Video Transcription
Ameera Patel 00:02
So my name is Ameera Patel. I'm the Chief Executive of TidalSense. I'm a clinician by background and mathematician, which is a bit of a weird combo, but there you go. TidalSense has a really big ambition, which is to effect population-scale change in moving towards preventative health care and respiratory. So if I just start by highlighting the problem, which a lot of you may already know or be aware of, but COPD and asthma now account for about 10% of Earth's population. Prevalence is rising in the US; the annual spend is around 100 billion. In the UK, it's around 5 billion, the third leading cause of death in the world, and the second leading cause of hospital admissions. And if you look at where the spend is, the majority of the spend is in late-stage disease management, and that, if you follow that back through, is caused by a lack of accurate diagnostic testing. So the current gold standard diagnostic test in international guidelines is spirometry. It was invented in the 1800s, became mainstream clinical practice in the 1900s, has a precision or positive predictive value of 63%, which you wouldn't be able to get cleared in any jurisdiction today, and the majority of patients are undiagnosed—two-thirds in the UK, in the US it's around a third. In other global markets, it's as high as 80-90%, and the market is just growing because of the rise of air pollution-related respiratory disease. So I'll skip over that. But for those who aren't familiar with spirometry, the diagnostic test is a clinic-based test. It's not the stuff you buy on Amazon, which is portable for home monitoring. To do a diagnosis, you have to do lab, clinic-based testing. So what have we built? We've been going for 10 years. We've raised seven and a half million in venture financing from growth finances in the UK from institutional investors and 8 million in government funding. We have a novel sensor technology, which you can see there, embedded in a handset that has now got 22 patents granted globally. It has a SIM card embedded in it. It takes a minute of relaxed breathing to capture the data, which is then transmitted to our cloud platform, and we are just about to launch what will be the first EU MDR class two cleared new diagnostic test with an intended use for diagnosis that has come out in, I suppose, living memory, to be honest. As a clinician, a lot of people are still using spirometry, but there is more and more pushback just because the diagnostic pathways everywhere are failing. And the cause of that, interestingly, has been the pandemic, so they were really dysfunctional beforehand. Diagnostic testing for respiratory has to happen in primary care. It has to be community-based. It all stopped in the pandemic in the UK, and now there's a decompensated failing system. If you look at what our sensor picks up on the right-hand side, you can't see my laser pointer. COPD has a very unique waveform shape, and it's caused by the structural damage that occurs in COPD. So over the last seven years, we've run six clinical studies and collected over two and a half million patient breaths in every cardio-respiratory condition that you can pretty well think of, and we built a set of machine learning models that can do accurate diagnosis of COPD. So if I just skip ahead quickly to the value proposition, health systems globally have no money. That's just a fact that we have to deal with. Everyone is fiscally challenged. So where does this actually fit in? We're not going to a health system and saying, "Hey, we got a nice, shiny toy. Please buy it and fork out money in this financial year." Actually, if you look at the value proposition, we've got a five-minute test, which we're pushing down to under one minute. 30 minutes for spirometry is generous; in Primary Care Physician Clinics, it's normally around 40 minutes. The other big pain point is training time. So we've validated a training regime that's under 10 minutes, whereas spirometry requires thousands of pounds of capital investment into training staff. And then we've used machine learning to automate the diagnosis, so we actually can allow a healthcare assistant to operate this, which has halved the service provision cost. And that's really it, in a nutshell. What we've done is we've taken a new technology, packaged it into something that generates immediate cost savings on budget impact. And unsurprisingly, everyone's interested because it's saving money immediately. Just to give you some information on our kind of pivotal study that we published this year, internal diagnose, which was our diagnostic software, which we'll be launching in Q1, showed higher performance than spirometry. Our first post-market studies have also read out even higher performance than that, and quite excitingly, from a scientific point of view, we actually shown that you can track the progression of the disease. So GOLD 4 is the most severe, GOLD 1 is the least severe, and you see this progressive rounding and flattening to the point where, if you become a triangle, that's not compatible with life anymore. So you progress from a square to a triangle, and that's really what happens in COPD. And then from the usability side, in our validation studies on usability, 100% of clinicians administered the test in under five minutes. 100% were trained in under 10 minutes. And actually, we get really, really good usability data from clinicians, and that's because it's very simple. It's got two buttons: on and play. It's relaxed breathing for patients, so there's no forced breathing maneuvers. All of the collectivity management is taken care of, and actually, we really worked hard on making it simple, reducing cognitive burden for clinicians. I have a quite bold statement. It tries reflecting what we're really trying to do in the market. And as I said, COPD is where we're launching. That will be cleared. The hardware is already CE marked, the diagnostic machine learning software has been audited now by the notified body that will be launching in Q1, but what's very exciting is these are coming along. So we have a number of different models for different diagnostic indications that use the exact same hardware. And that's exciting because if you look at traditional diagnostic reimbursement pathways, they are pay to play, really; they're pay as you go. And so if you're clever about your hardware, and you have a consumable-based model, and you make your consumables cheap, you can get quite a high gross margin business running. But each new machine learning model that you deploy for a new diagnostic indication is near enough 99% gross margin because it uses the same hardware platform. So this is where it really starts to get interesting because we're actually aiming, over time, to empower clinicians with really detailed diagnostic insights. I know I'm running out of time. So commercially, where are we? We are revenue-generating. We have deals with top five pharma companies already, who are validating the use of our technology in trials, both phase two, phase three, and post-market as well, to try and understand how best to fit patients to their new compounds. This is the bit which I'm really excited about, which is the commercial opportunity because we suddenly got a test that's so quick you can screen patients. You can build virtual diagnostic pathways by sticking it onto the back of lung cancer screening, for example, where you have a convergence of risk factors. So in the UK, the lung cancer screening program that went—that's now in national phase deployment—25% of people that were screened had evidence of COPD on their CT scan. So there are some really interesting things that are interesting pharma companies on growing the therapeutics market. And excitingly, this is now going to go into the NHS first-line diagnostic testing from Q1, so we have a new pathway developed in the UK. The UK is actually quite innovative, contrary to all of the stuff you read in the newspapers that is going ahead. And we have relationships covering over 50% of the UK market. And what we're really trying to do is reform the diagnostic pathways for both managing patients on a waiting list and traditional symptomatic testing. So really quickly, we are raising Series A; we are going to close that at the end of this year. There's already a healthcare diagnostic specialist lead in place, with a number of people looking to co-lead. There's probably around one to four million pounds left in the round. And for us, it's about scaling sales in the UK, EU, and then FDA clearance, which we're in the process of kicking off. And I'll leave you with a final team slide. I have to say, I've worked at a number of organizations, but this is one of the most talented scientific teams I've ever worked with. So it's an absolute pleasure to be Chief Exec of this business. That's it. Thank you.
