Sandpits, Better Eyes and New Monitors – Can NIRS work for prehospital medicine?

This is part 2 of a series (part 1 is here) on trying to study near-infrared spectroscopy in the prehospital setting by Dr Andrew Weatherall (@AndyDW_). Can NIRS work? No one can be sure but here’s one approach to getting some data we can actually use. 

A while back I did a post where I pointed out that when you get sold technology, there’s a whole history behind the machine that goes beep that means it’s probably not what you’re told. And the example I used was near-infrared spectroscopy tissue oximetry.

That was partly because I’m involved in research on NIRS monitoring and I’ve spent a lot of time looking at it.  Like every time I look carefully in the mirror, there’s a lot of blemishes that I miss on a casual glance. I also don’t mind pointing out those blemishes.

So that post was about all the things that could get in the way – light bouncing about like a pinball, humans being distressingly uncatlike, comparing monitors that are might be apples and aardvarks rather than apples and apples and basing your whole methodology on assumptions of tissue blood compartments. Oh, and maybe you can’t get sunlight near your red light.

Sheesh.

The thing is, I really want to answer that original question – “How’s the brain?”

So enough of the problems, can we find some solutions?

Actually I’m not certain. But I can say what we came up with. It’s a plan that involves sandpits, hiding numbers and finding better eyes. Oh, and changing the design of monitors forever and ever.

 

Playing in Sandpits

Our first step was to try and figure out if NIRS technology could even work in the places it wasn’t designed for. Not near the cosy bleeping of an operating theatre monitor where the roughest conditions might be inflicted by a rogue playlist.

We figured that the first issues might be all the practical things that stop monitors working so effectively. And we already knew that in the operating suite you often needed to provide shielding from external light to allow reliable measurements.

So we asked for volunteers, stuck sensors to their heads and took them driving in an ambulance or hopped on the helicopter to do some loops near Parramatta. It gave us lots of chances to figure out the practicalities of using an extra monitor too.

And we learnt a bit. That we could do it with some layers of shielding between the sensors and the outside world. That the device we tested, though comfortable next to an intensive care bed was a bit unwieldy at 6 kg and 30 cm long to be carried to the roadside. Most importantly that it was worth pushing on, rather than flattening everything in the sandpit and starting again.

Early engineering advice included "just put a tinfoil hat on everyone to shield the sensors". I just ... I ... can't ... [via eclipse_etc at Flickr 'The Commons']
Early engineering advice included “just put a tinfoil hat on everyone to shield the sensors”. I just … I … can’t … [via eclipse_etc at Flickr ‘The Commons’]

Hiding Numbers and Getting Out of the Way

The next thing that was pretty obvious was that we couldn’t set out to figure out what NIRS monitoring values were significant and at the same time deliver treatments on the basis of those numbers. We needed to prospectively look at the data from the monitor and see what associations were evident and establish which bits in the monitoring actually mattered for patients and clinicians.

Of course paramedics and doctors tend to like to fix stuff. Give them a  “regional saturation” number which looks a little like mixed venous oxygen saturation while the manufacturer (usually) puts a little line on the screen as the “good-bad” cutoff line is a pretty good way to see that fixing reflex kick in. So to make sure it really is a prospective observational study and we’re observing what happens in actual patients receiving their usual treatment we ended up with a monitor with none of the numbers displayed. Better not to be tempted.

It was also obvious that we couldn’t ask the treating team to look after the NIRS monitor because they’d immediately stop doing the same care they always do and occasionally (or always) they’ll be distracted by the patient from being as obsessive about the NIRS monitor as we need for research.

So recruiting needs a separate person just to manage the monitor. On the plus side this also means we can mark the electronic record accurately when treatments like anaesthesia, intubation and ventilation or transfusion happen (or indeed when the patient’s condition obviously changes). It’s all more data that might be useful.

Getting Better Eyes

One of the big problems with NIRS tissue oximetry so far seems to be that the “absolute oximetry” isn’t that absolute. When you see something claiming a specific number is the cutoff where things are all good or bad, you can throw a bucket of salt on that, not just a pinch.

