R36: UTILISING FRAILTY EARLY WARNING SCORE (FEWS) IN THE ACUTE HOSPITAL SETTING TO IDENTIFY FRAIL AND VULNERABLE PATIENTS Lotte Dinesen1,2,Alan J Poots1, Federico Ciardi3, Lauren M Bell3, John Soong1, Derek Bell1,2 1 CLAHRC NWL/ Imperial College, 2 Chelsea and Westminster Hospital NHS FT, 3 Imperial College London Aim Operationalize a clinically usable tool, frailty early warning score (FEWS), which will identify frailty and help predict significant outcomes: readmission length of stay (LOS) transfer to higher level of care mortality. Methods FEWS is based on a frailty model described by Soong et al (2015) with four specific domains (physical, mental, social and environmental, as illustrated in figure 1. Between 03 June 2015 and 08 January 2016, 1903 acutely admitted patients over the age of 65 were reviewed. Data were collected from clinical notes taken routinely as part of the emergency admission process. No non-routine clinical data were collected. All data were collected electronically through bespoke software by Thinkshield and innate hospital programs. The national early warning scores (NEWS) which reflect medical acuity (captures routine physiological data and is known to correlate to illness severity and need to escalate treatment for at risk patients. A total NEWS score exceeding 7 suggests rapid response Team), were simultaneously collected for comparison. Outcomes / Results 1903 patients were included (53.8% female) with an average age of 81 years. 94% were medical admissions. In hospital mortality 7.99%, 7 day readmission rate was 10.19% and the average length of stay (LOS) was 12.4 days (Table 1). Table 1 provides the patient demographics and outcomes. In hospital mortality; 7d mortality; 30d mortality; 90d mortality contrast 0-2, vs 3+ = all significant difference by Chi-sq; 7d readmission : 0 vs 1+; significant difference LOS 0-2, vs 3+, significant difference by Kruskal Wallis test Trends suggestive that the FEWS is a positive predictor of discharge to higher social care, LOS and downstream to wards which is shown in fig 1.  Conclusion This study describes FEWS as a novel way of predicting a frail individual’s outcomes. It can be easily calculated at the point of care and is fast and simple to use. Further work is needed to determine the weight of each domain and sub-domain, as this will be needed to define the sensitivity of the final aggregate score. In the process of readjusting the weighting which of the observed measures shall become the primary outcome tailor is still undecided. Frailty Score (FEWS) 1 2 3 4 5 6 7 8 >9 Number of patients 264 270 259 238 211 192 164 147 108 50 Average Age 74.01 76.61 78.07 80.60 81.09 83.29 85.68 86.84 86.03 87.72 F:M Ratio 1: 0.95 0.96:1 0.99:1 1:0.88 1:077 1:0.64 1:0.76 1:0.71 1:0.74 1:1 NEWS (on admission) 2.13 2.17 2.28 2.34 3.21 2.33 2.10 2.36 3.51 5.12 In hospital mortality 2.65% 2.96% 4.63% 10.50% 7.58% 9.90% 9.76% 14.29% 15.74% 11.00% 7 day mortality 3.03% 3.70% 5.79% 8.06% 10.42% 10.98% 13.00% 90 day mortality 6.44% 7.78% 8.49% 17.65% 11.85% 13.54% 15.85% 21.77% 16.67% 17.00% LOS (days) 7.31 8.08 9.09 11.09 14.44 17.33 17.78 12.29 23.42 15.36 Readmission 7 days 5.68% 9.63% 12.74% 10.94% 12.20% 11.56% 7.41% 8.00% Readmission 90 days 38.36% 42.96% 44.78% 45.80% 45.97% 40.63% 42.28% 46.26% 37.97% 40.00% Figure 1. Four domains of frailty Table 1.Number of patients, outcomes and average NEWS score for each frailty score Figure 2. Relationship between FEWS to length of stay, down streamed to wards and discharged to higher social settings