Development of an emergency department triage tool to predict admission or discharge for older adults
Dublin Core
Title
Development of an emergency department triage tool to predict admission or discharge for older adults
Subject
Hospitalization, Emergency department, Risk score, Older adults
Description
Abstract
Background Older adults present to Emergency Departments (ED) with complex conditions, requiring triage models
that support effective disposition decisions. While existing models perform well in the general population, they
often fall short for older patients. This study introduces a triage model aimed at improving early risk stratification and
disposition planning in this population.
Methods We analyzed the National Hospital Ambulatory Medical Care Survey data (2015–2019) for ED patients
aged≥60 years, excluding those who died in the ED or left against medical advice. Key predictors were identified
using a two-step process combining LASSO and backward stepwise selection. Model performance was evaluated
using AUC and calibration plots, while clinical utility was assessed through decision curve analysis. Risk thresholds
(<0.1, 0.1–0.5, >0.5) stratified patients into low, moderate, and high-risk groups, optimizing the balance between
sensitivity and specificity.
Results Of 13,431 patients, 3,180 (23.7%) were admitted. Key predictors for admission included ambulance arrival,
chronic conditions, gastrointestinal bleeding, and abnormal vital signs. The model showed strong discrimination (AUC
0.73) and good calibration, validated by 10-fold cross-validation (mean AUC 0.73, SD 0.02). Decision curve analysis
highlighted net benefit across clinically relevant thresholds. At thresholds of 0.1 and 0.5, the model identified 18.9% as
low-risk (91.2% accuracy) and 7.9% as high-risk (57.7%). Adjusting thresholds to 0.2 and 0.4 expanded low-risk (55.4%,
87.9% accuracy) and high-risk (14.1%, 53.7% accuracy) groups.
Conclusions This older adult–focused risk score uses readily available data to enhance early discharge, prioritize
admissions for high-risk patients, and enhance ED care delivery.
Highlights
• Readily available triage data predict hospital admission in older adult ED patients.
• Key predictors include chief complaint, ambulance arrival, comorbidities, and vital signs.
• The Hospital Admission Model effectively stratifies patients into low- and high-risk groups.
• At a 0.2 threshold, 55% of patients were classified as low risk with 88% accuracy.
• At a 0.5 threshold, 8% of patients were classified as high risk with 58% accuracy.
Background Older adults present to Emergency Departments (ED) with complex conditions, requiring triage models
that support effective disposition decisions. While existing models perform well in the general population, they
often fall short for older patients. This study introduces a triage model aimed at improving early risk stratification and
disposition planning in this population.
Methods We analyzed the National Hospital Ambulatory Medical Care Survey data (2015–2019) for ED patients
aged≥60 years, excluding those who died in the ED or left against medical advice. Key predictors were identified
using a two-step process combining LASSO and backward stepwise selection. Model performance was evaluated
using AUC and calibration plots, while clinical utility was assessed through decision curve analysis. Risk thresholds
(<0.1, 0.1–0.5, >0.5) stratified patients into low, moderate, and high-risk groups, optimizing the balance between
sensitivity and specificity.
Results Of 13,431 patients, 3,180 (23.7%) were admitted. Key predictors for admission included ambulance arrival,
chronic conditions, gastrointestinal bleeding, and abnormal vital signs. The model showed strong discrimination (AUC
0.73) and good calibration, validated by 10-fold cross-validation (mean AUC 0.73, SD 0.02). Decision curve analysis
highlighted net benefit across clinically relevant thresholds. At thresholds of 0.1 and 0.5, the model identified 18.9% as
low-risk (91.2% accuracy) and 7.9% as high-risk (57.7%). Adjusting thresholds to 0.2 and 0.4 expanded low-risk (55.4%,
87.9% accuracy) and high-risk (14.1%, 53.7% accuracy) groups.
Conclusions This older adult–focused risk score uses readily available data to enhance early discharge, prioritize
admissions for high-risk patients, and enhance ED care delivery.
Highlights
• Readily available triage data predict hospital admission in older adult ED patients.
• Key predictors include chief complaint, ambulance arrival, comorbidities, and vital signs.
• The Hospital Admission Model effectively stratifies patients into low- and high-risk groups.
• At a 0.2 threshold, 55% of patients were classified as low risk with 88% accuracy.
• At a 0.5 threshold, 8% of patients were classified as high risk with 58% accuracy.
Creator
Ashraf Abugroun1*, Saria Awadalla2
, Sanjay Singh3
and Margaret C. Fang1
, Sanjay Singh3
and Margaret C. Fang1
Source
https://doi.org/10.1186/s12245-025-00825-3
Date
2025
Contributor
Peri Irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Ashraf Abugroun1*, Saria Awadalla2
, Sanjay Singh3
and Margaret C. Fang1, “Development of an emergency department triage tool to predict admission or discharge for older adults,” Repository Horizon University Indonesia, accessed April 25, 2026, https://repository.horizon.ac.id/items/show/12651.