Save lives by predicting a future heart failure through remote monitoring
Someone has suffered from a cardiac arrest. Their life has been saved but a huge risk to their life still looms ahead. A second fatal cardiac failure might typically occur in the next month. Monitoring 24x7 is key, but it is also logistically cumbersome and very costly.
This was the brutal briefing we got from Srinivasan Murali and Francisco Rincón, SmartCardia co-founders. "It's about reducing the traumatic re-hospitalization rate that we see these days through real-time remote ECG trend analysis".
At the prestigious École polytechnique fédérale de Lausanne, SmartCardia startup had been kept busy developing a unique wearable.... wearable. Patients can carry it on with almost zero hassle. However, they needed a platform that would collect thousands of data points in real time. Not only that, also process them to early detect troubling trends. It was critical to develop this platform and we loved the challenge. We'd develop a Virtual Hospital Intensive Care Unit (ICU).
More than 70% of all medical costs come from chronic disease management. This expense could be drastically lowered if hospitalizations and unnecessary use of emergency services were reduced and therefore diverted into other much needed areas in healthcare. More than 60% of hospital emergencies are not real emergencies. A significant percentage of emergencies due to congestive heart failure fall into this category but there is no way we could be telling patients to not come if they fear they might be suffering from such heart failure; The fear of irreversible consequences is very real. So, how can we be smart about this?
The answer lied in bringing wearable technology and machine learning into a medical doctor oriented platform.
SmartCardia had to finish miniaturizing their wearable patch. Making sure it was up to ICU standards while sporting several days of battery life. If a patient was to be sent home with one of these patches, it had to resemble a remote ICU without all the cables and probes, just sticking it onto your breast. At the same time, Kaleidos had to develop all the software needed to monitor dozens of these wearables, process real-time signals, analyse everything using signal processing and time series machine learning technology and show data, charts and graphs on a robust and accurate web platform.
But, could we actually develop a time machine to predict future cardiac failures? The academic research so far was promising, but just that, promising.
Heart diseases come in all shapes and colours but there is an event that is very much linked to a big portion of those, Atrial Fibrillation. Detecting Atrial Fibrillation with enough time to assist the patient remotely can boost prognosis and avoid unnecessary hospitalization.
We developed a Machine Learning engine that would be trained through carefully selected annotated data. We employed a total of 110.135 Electrocardiograms (EGC) segments labelled as "normal" and 129.601 ECG segments labelled as Atrial Fibrillation coming from reliable public databases.
Next, we needed to find out how well our Machine Learning model fared compared to medical doctors, while reducing processing time as much as possible. To do so, we went from 200 defining ECG features for our model to just 43 carefully selected. With those, our Machine Learning model was able to almost outperform three medical doctors in several double-blind experiments. And most importantly, do it so in almost real-time instead of waiting minutes or even hours for a proper diagnosis. There were various hospitals involved throughout this applied research but we would like to thank Hospital Ramón y Cajal in Madrid, and the staff at Cardiology Unit.
The winner classifier (related to Sensitivity and Specificity) used ADA Boost based on Random Forest with 100 estimators. It was able to achieve 95% overlap with medical doctors diagnose (which enjoy 100% success rate if we combine two or more of them).
Building this medical platform was no small feat either. There were various different modules for a complex platform architecture that should be able to withstand heavy hospital loads and recover from lost Internet signal at the patient's side. The main modules were:
Recent academic research had linked some cardiovascular parameters to blood pressure. High blood pressure combined with other trends are clear indicators of imminent heart failure. Since the wearable patch is not invasive and can't measure blood pressure, we thought we might be able to infer blood pressure using machine learning and complex classifiers. Unfortunately, even though we outperformed all other competitors, we fell short of our major goal. More research and work will be needed.
With 10 clinical studies in various hospitals around the world, totalling more than 3.000 patients, we have been receiving wonderful feedback from the real professionals in the field, expert cardiologists. The Ramon y Cajal Hospital in Madrid, one of the most prestigious in Spain, has already been using the platform to real-time monitor 20 patients. Other pilots in India will add 100 more patients and another 25 with both HF-Predict and Holter. Denmark is the most recent country to join the list with 30 more patients being monitored via HF-Predict.
This means that HF-Predict performs great under real-world stressful situations where many legacy systems are the norm and resistance to change is common.
It's no wonder than the HF-Predict software platform and the wearable patch were awarded the CE marking, which allows full scale commercialization.
Go-to-market time for us was critical, Kaleidos delivered on time, amazing committent and work!Visit HF-Predict Website