If patients in intensive care or undergoing major surgery experience extreme fluctuations in blood pressure, the consequences can be dire, potentially leading to severe organ dysfunction. Simply knowing that blood pressure is abnormal isn’t enough. To administer the most effective treatment, doctors need to understand the underlying cause of the change. A new study from MIT introduces a mathematical framework that provides this crucial information accurately and instantaneously.
This novel mathematical approach, detailed in a recent open-access study published in IEEE Transactions on Biomedical Engineering, generates proportional estimates of the two critical factors influencing blood pressure: cardiac output (the heart’s blood output rate) and systemic vascular resistance (the arterial system’s resistance to blood flow). By applying this method to existing data from animal models, the researchers demonstrated that their estimates, derived from minimally invasive peripheral arterial blood pressure measurements, precisely matched estimates obtained using invasive flow probes placed on the aorta. Furthermore, the estimates accurately tracked changes induced by various drugs commonly used to regulate blood pressure.
The study’s authors emphasize the practical implications of their findings: “Estimates of resistance and cardiac output from our approach provide information that can readily be used to guide hemodynamic management decisions in real-time.”
With further testing and regulatory approval, the authors envision this method being implemented during heart surgeries, liver transplants, intensive care unit treatment, and numerous other procedures that impact cardiovascular function or blood volume.
“Any patient who is having cardiac surgery could need this,” explains study senior author Emery N. Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at The Picower Institute for Learning and Memory, the Institute for Medical Engineering and Science, and the Department of Brain and Cognitive Sciences at MIT. Brown is also an anesthesiologist at Massachusetts General Hospital and a professor of anesthesiology at Harvard Medical School. “So might any patient undergoing a more normal surgery but who might have a compromised cardiovascular system, such as ischemic heart disease. You can’t have the blood pressure being all over the place.”
The study’s lead author is electrical engineering and computer science (EECS) graduate student Taylor Baum, who is co-supervised by Brown and Munther Dahleh, the William A. Coolidge Professor in EECS.
Algorithmic Advance
The foundation of this breakthrough lies in the two-element Windkessel model, which posits that cardiac output and systemic resistance are the two primary determinants of blood pressure. While previous attempts have been made to estimate these components from blood pressure measurements using this model, they often encountered a trade-off between the speed of estimate updates and their accuracy. Methods either provided less accurate estimates at each heartbeat or more reliable estimates that were updated at minute intervals.
The MIT team, spearheaded by Baum, overcame this obstacle by introducing a novel approach that leverages statistical and signal processing techniques, such as “state-space” modeling.
“Our estimates, updated at every beat, are not just informed by the current beat; but they incorporate where things were in previous beats as well,” Baum elaborates. “It’s that combination of past history and current observations that produces a more reliable estimate while still at a beat-by-beat time scale.”
It’s important to note that the resulting estimates of cardiac output and systemic resistance are “proportional,” meaning they are mathematically intertwined with another co-factor, rather than being estimated independently. However, when applied to data collected from six animals in a previous study, the new method demonstrated that proportional estimates derived from minimally invasive catheter recordings provided comparable information for cardiovascular system management.
One significant finding was that proportional estimates based on arterial blood pressure readings from catheters placed in various peripheral locations (e.g., the leg or arm) mirrored estimates obtained from more invasive catheters positioned within the aorta. This discovery suggests that a system employing this new estimation method could potentially rely on a minimally invasive catheter in peripheral arteries, eliminating the need for riskier central artery or pulmonary artery catheter placements directly in the heart, which is currently the clinical gold standard for cardiovascular state estimation.
Another crucial finding was that when the animals were administered each of the five drugs commonly used to regulate systemic vascular resistance or cardiac output, the proportional estimates accurately reflected the resulting changes. This suggests that the proportional estimates of each factor reliably track their physiological variations.
Toward the Clinic
Given these promising results, Baum and Brown believe that this method is ripe for implementation in clinical settings to provide perioperative care teams with real-time insights into the underlying causes of critical blood pressure fluctuations. They are actively pursuing regulatory approval for the use of this method in a clinical device.
Furthermore, the researchers are conducting additional animal studies to validate an advanced blood pressure management approach that utilizes this method. They have developed a closed-loop system, informed by this estimation framework, to precisely regulate blood pressure in an animal model. Upon successful completion of these animal studies, they plan to seek regulatory clearance for human trials.
In addition to Baum, Dahleh, and Brown, the paper’s other authors are Elie Adam, Christian Guay, Gabriel Schamberg, Mohammadreza Kazemi, and Thomas Heldt.
This research was supported by the National Science Foundation, the National Institutes of Health, a Mathworks Fellowship, The Picower Institute for Learning and Memory, and The JPB Foundation.
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