Ignite Creation Date:
2024-05-06 @ 2:53 PM
Last Modification Date:
2024-10-26 @ 1:39 PM
Study NCT ID:
NCT04455828
Status:
WITHDRAWN
Last Update Posted:
2023-01-26
First Post:
2020-06-29
Brief Title:
Wearable Remote Monitoring of Heart Rate and Respiratory Rate for Heart Failure
Sponsor:
Milton S Hershey Medical Center
Organization:
Milton S Hershey Medical Center
Study Overview
Official Title:
Wearable Remote Monitoring of Heart Rate and Respiratory Rate for Heart Failure
Status:
WITHDRAWN
Status Verified Date:
2023-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
IRB Approval lapsed
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The primary objective of this study is to study in heart failure HF patients to better assess HF disease state which can aid in management and improve outcomes Primary aims of the study include 1 Measure HR and RR at rest and during daily activity using the WHOOP device 2 Correlate HR and RR response to activity to New York Heart Association NYHA class and 90-day HF hospitalization rate 3 Identify additional predictors of NYHA class and HF hospitalization rate for algorithm development to use the WHOOP device as a clinical tool for HF management
Detailed Description:
Heart Failure HF is a challenging condition to manage with hospital readmission for HF exacerbation having negative impacts on patient outcomes and financial burden to both patient and health system Lloyd-Jones 2010 Yancy 2017 Ross 2009 Chaudhry 2007 An intuitive need for more sensitive predictors of HF exacerbations has led researchers to explore remote monitoring as a possible answer Consumer-owned sensors have become more accurate in their recording of vital signs and thus could hold potential for remote monitoring Dickinson 2018 The combined measure of heart rate HR and respiratory rate RR has been shown to predict New York Heart Association NYHA HF class an indicator of severity of heart disease in implantable cardiac devices with multi-sensor monitoring capabilities Auricchio 2014 Prasun 2019 Boehmer 2015 Boehmer 2017 Heart rate variability HRV a measure of sympathetic autonomic function has also shown potential in prediction of adverse cardiac events Al-Zaiti 2019 Shaffer 2017 Bullinga 2005 Tsuji 1996
The WHOOP device a wearable strap similar to a Fitbit allows for real-time HR monitoring and can determine RR using respiratory sinus arrhythmia wwwwhoopcomexperience Berryhill 2020 It is one of the few devices on the market that can accurately track heart rate as well as respiratory rate in real-time during activity and is equipped with a multidirectional accelerometer for activity tracking The WHOOP device was recently externally validated against polysomnography and continuous electroencephalogram EEG for sleep tracking and continuous electrocardiogram ECG for HR and HRV with less than 5 error Berryhill 2020 HRV which represents the balance of the sympathetic and parasympathetic nervous systems is a known predictor of cardiac events It is especially useful in HF which is a chronically elevated catecholamine state leading to depressed HRV and is tied to NYHA HF class an indicator of severity of disease Bullinga 2005 Tsuji 1996
Data so far regarding the efficacy of remote physiologic monitoring using cardiac implantable electronic devices CIEDs although promising in theory has not yet proved sensitive in the detection of HF exacerbation The aim of the CLEPSYDRA study was to use data extracted from implanted cardiac resynchronization therapy with defibrillation CRT-D devices in HF patients to predict heart failure events although the main variables used in the novel algorithm minute ventilation and patient activity would intuitively seem to be predictors of poor outcomeHF exacerbation the sensitivity of the algorithm to predict an event was only 34 6 It would appear that this combination of variables is not sufficient to predict adverse HF events However the HOME-CARE HOME Monitoring in CArdiac REsynchronization Therapy study showed more promising results as their enhanced predictor utilizing seven diagnostic variables from implanted CRT-Ds boasted a sensitivity of 654 Sack 2011 While the data from these studies is helpful no study has been able to adequately identify and assess accurate predictors of HF class
Current efficacious management strategies are limited to hemodynamic or multisensor monitoring systems However these are only available in implanted cardioverter-defibrillator ICD or cardiac resynchronization therapy-defibrillator CRT-D devices These are not implanted in every HF patient Al-Zaiti 2019 Non-invasive monitoring that provides similar data such as wearable device monitoring would expand the cohort of patients that would benefit from remote monitoring and would avoid the risks of having implanted hardware Furthermore better prediction of HF severity could help guide follow-up care and predict HF events Boehmer 2015 Boehmer 2017 This would lead to more efficient management less hospital readmission and improve outcomes for HF patients overall Dickinson 2018
