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Open access · US-GOV via ClinicalTrials.gov Clinical trial

Performance of Different Early Warning Systems in Predicting 28-day Mortality Among Geriatric Emergency Department Patients

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Haseki Training and Research Hospital · 2025

Abstract

This prospective observational cohort study evaluated the prognostic performance of commonly used early warning scores for predicting 28-day all-cause mortality among geriatric patients presenting to the emergency department with non-traumatic conditions. Patients aged 65 years and older were consecutively screened during the study period. Demographic characteristics, comorbidities, vital signs, level of consciousness, blood gas parameters, complete blood count parameters, frailty status, and early warning scores were recorded at emergency department presentation or within the first hour of admission. The evaluated scoring systems included National Early Warning Score (NEWS/NEWS2), Modified Early Warning Score (MEWS), quick Sequential Organ Failure Assessment (qSOFA), Rapid Emergency Medicine Score (REMS), Cardiac Arrest Risk Triage (CART), and Hamilton Early Warning Score (HEWS) score. The primary outcome was 28-day all-cause mortality. The study also examined whether age, comorbidity burden, frailty, laboratory markers, and hemodynamic parameters were independently associated with 28-day mortality in this population.

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ClinicalTrials.gov
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2026-10-20 MST

Cite this

APA
Anonymous. (2025). Performance of Different Early Warning Systems in Predicting 28-day Mortality Among Geriatric Emergency Department Patients. <em>Haseki Training and Research Hospital</em>. https://clinicaltrials.gov/study/NCT07577349
Vancouver
Anonymous. Performance of Different Early Warning Systems in Predicting 28-day Mortality Among Geriatric Emergency Department Patients. Haseki Training and Research Hospital. 2025.
BibTeX
@misc{anon2025Perfor, title = {Performance of Different Early Warning Systems in Predicting 28-day Mortality Among Geriatric Emergency Department Patients}, author = {Anonymous}, journal = {Haseki Training and Research Hospital}, year = {2025}, }

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