News

Non-invasive technology is rapidly expanding opportunities for remote patient monitoring to improve outcomes. Cardiovascular researchers are joining the effort with two approaches that tap into juvenile humor and may reduce re-hospitalization rates for heart failure.

Home monitoring is challenging because of patient adherence, and especially low in the heart failure population. As a result, these patients are frequently hospitalized, and about a quarter of them are readmitted within the first 30 days, which leads to greater frustration for them and their families.

Wearable Sensors for HF: Valentina Kutyifa, associate professor of Cardiology, and Spencer Rosero, interim chief of Cardiology, are studying wearable sensors to monitor patients after hospitalization to capture vital signs and whether there are indicators of one-year outcomes.

Using Spire Health Tags for continuous monitoring, they will assess heart failure patients' respiration, heart rate, activity, sleep patterns and stress levels. Participants will enroll at hospital discharge and use the wearable sensors to provide the essential data.

The two-inch, felt-covered sensors attach to underwear for essentially around-the-clock data collection, according to Kutyifa. Once attached to undergarments, the sensors can be laundered and used for as long as a year.

The 30-day study will analyze the various indicators and compare data with patient outcomes. The goal is to establish feasibility of this new approach and to explore potential physiological markers that could predict risk of readmission.

FIT Seat: Working in partnership with Rochester Institute of Technology, cardiovascular researchers led by Wojciech Zareba, professor of Cardiology, will study the use of the Fully-Integrated Toilet Seat, or FIT Seat, in monitoring heart function in this challenging population. The work is funded by a new $2.9 million grant from the National Institutes of Technology.

The high-tech toilet seat was developed in 2014 by Rochester Institute of Technology engineers David Borkholder and Nicholas Conn, and Karl Schwarz, professor of Cardiology and director of the Echocardiography Laboratory. The FIT Seat has built-in sensors and uses machine learning and artificial intelligence to measure and transmit blood pressure, weight and heart rate and other key indicators to physicians. The data points will also be unique to each patient involved in the study.

The sensor algorithm in the FIT Seat is "smart" in that it can be trained to recognize patterns and characteristics that will be distinct to each person, even taking into account the communal nature of the toilet seat in an individual's home.

"There are a number of factors that can be evaluated in these patients. It is like having a patient on bedside monitoring in an intensive care unit," Zareba said. "At home, people don't usually have these monitoring tools and even if it is not continuous, it will be used by patients several times per day, and each time, it will record data and send it to be processed."

Zareba is working with Schwarz, Leway Chen, professor of Cardiology and medical director of the Advanced Heart Failure Program, and Robert Strawderman, chair and Distinguished Professor of Biostatistics and Computational Biology, to launch a study of 160 patients with heart failure in spring 2021.

Toilet seats with high tech sensors might be the non-invasive technology of the future that could help reduce hospital return rates of individuals with heart disease.

Heart failure is one of the leading causes of adults admitted to hospitals and more than six million adults in the United States have heart disease, according to the American Heart Association. Re-hospitalizations occur in some instances within 30 days to 6-months of initial treatment. Having a way to intercept these rehospitalizations might afford patients improved care and decrease costs.

A joint project by researchers at Rochester Institute of Technology and the University of Rochester Medical Center (URMC), will determine if in-home monitoring can successfully monitor vital signs and reduce risk and costly re-hospitalization rates for people with heart failure. The five-year, $2.9 million venture, is funded by the National Institutes of Health (NIH).

The new Fully-Integrated Toilet Seat, or FIT Seat monitoring system will incorporate artificial intelligence and improved user interfaces to provide physicians with up-to-date patient data over time and in a format that is easily readable. Artificial Intelligence technology will be added as part of an early alert system to help physicians identify possible deterioration sooner, said David Borkholder, the Bausch and Lomb Professor in RIT's Kate Gleason College of Engineering. He and Wojciech Zareba, MD, professor of Medicine, Cardiology at the University of Rochester Medical Center, will lead a multi-disciplinary research team to further develop the technological functionality of the FIT Seat.

The CCRC and CTU would like to announce some exciting news!

