302 MONITORING FALLS RISK IN THE COMMUNITY USING AN IMPLANTABLE CARDIAC MONITOR WITH EMBEDDED ACCELEROMETER. (25th October 2022) - Bourke, R; Perez, S; Mogollo, AZ; Finucane, C; Leenders, M; Roberts, F; Morren, G; Maree, A; De Melis, M; Kenny, RA; Foran, T
Abstract: Background: Falls are the most common cause of injury amongst older adults. Falls can lead to hospitalisation, functional decline and are associated with increased morbidity and mortality. The holy grail for clinicians would be to predict increased likelihood of falls occurring and intervene before the event. Understanding underlying dynamic biophysiological changes may therefore inform novel predictor models and falls prevention. This study examines activity and cardiac data acquired from an implanted Medtronic Reveal LINQ™ Insertable Cardiac Monitor (ICM) with an embedded tri-axial accelerometer. Methods: Thirty participants with at least one unexplained fall in the previous two years were prospectively recruited. All met criteria for ICM insertion following comprehensive assessment. Participants were followed for one year and attended every three-months for cardiac and gait assessment. Information pertaining to activity levels, posture changes and cardiac parameters were collected daily from the device. Summary metrics and trends were collected for inclusion in a continual assessment of falls risk. Results: Mean age of participants was 68.0 years (±9.3). 19/30 (63.3%) were female. 22/30 (73.3%) had at least one cardiovascular condition documented in their medical history. There was seasonal variation in activity levels. Twelve participants had falls and cardiovascular, gait and activity variables were examined at the time of a fall to determine any trends in biophysiological changes. Conclusion: Causes of falls are usually multifactorial. A holistic approach is necessary to manage and minimise risk factors. The use of an ICM with an embedded tri-axial accelerometer allows clinicians to formulate an algorithm to determine if a person is at an increased risk of falling based on biophysiological changes. This may create an opportunity for falls to be predicted and prevented.