 

Maybe this much salt. [via user pee vee at Flickr's 'The Commons']
Maybe all of this salt. [via user pee vee at Flickr’s ‘The Commons’]
The other thing is that to pick up evolving changes in a dynamic clinical environment is difficult. What if it isn’t just the change in oximetry number, but the rate of change that matters? What if it’s the rate of change in that number vs the change over the same time in total haemoglobin measurements, or balance between cerebral monitoring and peripheral monitoring at the same time? How does a clinician keep track of that?

What we might need is a way of analysing the information that looks for patterns in the biological signals or can look at trends. The good news is there’s people who can do that as it’s acutally a pretty common thing for clever biomedical engineers to consider. So there are some clever biomedical engineers who will be part of looking at the data we obtain. When they have spare time from building a bionic eye.

My bet is that if NIRS monitoring is ever to show real benefits to patients it won’t be only by looking at regional saturation (though we’ll try that too). It will be the way we look at the data that matters. Examining rapidly changing trends across different values might just be the key.

 

Thinking About the Monitors We Need

Let’s imagine it all works. Let’s assume that even with all those hurdles the analysis reveals ways to pretty reliably pick up haematomas are developing, or the brain is not receiving enough blood flow, or oedema is developing (and there are other settings where these things have been shown), there’s still a big problem. How do you make that information useful to a clinician who has a significant cognitive load while looking after a patient?

For each NIRS sensor that is on (3 in this study) we’ll be generating 4 measurements with trendlines. The patient is likely to have pulse oximetry, ECG, blood pressure and often end-tidal capnography too. Putting together multiple bits of information is an underappreciated skill that highly trained clinicians make a part of every day. But it adds a lot of work. How would you go with 12 more monitoring values on the screen?

Yes Sclater's lemur, that's 16 monitoring values to keep track of. [via user Tambako the Jaguar at flickr]
Yes Sclater’s lemur, that’s 16 monitoring values to keep track of. [via user Tambako the Jaguar at flickr]
So before we can take any useful stuff the analysis reveals and free clinicians to use the information, we need to figure out how to present it in a way that lets them glance at the monitor and understand it.

How should we do that? Well it’s a bit hard to know until we know what we need to display. My current guess is that it will involve getting clever graphics people to come up with a way to display the aggregated information through shapes and colours rather than our more familiar waveforms (and that’s not an entirely novel idea, other people have been on this for a bit).

So then we’d need to test the most effective way to show it before finally trying interventional studies.

This could take a bit.

And that is a story about the many, many steps for just one group trying to figure out if a particular monitor might work in the real world of prehospital medicine. There are others taking steps on their own path with similar goals and I’m sure they’ll all do it slightly differently.

I hope we end up bumping into each other somewhere along the road.

 

Notes and References:

Here’s the link to our first volunteer study (unfortunately Acta Anaesthesiologica Scandinavica has a paywall):

Weatherall A, Skowno J, Lansdown A, Lupton T, Garner A. Feasibility of cerebral near-infrared spectroscopy monitoring in the pre-hospital environment. Acta Anaes Scand 2012;56:172-7.

If you didn’t look on the way past, you should really check the video of Prof. Nigel Lovell introducing their version of a bionic eye. It’s pretty astonishing and I can’t quite believe I get to learn things from him.

It’s the very clever Dr Paul Middleton who was first author on a review of noninvasive monitoring in the emergency department that is well worth a read (alas, another paywall):

Middleton PM, Davies SR. Noninvasive hemodynamic monitoring in the emergency department. Curr Opin Crit Care. 2011;17:342-50.

Here’s the PubMed link from a team taking a preliminary looking tissue oxygen monitoring after out-of-hospital cardiac arrest:

Frisch A, Suffoletto BP, Frank R, Martin-Gill C, Menegazzi JJ. Potential utility of near-infrared spectroscopy in out-of-hospital cardiac arrest: an illustrative case series. Prehosp Emerg Care. 2012;16:564-70.

 

All the images here were via flickr’s ‘The Commons’ area and used without any modification under CC 2.0

4 thoughts on “Sandpits, Better Eyes and New Monitors – Can NIRS work for prehospital medicine?”

  1. While the broader cultural niche of adult humans dressed as cats leaves me cold, it would have made things easier for all those researchers working on optics in brain tissue. Had human brains been more like cat brains in size and the like we could have gone with the transillumination approach and life would have been much easier. As it was they had to waste all those years trying to figure out reflectance and all that junk.

    But otherwise? Cats? Not really.

    Like

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