We have reached 500 enrollments in the BIO-LIBRA study, halfway at our enrollment target of 1000 patients. We currently have 46% of the enrolled subjects women, exceeding our initial goal of 40%. UofR (CTU) has enrolled 13 subjects so far (54% women)!

This is an unprecedented success, especially during a pandemic!!

Please find below an article that was released on this special occasion of reaching 500 enrollments.

https://news.biotronik.com/women-still-underrepresented-in-clinical-study-patient-groups/

https://clinicaltrials.gov/ct2/show/NCT03884608

A new study leverages AI-based technology to offer a contactless monitoring method for atrial fibrillation (AF) without the use of a dedicated device. The method reveals an accurate way to effortlessly identify irregular pulse rates (PR) using automatic selfie video novel videoplethysmography (VPG) software that can reside on most smartphones. Findings of the single, observational study were presented today as part of the Heart Rhythm Society 2020 Scientific Sessions.

These findings come at a critical time when patients and physicians are becoming more reliant on telemedicine, and the COVID-19 pandemic highlights a need for contactless monitoring innovations. AF is a growing public health concern as the heart rhythm disorder impacts more than 33.5 million individuals globally1 and is associated with high rates of reoccurrence. Further, approximately one-third of AF patients do not show symptoms.2 New technologies can enable the detection of irregular PR and arrhythmias in asymptomatic patients or patients who would benefit from long-term monitoring.

The primary advantage of VPG technology is to enable long-term intermittent monitoring of PR without the burden of using wearable devices that need to be in contact with the skin or that require other compliance from the patient. VPG technology is embedded into a simple app running on smart devices to acquire the PR of the user. VPG signal is captured by the video camera by detecting subtle changes in the facial skin color during each heartbeat. The smart device does not record the face of the user, thereby preserving the user's privacy. The study evaluated the accuracy and quality of VPG signal captured in an uncontrolled environment.

"With the growing incidence of AF and no definite cure in sight, we sought to provide a reliable solution that would work with a simple download to a patient's device," said Dr. Jean-Philippe Couderc, Principal investigator of the NIH-funded study conducted at the Clinical Cardiovascular Research Center of the University of Rochester Medical Center (NY). "With 'always-on' technology that can work in the background as patients go about their daily lives, we hope this will help the way patients track their pulse rates and heart rhythm remotely. We see our findings as an exciting way to use AI and mobile devices to push the monitoring experience forward."

In the study, 60 subjects (47 men, 13 women), aged 65±8 years, were enrolled after successful electrical cardioversion or successful AF ablation. Subjects were provided a smart tablet loaded with the VPG technology and an electrocardiogram (ECG) patch for 14 days, and were asked to use a tablet twice a day using an application extracting VPG signals. The average PR and heart rate (HR) vales were extracted from the synchronized VPG and ECG signals, respectively. Machine learning was trained to reject VPG recordings associated with an error >10 percent in reference to HR using a 30/70 percent split of the data (validation based on 30 percent). Findings recorded 880 video-based PR in sinus rhythm from June 2018 to May 2019. Subjects wore the ECG patch for 11 days on average (ranging from 1 to 15 days). The recorded HR varied between 40 and 122 bpm. Random Forest model was trained to reject measurements with an error <10% between VPG and ECG rates. Bland Altman applied to the validation set revealed a mean difference between PR and HR of 0.3±9.8 bpm while rejecting 33% of the VPG signals for low signal quality.

The authors place importance on complete findings as the current study is still taking place. As a next step, they plan to extend the use of this technology to determine its value in monitoring heart failure patients.

The CCRC is excited to share that they have just reached 300 enrollments in the BIO-LIBRA study (30% of total enrollments) in 10 months. There are 45% women enrollments -exceeding the initial goal.

Congratulations to the BIO-LIBRA team at CCRC on their outstanding work and dedication to the study!

Please find below a recent article published on the background and goals of the BIO-LIBRA study.

https://www.dicardiology.com/article/bio-libra-study-evaluates-role-sex-and-device-type-non-ischemic-cardiomyopathy