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Title: Clinical scoring system to identify high-acuity patients from information available in the Emergency Department
Author: Challen, Kirsty
ISNI:       0000 0004 5355 5886
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2014
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Background: Acute illness results in millions of hospital admissions per year. Assessment of illness severity can guide the intensity and location of care provided but although multiple clinical decision aids exist for large numbers of conditions no well-validated clinical decision aid exists for the assessment of patients with unselected medical emergencies. It cannot be assumed that tools which predict death will accurately identify those patients with the most potential to benefit from urgent care. Methods: A prospective cohort was analysed using logistic regression to develop bedside scores to identify patients at high risk of death, and those where emergency care has the potential to affect survival. Consensus methodology was used to develop threshold responses for the Emergency Department. Results: 7 variables and one interaction (age, respiratory rate, diastolic blood pressure, oxygen saturation, temperature, GCS, pre-existing respiratory disease, respiratory disease by temperature interaction) predicted death in 7 days with AUROC 0.753 (derivation set: n=2437) and 0.719 (validation set: n=2322). Other scores showed AUROC 0.658 – 0.762. 3 variables (pulse, systolic blood pressure, GCS) predicted potentially preventable or potentially prevented death with AUROC 0.737 (derivation set: n=398) and 0.686 (validation set: n=227). Other scores showed AUROC 0.559 – 0.684. Consensus was reached on four thresholds of clinical response on the 0-27 point score for potentially preventable or potentially prevented death. Conclusions: No published tool exists to identify the patient most likely to benefit from emergency department care. Variables predicting death do not necessarily predict potential to benefit from care, and existing scores have only moderate discrimination for this. The tool developed here shows potential but ongoing research should address which patients will benefit from time-critical interventions and the complexities of ED prioritisation.
Supervisor: Goodacre, Steve ; Nicholl, Jon ; Bradburn, Mike Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available