The investigators propose a feasibility study in HF patients to better assess HF disease state which can aid in management and improve outcomes Subjects will wear the WHOOP device which measures both activity and HR parameters and can derive RR using respiratory sinus arrhythmia for 90 days During this period their HR and RR will be recorded at rest during activity and post-activity recovery phases This combined measure of HRRR has been shown to predict NYHA HF class an indicator of severity of disease in implantable devices with multi-sensor monitoring capabilities thus it represents a useful management strategy in HF patients Bullinga 2005 Tsuji 1996 A continuous external monitoring device worn on the wrist such as the WHOOP device would provide valuable physiologic data for a cohort of HF patients who were previously unable to be monitored in this fashion Secondary analysis of this study will investigate the use of intra- and post-activity HR and RR as predictors of hospitalization rates a common problem in HF patients that correlate with worse mortality outcomes
The WHOOP device a wearable strap similar to a Fitbit allows for real-time HR monitoring and can determine RR using respiratory sinus arrhythmia wwwwhoopcomexperience Berryhill 2020 It is one of the few devices on the market that can accurately track heart rate as well as respiratory rate in real-time during activity and is equipped with a multidirectional accelerometer for activity tracking The WHOOP device was recently externally validated against polysomnography and continuous electroencephalogram EEG for sleep tracking and continuous electrocardiogram ECG for HR and HRV with less than 5 error Berryhill 2020 HRV which represents the balance of the sympathetic and parasympathetic nervous systems is a known predictor of cardiac events It is especially useful in HF which is a chronically elevated catecholamine state leading to depressed HRV and is tied to NYHA HF class an indicator of severity of disease Bullinga 2005 Tsuji 1996
Data so far regarding the efficacy of remote physiologic monitoring using cardiac implantable electronic devices CIEDs although promising in theory has not yet proved sensitive in the detection of HF exacerbation The aim of the CLEPSYDRA study was to use data extracted from implanted cardiac resynchronization therapy with defibrillation CRT-D devices in HF patients to predict heart failure events although the main variables used in the novel algorithm minute ventilation and patient activity would intuitively seem to be predictors of poor outcomeHF exacerbation the sensitivity of the algorithm to predict an event was only 34 6 It would appear that this combination of variables is not sufficient to predict adverse HF events However the HOME-CARE HOME Monitoring in CArdiac REsynchronization Therapy study showed more promising results as their enhanced predictor utilizing seven diagnostic variables from implanted CRT-Ds boasted a sensitivity of 654 Sack 2011 While the data from these studies is helpful no study has been able to adequately identify and assess accurate predictors of HF class
Current efficacious management strategies are limited to hemodynamic or multisensor monitoring systems However these are only available in implanted cardioverter-defibrillator ICD or cardiac resynchronization therapy-defibrillator CRT-D devices These are not implanted in every HF patient Al-Zaiti 2019 Non-invasive monitoring that provides similar data such as wearable device monitoring would expand the cohort of patients that would benefit from remote monitoring and would avoid the risks of having implanted hardware Furthermore better prediction of HF severity could help guide follow-up care and predict HF events Boehmer 2015 Boehmer 2017 This would lead to more efficient management less hospital readmission and improve outcomes for HF patients overall Dickinson 2018
The investigators propose a feasibility study in HF patients to better assess HF disease state which can aid in management and improve outcomes Subjects will wear the WHOOP device which measures both activity and HR parameters and can derive RR using respiratory sinus arrhythmia for 90 days During this period their HR and RR will be recorded at rest during activity and post-activity recovery phases This combined measure of HRRR has been shown to predict NYHA HF class an indicator of severity of disease in implantable devices with multi-sensor monitoring capabilities thus it represents a useful management strategy in HF patients Bullinga 2005 Tsuji 1996 A continuous external monitoring device worn on the wrist such as the WHOOP device would provide valuable physiologic data for a cohort of HF patients who were previously unable to be monitored in this fashion Secondary analysis of this study will investigate the use of intra- and post-activity HR and RR as predictors of hospitalization rates a common problem in HF patients that correlate with worse mortality outcomes
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None