Full Transcript Pre-show banter and guest introduction 0:00 All right. 0:01 Time for a new episode. 0:03 So, folks, you know what that means. 0:05 It's time for some pre-calibrations. 0:09 Robert, what's going on? 0:10 Well, before what was going. 0:12 Where is Emerson? 0:15 I don't know. 0:15 I have a feeling our questions may not be quite as intelligent today. 0:20 I think you'll make up for that. 0:22 Definitely not me. 0:24 Well, Emerson, he always brings the heat when it comes to asking the probing and sophisticated questions. 0:32 So we'll do our best to keep it in the road today. 0:36 Agreed. 0:37 But yeah, so what's going on? 0:39 All right, so today we dive into the mind shaping innovation and technology joining us today is Hamid Hanafi who is the CEO of Novoresp. 0:47 He has a PhD in biomedical engineering with an MSc in electrical and electronics engineering from Dalhousie University. 0:54 Hamed is a recognized leader in the respiratory technology space. 0:58 As an adjunct professor at Dalhousie, he bridges academia and industry, driving cutting edge solutions in a fight against sleep apnea and other breathing disorders. 1:07 Under his leadership, Novoresp is pioneering breakthroughs that are transforming the future of respiratory health. 1:13 Stay tuned as we explore Homed's inspiring journey, the challenges of innovation, and his vision for a healthier tomorrow. 1:21 All right, it sounds like we have another one of these underachievers on the show, huh? 1:27 My gosh, a PhD, huh? 1:29 Yes. 1:29 Incredible, incredible guest that we get to meet here on the podcast. 1:34 And I think it's going to be interesting. 1:36 We've talked a little bit about the fact that, we all know that. 1:38 we need better technology from a PAP therapy perspective for our patient populations. 1:45 He is on, the cutting edge of delivering an AI auto CPAP algorithm. 1:51 So I can't wait to talk to him. 1:54 Yeah, that is exciting. 1:55 I mean, I remember back when we were at Respironics, how it was like a wonder to figure out what is the next algorithm going to be and how much more can
Novoresp and the AI CPAP concept 2:05 we do? 2:05 We can't really do much. 2:06 And I, remember one of the engineers like, Hey, we're just getting started. 2:10 and I I. 2:11 Remember as a clinician thinking what more we can do and to here now to leverage AI. 2:18 to create a new algorithm and make something that's more comfortable for patients. 2:23 It's just absolutely exciting. 2:25 And we were lucky enough to have some of the others developing these things, you know, with Dr. 2:31 Noah and the other gentleman from the University of Cleveland, right? 2:36 I can't remember the... 2:37 Yes. 2:37 That's it. 2:38 Yeah. 2:39 The rocket scientist, Vortex Bab with the rocket scientists. 2:42 So it's going to be interesting to hear AI now for this. 2:47 Absolutely. 2:47 Let's jump into the show. 2:49 All right. 2:50 On the Show. 2:51 Now a word from our sponsor, MedBridge Healthcare. 2:55 Medbridge Healthcare is a leading provider of sleep lab management services and home sleep apnea testing. 3:02 medbridge partners with hospitals, healthcare systems, and medical academic institutions to offer comprehensive, fully integrated services for sleep disorders. 3:31 Lights out. 3:32 Welcome everyone once again to another episode of Sleep Tech Talk, the sleep podcast with your host and friends, Emerson Kerr, 3:40 Robert Miller and me, Dr. 3:41 Gerald George-Moneycrow. 3:43 Folks, you will notice a missing person today and we won't give it away right now. 3:50 But in the meanwhile, we just want to say thank you so much for all the support that you've given us. 3:55 All these episodes, all these years, We can't thank Q&A for that. 3:58 We've grown so, much only because of you. 4:00 Please continue to like, please continue, to subscribe, and most importantly, Please, continue Share the episode, share the podcast with all those, 4:09 all your sleep technologist friends, All those in the sleep industry, as well as those that are sleep curious. 4:14 A huge shout out to our sponsors without whom we can't do this. 4:18 So thank you all so much. 4:20 And folks, be sure to check out our Sponsors as Well. 4:24 With that being said, we've got an amazing, amazing guest today for this amazing show. 4:30 Robert, who's on the show today? 4:32 Hey, Jerry. 4:33 Thanks, everyone, for joining the show today. 4:35 We have with us Hamed Hanafi, who is the CEO of a new company called Novoresp. 4:41 You may have started to see more information about Novoresp, but it is an AI auto CPAP algorithm. 4:49 And we're excited to learn about Hameed's journey into sleep medicine, But then also the development of this new CPAPP algorithm, 4:57 we certainly need more tools to help our patients from an adherence. 5:01 and a PAP therapy standpoint. 5:03 So we're excited today to hear more. 5:06 Dr. 5:07 Hnafi, can you tell us a little bit about yourself and how you got into sleep medicine? 5:11 And then we'll jump into the AI algorithm. 5:17 Firstly, thanks so much for having me. 5:19 Hello, everyone. 5:21 So my background is in electrical engineering, masters in microelectronics. 5:27 And then during the PhD, I got into the biomedical engineering with a focus on respiratory mechanics. 5:35 I also took business courses during that PhD. 5:41 And that's where I got interested generally in medical devices with focus on respiratory. 5:47 And then later on, I get familiar with, you know, new ideas and how to help patients in the respiratory field with focused on anesthesia machines at first. 6:02 Another motivational factor here is that my father has severe sleep apnea with an AHI of 70 that was diagnosed and didn't want to use his CPAP machine. 6:14 So I slowly got interested and wanted to make the therapy better. 6:21 Started working with key opinion leaders and came up with and idea to track changes faster. 6:27 And then later on in 2018, came up with the idea of what if we could predict and prevent apneas to help patients?
