P90 PERCEIVE: PrEdiction of Risk and Communication of outcome following major lower limb amputation - a collaboratIVE study. (8th April 2021) - Gwilym, B; Waldron, C; Thomas-Jones, E; Pallmann, P; Preece, R; Brookes-Howell, L; Milosevic, S; Edwards, A; Twine, C; Massey, I; Burton, J; Harris, D; et al.
Abstract: Introduction: Major Lower Limb Amputation (MLLA) is a life changing event with significant morbidity and mortality. Inaccurate risk prediction can lead to poor decision making, resulting in delay to definitive surgery, or undertaking amputation when not in the patient's best interest. We aim to answer: In adult patients undergoing MLLA for chronic limb threatening ischaemia or diabetes, how accurately do health care professionals prospectively predict outcomes after MLLA, and how does this compare to existing prediction tools? Methods: A multicentre prospective observational cohort study is being delivered through the Vascular and Endovascular Research Network. Dissemination was via an existing network of contacts and social media. Consecutive data will be collected for seven months from site launch date, including demographic data and pre-operative outcome predictions from surgeons, anaesthetists, and allied healthcare professionals. Follow-up data will comprise 30-day (mortality, morbidity, MLLA revision, surgical site infection, and blood transfusion) and 1-year (mortality, MLLA revision and ambulation). The accuracy of surgeons' predictions will be evaluated and compared to pre-existing risk prediction scoring tools. Results: PERCEIVE launched on 01/10/2020 with 23 centres (16 UK, 7 international) registered to collect data. 50 other centres (27 UK, 23 international) have expressed interest/are pursuing local audit/ethical approval. We aim to collect data on clinicians estimate of outcomes for over 500 patients. Discussion: This study will utilise a trainee research network to provide data on the accuracy of healthcare professionals' predictions of outcomes following MLLA and compare this to the utility of existing prediction tools in this patient cohort.