6 Prognostic value of the glasgow admission prediction score: hospital length of stay, mortality and hospital readmission. Issue 12 (23rd November 2017)

• Record Type: Journal Article
• Authors: Jones, Dominic; Cameron, Allan; Mason, Suzanne; O'Keeffe, Colin; Lowe, David
• Is Part Of: Emergency medicine journal. Volume 34:Issue 12(2017)
• Page Start: A864
• Page End: A865
• Publication Date: 2017-11-23
• Subjects: Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1136/emermed-2017-207308.6
• Languages: English
• ISSNs: 1472-0205

Abstract

Abstract : Introduction: As patient numbers presenting to emergency departments (ED) increase, with their myriad of comorbidities, early hospital admission prediction and demand modelling are crucial both in the ED and beyond. The Glasgow admission prediction score (GAPS) (figure 1 ) 1 has already been shown to be accurate in predicting hospital admission from the ED at the point of triage. 2 As demand on EDs increase, data driven models such as GAPS will become increasingly important for predicting patient course. However, GAPS has not previously been tested beyond the point of admission. Aim: To assess whether GAPS has the ability to predict hospital length of stay (LOS), six-month mortality and six-month hospital readmission. Methods: Sampling was conducted in 2016 at the Sheffield Teaching Hospitals NHS foundation trust ED and the NHS Greater Glasgow and Clyde ED. Data were collected prospectively at the point of triage for all consecutive patients who presented to the ED during sampling times. GAPS was calculated independent of patient clinical management and recorded. Patients were followed up at six months, looking at length of any hospital admission, mortality and hospital readmission. Length of hospital stay, mortality and hospital readmission against GAPS was modelled using survival analysis. Results: In total 1420 patients were recruited, 39.6% of these patients were initially admitted to hospital. At six months, 30.6% of patients had been readmitted and 5.6% of patients had died. For those admitted at first presentation, the chance of being discharged at any one time fell by 4.3% (95% confidence interval (CI) 3.2%–5.3%) per GAPS point increase. Figure 2 displays the Kaplan Meier curves for 6 month mortality. Cox regression showed a significant association between GAPS and mortality, with a hazard increase of 9% (95% CI:6.9% to 11.2%) for every point increase on GAPS. Figure 3 displays the Kaplan Meier curves for 6 month hospital readmission. Discussion: GAPS is a simple tool which utilises data routinely collected at triage. It is predictive of hospital admission, hospital length of stay, six-month all-cause mortality and six-month hospital readmission. Therefore, GAPS could be employed to aid staff in hospital bed planning, clinical decision making and ED resource allocation and utilisation. References: Logan E, et al. Predicating admission at triage . Presented at International Acute Medicine Conference, Edinburgh 2016. Cameron A, et al. A simple tool to predict admission at the time of triage. Emergency Medicine Journal2014.