED-LOS) and adverse patient outcomes. No uniform definition of ED crowding exists. Several scores have been developed to quantify ED crowding; the best known is the Emergency Department Work Index (EDWIN). Research on the EDWIN is often applied to limited settings and conducted over a short period of time. OBJECTIVES:To explore whether the EDWIN as a measure can track occupancy at a Dutch ED over the course of one year and to identify fluctuations in ED occupancy per hour, day, and month. Secondary objective is to investigate the discriminatory value of the EDWIN in detecting crowding, as compared with the occupancy rate and prolonged ED-LOS. METHODS:A retrospective cohort study of all ED visits during the period from September 2010 to August 2011 was performed in one hospital in the Netherlands. The EDWIN incorporates the number of patients per triage level, physicians, treatment beds and admitted patients to quantify ED crowding. The EDWIN was adjusted to emergency care in the Netherlands: modified EDWIN (mEDWIN). ED crowding was defined as the 75th percentile of mEDWIN per hour, which was ?0.28. RESULTS:In total, 28,220 ED visits were included in the analysis. The median mEDWIN per hour was 0.15 (Interquartile range (IQR) 0.05-0.28); median mEDWIN per patient was 0.25 (IQR 0.15-0.39). The EDWIN was higher on Wednesday (0.16) than on other days (0.14-0.16, p<0.001), and a peak in both mEDWIN (0.30-0.33) and ED crowding (52.9-63.4%) was found between 13:00-18:00 h. A comparison of the mEDWIN with the occupancy rate revealed an area under the curve (AUC) of 0.86 (95%CI 0.85-0.87). The AUC of mEDWIN compared with a prolonged ED-LOS (?4 hours) was 0.50 (95%CI 0.40-0.60). CONCLUSION:The mEDWIN was applicable at a Dutch ED. The mEDWIN was able to identify fluctuations in ED occupancy. In addition, the mEDWIN had high discriminatory power for identification of a busy ED, when compared with the occupancy rate.

Project description:BackgroundDevelopment of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI).MethodsUsing National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ??18?years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.ResultsOf 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85-0.87] in the deep neural network vs 0.74 [95%CI 0.72-0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82-0.83] in the deep neural network vs 0.69 [95%CI 0.68-0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit-a larger number of appropriate triages considering a trade-off with over-triages-across the range of clinical thresholds.ConclusionsCompared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.

Dataset Information

Validation of a Modified Triage Scale in a Norwegian Pediatric Emergency Department.

Objective

Methods

Results

Conclusions

Publications

Validation of a Modified Triage Scale in a Norwegian Pediatric Emergency Department.

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