Using data to predict the unpredictable
So, the big question is: can the attractor method be used to diagnose or detect sepsis earlier in patients?
Currently, the team are reliant on using archived data to build their attractor. They are comparing existing data collected from patients who came into hospital – some developed sepsis during their stay, others didn’t. Manasi explains, “we are still very much in the earliest stages of research. We're feeding known data into the system. We use our attractor data combined with the clinical notes - the annotations that the doctor put on the original record - to see if our method could have predicted that a patient was going to deteriorate earlier than the clinical notes suggests.”
By re-examining existing data, they can see if there are any emerging patterns in the attractor. “If we’re successful at finding attractor signatures that correlate with particular patients, then we can start to build algorithms that could be installed into existing software and used in a clinical environment.” So, when a patient has particular attractor features that associate with sepsis, for example, this could trigger an alert to the clinical team.
The preclinical tests have shown promising results, suggesting clear differences in the attractor between a healthy subject and one in the early stages of sepsis – but this has to be validated in humans.
Manasi explains, “what's interesting is if you just look at the conventional waveform measures of blood pressure, you wouldn't necessarily know that there was anything wrong. The attractor seems to be very sensitive at detecting something that happens in those really early stages of sepsis, once that microbial infection is taking hold. Something happens in the cardiovascular system and it seems that we're able to sensitively detect that with our method. In the conventional signal, it only becomes obvious about four or five hours later.”
One of the reasons for this is that the human body has evolved to divert blood from certain body systems, such as the gut and skin, in order to maintain blood pressure to vital organs such as the heart and brain when the body goes into ‘shock’. This can mean that a conventional signal like measured blood pressure can appear normal even though systems have started to close down in the body and the patient is actually getting ill.
Manasi’s initial research has shown that the attractor method is very sensitive at detecting cardiovascular changes in the earliest stages of sepsis compared to conventional cardiovascular measures such as systolic/diastolic blood pressure or heart rate.
It’s this early warning system that has the potential to save lives in settings ranging from an ICU to the Accident and Emergency Department.
Manasi says, “I'm really excited to be working on this project because it has immediate potential in the clinic. With a lot of the research that I've done to date you're 10, 15, 20 years away from it ever being tested in patients. Whereas something like this we're starting to test the system within 5 years - that's really exciting.”
“In terms of how that would impact on healthcare as a whole? If we can diagnose these patients earlier, they've got a much greater chance of surviving and leaving hospital. And the earlier you do it, the less likely they are to have ongoing complications. There's the benefit to the patient and their families. But there is also a huge benefit to the healthcare system because it means that those patients are not going to be hospitalized for as long. There are both economic and societal benefits of an early detection method.”
Manasi says, “attractor reconstruction has very wide application that is above and beyond recognition of patients with sepsis. But we're applying it to sepsis in the first instance because that’s a really big clinical problem, and it's one where patients are already routinely monitored using continuous bedside monitors – so we have the data we need.”
Attractor reconstruction has the potential to be extended to predicting a range of health conditions and could even be used to help understand how effective drugs are, or whether they carry a safety risk.
Manasi says, “We are in the earliest stages, but the project is getting into quite an interesting space at the moment. We’re collaborating with lots of different doctors and research scientists and they're sending us their data and we are starting to see if this method can be applied to other areas.”