Frailty and Falls Implantable System for Prediction and Prevention

Summary

Participation Requirements

Description

Falls are an evolving frailty state and are the most common reason for older adults to attend the Emergency Room (ER) and for admission to long term institutional care. The Irish Longitudinal Study on Ageing (TILDA) has shown that almost 40% of older adults reported at least one fall during a four y...

Falls are an evolving frailty state and are the most common reason for older adults to attend the Emergency Room (ER) and for admission to long term institutional care. The Irish Longitudinal Study on Ageing (TILDA) has shown that almost 40% of older adults reported at least one fall during a four year period and almost 50% had 'fear of falling', an independent risk factor for falls and loss of independence. New mechanisms for monitoring early risk factors for falls will advance prevention and management of these conditions, improving healthcare and supporting independent living. Implantable devices are a new addition to the sensor market, and as yet have limited capabilities. This study is focused on 'unexplained' or 'non accidental' falls- that is falls which are not clearly due to a slip or a trip. Previous research shows that a high number of these may be due to changes in heart rate and irregular heartbeats (heart rhythm). There may also be other changes associated with non accidental falls, such as activity levels i.e. how active you are in the time before a fall. Patients under the care of FASU undergo a full clinical assessment, where the medical team aim to identify and treat factors which might contribute to falls. They often manage such falls by implanting a monitoring device which will measure heart rate and rhythm. The Reveal LINQ™ device from Medtronic™, is the implantable monitoring device which is used in FASU. There is scope to further develop implantable devices such as the Reveal LINQ™ to monitor additional physiological parameters, which may help identify fall risk factors. Medtronic in collaboration with the PI Prof Kenny have developed a RAMware update for the Reveal LINQ™ which will enable the collection of additional sensor information. The Falls Prediction RAMware is programmed externally to the Reveal LINQ™, there are no changes to the physical properties of the device. Study Aim: The aim of this project is to use the investigational build on previous work and use an implantable device (Reveal LINQ™) to monitor cardiac parameters, such as heart rate, rhythm and variability and to enhance the monitoring capabilities of the device with additional investigational software (Falls Predictor RAMware), creating the Reveal LINQ ™ Falls Prediction System (LINQ FP). The RAMware update will enable the Reveal LINQ™ device to collect additional sensor information including temperature, posture, accelerometer (step measure) and impedance measure (information on activity and fluid status), to identify early changes in these measures that may indicate increased risk of a fall. Study Design: This is a prospective, single centre, pilot feasibility study, which aims to investigate the value the Reveal LINQ ™ Falls Prediction System (LINQ FP) in predicting falls or identifying fall risk. Participants will be recruited from recurrent fallers referred to FASU for assessment. A full set of baseline assessments will be performed as necessary. Participants will have a Reveal LINQ™ Device Implanted that will be updated with the Falls Prediction RAMware. The Falls Prediction RAMware is programmed externally to the Reveal LINQ™, there are no changes to the physical properties of the device. Participants will be followed in the study for 12 months, with in clinic follow up assessments at 3, 6, 9 and 12 months Recurrent non-accidental fallers (n=30) over the age of 50 will be invited to participate in the investigation, provided both inclusion and exclusion criteria are met. The study will take place at St James's Hospital, in the Falls and Syncope Unit at MISA. Clinical data collection, processing, and data analysis will be conducted on-site by the study nurse and doctor and the on-site data manager recruited to the study team. The data collected by the investigational Falls Predictor software will be transmitted via CareLink™ and will be processed and analysed by Medtronic.

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