Vital signs are essential for monitoring and prognostication in the emergency department (ED); however, they may not fully capture the complexity of frailty in older adults. In this multicenter retrospective study of 932 older patients who visited the EDs of three tertiary university hospitals between August 1 and October 31, 2023, we investigated the prognostic value of the Clinical Frailty Scale (CFS) in older patients in the ED and its potential to improve existing vital sign-based scoring systems. The primary outcomes were hospital admission, intensive care unit (ICU) admission, and in-hospital mortality. The AUROC was used to evaluate and compare the predictive performance of CFS, qSOFA, NEWS2, and REMS scores individually and in combination. Combining the CFS with these scores significantly improved predictive accuracy compared to individual scores alone. For hospital admission, the AUROCs were 0.715 (95% CI 0.685–0.744), 0.723 (95% CI 0.693–0.752), and 0.688 (95% CI 0.657–0.718) for CFS + qSOFA, CFS + NEWS2, and CFS + REMS, respectively. For ICU admission, the AUROCs were 0.730 (95% CI 0.701–0.759), 0.714 (95% CI 0.684–0.743), and 0.707 (95% CI 0.677–0.736), respectively. For in-hospital mortality, the AUROCs were 0.798 (95% CI: 0.771–0.823), 0.774 (95% CI: 0.746–0.801), and 0.819 (95% CI: 0.793–0.843), respectively, indicating excellent performance. Incorporating frailty assessment using the CFS enhances risk stratification in older patients in the ED by complementing vital sign-based scores. This provides a more comprehensive assessment, enabling better informed clinical decisions. This study supports employing routine frailty assessment in the ED and the development of enhanced risk stratification tools that incorporate frailty.

Lim JY, et al. J Korean Med Sci. 2018 Feb;33(11):e86. https://doi.org/10.3346/jkms.2018.33.e86

The South Korean population is rapidly aging, and the number of older patients in long-term-care hospitals (LTCHs) continues to increase. This study aims to investigate the epidemiologic data, characteristics, and clinical outcomes of patients aged 65 years and older transferred from LTCHs to emergency departments (EDs). This is a retrospective study based on National Emergency Department Information System data from 2014 to 2019. Of the 6,209,695 older patients visiting EDs for disease treatment, 211,141 (3.4%) were transferred from LTCHs. Among patients from LTCHs (211,141), 24.2% were discharged from EDs, 43.0% were admitted to general wards, 20.7% were hospitalized in intensive care units, 3.1% were transferred to another hospital, 6.1% returned to LTCHs, and 2.1% died in EDs. ED stays were the longest for those returning to LTCHs (710.49 ± 1127.43 min). Foley catheterization (40.3%) was most frequently performed in preventable ED visits. In South Korea, older patients being discharged from the ED or returning to LTCHs, after being transferred from LTCHs to EDs, increased. ED stays among older LTCH patients were longer than among non-LTCH older patients, contributing to congestion. To reduce avoidable transfer to EDs from LTCHs, it is necessary to discuss policies, such as expanding appropriate medical personnel and transitional treatment.

Background and Objectives: Elderly trauma patients face unique physiological challenges that often lead to undertriage under the current guidelines. The present study aimed to develop machine-learning (ML)-based, age-specific triage guidelines to improve predictions for intensive care unit (ICU) admissions and in-hospital mortality. Materials and Methods: A total of 274,347 trauma cases transported via Emergency Medical System (EMS)-119 in Seoul (2020–2022) were analyzed. Physiological indicators (e.g., systolic blood pressure; saturation of partial pressure oxygen; and alert, verbal, pain, unresponsiveness scale) were incorporated. Bayesian optimization was used to fine-tuned models for sensitivity and specificity, emphasizing the F2 score to minimize undertriage. Results: Compared with the current guidelines, the alternative guidelines achieved superior sensitivity for ICU admissions (0.728 vs. 0.541) and in-hospital mortality (0.815 vs. 0.599). Subgroup analyses across injury severities, including traumatic brain and chest injuries, confirmed the enhanced performance of the alternative guidelines. Conclusions: ML-based, age-specific triage guidelines improve the sensitivity of triage decisions, reduce undertriage, and optimize elderly trauma care. Implementing these guidelines can significantly enhance patient outcomes and resource allocation in emergency settings.