Frailty and Falls Implantable System for Prediction and Prevention

Frailty Clinical Trial

— FFallS  

Official title:

Frailty and Falls Implantable System for Prediction and Prevention Investigational Study - FFallS Predictor

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04881136
• Other study ID #: TRI CRF 20-01
• Status: Recruiting
• Phase: N/A
• Start date: March 23, 2021
• Est. completion date: June 30, 2022

Study information

• Verified date: June 2021
• Source: University of Dublin, Trinity College
• Contact: Sergio R Perez, M Sc
• Phone: 014284182
• Email: PEREZSR[@]tcd.ie
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The Falls Predictor Clinical Investigation is a research study that aims to investigate the value of an update (Falls Prediction RAMware) to an implantable cardiac monitoring device (The Reveal LINQ™) in predicting unexplained falls. The Reveal LINQ™ is an implantable cardiac monitoring system manufactured by Medtronic that has the ability to monitor heart rate, rhythm and activity and is preprogrammed to detect abnormalities. An R&D team at Medtronic has been collaborating with the study PI Prof Rose Anne Kenny on this project they are responsible for developing a software update for the Reveal LINQ™ that would enable the device to collect additional sensor data such as accelerometer (step count) and Posture change. The additional investigational fields along with the standard cardiac fields that are monitored may be useful in predicting or identifying physiological changes before a fall. The study will involve up to 30 patients, recruited and consented from recurrent non-accidental fallers referred to the Falls and Syncope Unit at St James's Hospital, Dublin.

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 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 30
• Est. completion date: June 30, 2022
• Est. primary completion date: June 30, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 50 Years and older

Eligibility

Inclusion Criteria:

• Referred to St James's due to a non-accidental fall (not a slip or trip), with a history of another non-accidental fall or syncope within the previous 3 years.
• Age = 50 Years
• Participant is willing and has capacity to provide informed consent to the study

Exclusion Criteria:

• Inability or unwilling to follow or perform the study protocol requirements
• Cognitive impairment (MMSE </= 20)
• Current Pacemaker or other implanted therapy devices.
• Known intolerance to subcutaneous implantable devices or any of the Reveal LINQ™ materials. 5. Life expectancy < 12 months

Related Conditions & MeSH terms

• Fall Patients
• Frailty

Intervention

Device:
• Reveal LINQ™
The physical device is the Reveal LINQ™, which is a small implantable loop recorder that is used to monitor cardiac parameters at present. The Investigational Falls Prediction RAMware is software that will be downloaded on to the Reveal LINQ™ that will enable it to collect additional sensor information including accelerometer and posture count data that will be used for gait analysis.

Locations

Country	Name	City	State
Ireland	Falls and Syncope Unit (FASU), Mercer's Institute for Successful Aging (MISA), St James's Hospital, Dublin 8	Dublin	Leinster

Sponsors (3)

Lead Sponsor	Collaborator
University of Dublin, Trinity College	Medtronic Bakken Research Center, University of Copenhagen

Country where clinical trial is conducted

Ireland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	the number of falls associated with early changes in physiological parameters as recorded by the investigational Reveal LINQ™ Falls Prediction System.	Uses the Reveal LINQ™ Falls Prediction Research System to identify early changes in physiological parameters which helps to create a profile on which to predict falls, with the potential to implement a score for the risk of falling based on monitoring of frailty parameters in the elderly measured with the implantable device.	16 months
Secondary	Established research of cardiac parameters of Reveal LINQ	building on established research of the cardiac parameters of the Reveal LINQ ™ to identify heart rate and rhythm disturbances in fallers and to evaluate their role in predicting a fall.	16 months
Secondary	Development of Clinical risk stratification algorithm	Developing a clinical risk stratification algorithm for management, treatment and prevention of frailty and Falls.	16 months