Go with the flow: implanted sensor assesses blood flow to monitor patients’ prosthetic valves
Researchers from Switzerland have developed a smartphone-enabled wireless and battery-less blood flow sensor to monitor patients who have prosthetic heart valve implants. The small implantable sensor wirelessly transmits data to patients’ smartphones allowing for remote management of the patient.
Prosthetic heart valves have become an important answer to valvular heart disease, since their first use in the 1950s.1,2 Over 300,000 surgical heart valve replacement procedures are performed worldwide annually.1 And by 2050 the demand for heart valve interventions for patients with valvular heart disease is expected to hit 850,000, worldwide.1
For patients who need valve replacement, there are two options: a mechanical heart valve or bioprosthetic heart valve. Bioprosthetic heart valves are composed of either animal or human tissue and are designed to mimic the human valve as closely as possible. One advantage of biological valves is that recipients do not need lifelong warfarin therapy to prevent blood clots. However, bioprosthetic valves are not as durable as mechanical valves and often need to be replaced within 10–20 years.3
Bringing the digital age to traditional methods
“Limited bioprosthetic heart valve durability remains an open research question,” said Bernhard Vennemann, one of the researchers involved in the new biosensor’s development, Institute of Fluid Dynamics, ETH Zurich, Switzerland. “Not only is the durability of these valves often shorter than the patient’s life expectancy, it is also very difficult to accurately predict how long an implanted valve is going to last before a reintervention is necessary. This calls for close monitoring of the valve, a process that is both time-consuming and expensive. Our research aims at automating diagnostic processes through medical sensory implants coupled with machine learning based data processing.”
Our research aims at automating diagnostic processes through medical sensory implants coupled with machine learning based data processing.
Inspired by Michael Faraday’s 1832 famous experiments measuring water flow under London’s Waterloo Bridge, the Swiss team set out to understand the patterns of blood flow downstream of heart valves.4 The team hypothesised that altered blood flow would indicate the functional state of the artificial valve and any dysfunction or degradation of the implant. In turn, frequent assessment of prosthetic valve function can impact a patient’s long-term outcomes and help plan the optimal time for future valve replacement procedures.5 Faraday had discovered that electromagnetic induction occurs when a conductor (the Thames river, in his experiments) flows through a magnetic field (the earth’s magnetic field).4 Using this same principle, the team designed a sensor to assess blood flow in the heart, which would allow blood in the heart to become electrically conductive via magnetic field. “The sensor adopts the principles of a magnetic flow sensor, a flow measurement technique that is commonly found in engineering applications for its reliability and accuracy, even in challenging environments,” explained Vennemann.
Lab testing and machine learning
In the first part of their research, the team tested an in vitro flow loop, capable of recreating the flow and pressure conditions of human circulation in a lab setting. The researchers used different tube-shaped inserts, which allowed deliberate introduction of aortic regurgitation of varying severity and they then measured blood flow. The team came up with a diagnostic index that indicated severity of valve degradation, with high sensitivity towards pathological changes in valvular function. This allowed them to automatically identify valvular degradation.5
In the second part of their research, the team designed an implantable magnetic blood flow sensor, which was optimised for small size, low power consumption and no need for batteries.6 The data is transmitted wirelessly to a smartphone, which can remotely assess the information, while also providing the energy to power the implant.
While magnetic flow meters have been around for a while, the team spent some time redesigning essential components to allow for the sensor’s ultra low-power use and its small size. And as blood is a conductive liquid, it will experience an electromotive force as it passes through the magnet array from the sensor. This produces differing voltages across the fluid, which are picked up with electrodes that are in contact. These signals are amplified and filtered before being transmitted wirelessly to a smartphone.
“The data is automatically processed using machine learning algorithms, which are able to automatically differentiate between physiological and pathological measurements and is able to inform the physician of valve degradation,” said Vennemann.
Goals and next steps
These initial results are exciting, and the team hope to move from the lab to testing their sensor in animals. Vennemann said his inspiration for this project was the vision of transitioning healthcare to the digital age with solutions that are cost-effective and scalable, while ensuring the highest level of medical care and quality of life for patients.
Vennemann is grateful for the medical and cardiovascular expertise that his team received as they developed their sensor. “The strong link between blood flow mechanics and cardiovascular health is the source for our research initiatives in the field.” The team worked closely with medical professionals to understand aspects of implants, biocompatibility and practicality. Vennemann explains the importance of involving other fields in their research. “Developing medical technology is a truly interdisciplinary effort that requires experts from different fields,” said Vennemann.
Li KYC. Bioprosthetic heart valves: upgrading a 50-year old technology. Front Cardiovasc Med 2019; 6:47.
Harris C, et al. Tissue and mechanical heart valves. Ann Cardiothorac Surg 2015; 4:399.
I.E.C. Michael Faraday and platinum: “This beautiful, magnificent and valuable metal.”Platinum Metals Rev 1991: 35:222–227.
Vennemann B, et al. Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning. PLoS ONE 2019; 14(9):e0222983.
Vennemann B, et al. A smartphone-enabled wireless and batteryless implantable blood flow sensor for remote monitoring of prosthetic heart valve function. PLoS One 2020; 15(1):e0227372