Link-HF, Phase II: Multisensor Non-invasive Telemonitoring System for Prediction of Heart Failure Exacerbation
Status: | Completed |
---|---|
Conditions: | Cardiology |
Therapuetic Areas: | Cardiology / Vascular Diseases |
Healthy: | No |
Age Range: | 18 - Any |
Updated: | 1/11/2018 |
Start Date: | June 2015 |
End Date: | January 26, 2017 |
This is a multi-center, non-randomized, non-interventional study to evaluate the accuracy of
a remote monitoring and analytical platform for prediction of heart failure exacerbation. The
platform acquires continuous multivariate vital signs from HF patients using a new ambulatory
wearable (attached by an adhesive) multi-sensor device and analyzes the data using a novel
machine learning algorithm.
a remote monitoring and analytical platform for prediction of heart failure exacerbation. The
platform acquires continuous multivariate vital signs from HF patients using a new ambulatory
wearable (attached by an adhesive) multi-sensor device and analyzes the data using a novel
machine learning algorithm.
The analytics being investigated includes a Similarity-Based Modeling technique, that
empirically estimates the expected physiological behavior of a subject based on prior learned
dynamic data, for comparison to actual measured behavior from the subject, to reveal
discrepancies hidden by normal variation. The measurements are typically an ensemble of vital
signs that effectively characterizes the physiological "control system" of the subject. This
technique is multivariate: multiple variables are leveraged, because single variables in
isolation have little context - a high heart rate by itself could mean a person is exerting
himself, or it could mean his physiology is in distress even though he is not exerting
himself. With reference to several other variables, however, such as respiration rate,
oximetry and motion/activity, a high heart rate might be recognized as a normal state when
accompanied by the corroborating data showing a high respiration rate, a normal oximetry and
a high level of motion - the person is exercising.
A wearable adhesive multi-sensor device will be used to collect continuous vital sign and
other data from study subjects, including heart rate, respiration rate, bodily
motion/activity, skin temperature, pulse, electrocardiogram and peripheral capillary oxygen
saturation. Subjects are provided with a smartphone or cellular tablet that will be paired
with the multi-sensor device to receive data and upload it to the analytics server via
cellular network or WiFi internet. Study staff will interact with the subject during visits
scheduled for routine heart failure follow-up to capture pre-specified heart failure medical
events. All standard of care clinic and hospitalization notes and procedure reports including
echocardiograms, right heart catheterizations, pulmonary function tests, six minute walk
tests and radiology reports will be collected as they occur.
empirically estimates the expected physiological behavior of a subject based on prior learned
dynamic data, for comparison to actual measured behavior from the subject, to reveal
discrepancies hidden by normal variation. The measurements are typically an ensemble of vital
signs that effectively characterizes the physiological "control system" of the subject. This
technique is multivariate: multiple variables are leveraged, because single variables in
isolation have little context - a high heart rate by itself could mean a person is exerting
himself, or it could mean his physiology is in distress even though he is not exerting
himself. With reference to several other variables, however, such as respiration rate,
oximetry and motion/activity, a high heart rate might be recognized as a normal state when
accompanied by the corroborating data showing a high respiration rate, a normal oximetry and
a high level of motion - the person is exercising.
A wearable adhesive multi-sensor device will be used to collect continuous vital sign and
other data from study subjects, including heart rate, respiration rate, bodily
motion/activity, skin temperature, pulse, electrocardiogram and peripheral capillary oxygen
saturation. Subjects are provided with a smartphone or cellular tablet that will be paired
with the multi-sensor device to receive data and upload it to the analytics server via
cellular network or WiFi internet. Study staff will interact with the subject during visits
scheduled for routine heart failure follow-up to capture pre-specified heart failure medical
events. All standard of care clinic and hospitalization notes and procedure reports including
echocardiograms, right heart catheterizations, pulmonary function tests, six minute walk
tests and radiology reports will be collected as they occur.
Inclusion Criteria:
1. Subject must be 18 years old or older
2. NYHA( New York Heart Association Functional Classification) Class II-IV, documented in
site's medical record system.
3. Subject able and willing to sign Informed Consent Document.
4. Subject willing and able to perform all study related procedures.
Exclusion Criteria:
1. Expected LVAD (Left Ventricular Assist Device) implantation or heart transplantation
in the next 30 days.
2. Skin damage or significant arthritis, preventing wearing of device.
3. Uncontrolled seizures or other neurological disorders leading to excessive abnormal
movements or tremors in the upper body.
4. Pregnant women or those who are currently nursing.
5. Visual/cognitive impairment that as judged by the investigator does not allow the
subject to independently follow rules and procedures of the protocol.
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Michael E. Debakey VA Medical Center The Michael E. DeBakey VA Medical Center serves as...
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