All-cause, real-time predictive Analytic for Hemodynamic Instability (AHI)
AHI uses sophisticated real-time signal processing and a clinically validated machine learning model to predict hemodynamic instability from a single lead of an ECG.
How AHI works.
The human body’s hemodynamic compensatory mechanisms, driven by the autonomic nervous system, protect against shifts in blood pressure to preserve critical organ function. However, such compensatory responses can mask underlying physiology and delay intervention, especially when relying on traditional vital signs. Once the vital signs and symptoms that show decompensation are evident, it may be too late to effectively intervene. Late detection of hemodynamic instability can lead to more invasive interventions, poorer outcomes, and even death.
AHI leverages the known relationships between the cardiac rhythm, heart rate variability and the autonomic nervous system. Using an innovative peak-detection and noise isolation approach, AHI examines precise beat-to-beat variations and morphological transitions within the ECG to quantify the physiologic and autonomic nervous system burden well before it is evident via traditional vitals. These nuanced cardiac variations associated with hemodynamic decompensation are typically obscure and cannot be visually observed by manual examination of streaming ECG, especially during the early period of its onset.
The framework and mathematics used to build AHI have been specially designed to operate in real-time on existing streaming ECG data from a single lead.
Existing data. New insights.
So, if the ECG data was always there, why does it work now? Well, since ECG signals have better signal to noise ratio than other signals, we were able to develop novel techniques for signal processing and machine learning, along with new math techniques to perform complex pattern recognition on streaming data. That combined with the ability of computers to now affordably and efficiently process large volumes of data has allowed the development of AHI.
The key value proposition is in accurately predicting problems before they happen to aid in the recognition and rescue of the deteriorating patient…this makes it a novel next generation monitoring and predicting tool.
We have a couple of teams dabbling in "early warning" models but they have not distilled it down to a single ECG lead!
The linkage of an early response team with this technology would have a very high likelihood of being associated with a significant survival benefit.
This is an amazing breakthrough! I can see so many applications within the hospital. I would like to get you connected to our innovation center so that we could be involved in its development.
Yes, this is a game-changer. The industry absolutely needs a better detection system for hemodynamic instability.
All I can say is wow! That is awesome.