Predictive therapy, pressure reduction, and phenotyping 6:41 And we can get into that during the podcast. 6:46 Well, that's fantastic. 6:47 I think that in order for you to have recognized that there was the need for this advancement from an auto algorithm standpoint, 6:56 you must have done some research on the current offerings that are on market and found maybe a space where there were some opportunities for improvement, 7:07 I'm assuming. 7:09 Yes. 7:10 So acclimation to therapy is pretty difficult at first. 7:14 There's a lot of advancements that have been made. 7:18 I should first honestly step back and say that I really appreciate the work that has gone into CPAP, APAP and other therapy here at Novorusp is an evolution 7:31 to that and to make things better and move forward. 7:33 and just making things predictive instead of reactive. 7:38 The research we had done was that the Acclimation to therapy seemed difficult and a lot of the issues came from at first accepting that you're going to 7:51 have to wear a mask and deal with a tube and the machine on your bedside table, but through interviewing respiratory therapists that were setting up patients 7:59 at DME's, I realized there is a big missing information here where You know, it's the pressure and air flow that's being delivered through the mask. 8:08 And a lot of patients might at first think that it is the mass that is a problem. 8:14 But you realize that sometimes patients are sleeping and at the first the pressures low and they accept it because the doctor told them, 8:21 you're going to get heart attacks and strokes if you don't use this machine. 8:23 So they fall asleep with it, but somehow they don t sometimes even remember ripping the masks off. 8:31 They don't have it on. 8:32 And as you know, having it for more than four hours a night is very important. 8:39 At that point, we started thinking of how could we use what we have found in terms of predicting and preventing apneas. 8:48 to lower the pressure of therapy and make it more comfortable in this application. 8:52 I have to say the application of this algorithm is bigger than CPAP therapy in sleep apnea. 8:59 You can make any sort of ventilation sort of predictive and preventative instead of being reactive. 9:07 But we decided to focus on CPAP for personal motivation and it's the trials are at home and easier to conduct trials at the hospital and we had access 9:17 to data. 9:19 So, Doc, you had mentioned just now predictive as opposed to just simply interventional. 9:27 So when you talk about predictive, do you see, I know you said other applications, but let's focus on CPAP right now. 9:34 What other application apart from just the patient doesn't rip it off the face? 9:40 What are the applications could we look forward to with this? 9:45 In terms of general med tech, No, no, in CPAP itself, when you're talking about predictive, are we going to be able to use this to better leverage on how 9:55 we understand patients requiring CPap, for example? 9:59 Absolutely. 10:00 So there's multiple aspects to this. 10:04 We know that long-term adherence is super important, and we know the patients that are even adherent to therapy in the beginning, 10:12 they meet the criteria, the cost of the machine is covered, long-term adherence still drops. 10:20 And it's partly due to not going through sleep staging and actually feeling the benefits of better sleep long term. 10:30 That's because the algorithms were not necessarily designed for that. 10:34 They were aimed at lowering AHI. 10:37 So this is where our algorithm really comes in handy where based on our trial results that I'll discuss, it seems like patients are having better sleep 10:49 staging and comfort. 10:50 Now it's partly due to lowering the pressure and not having to tighten the mask as much and such, but we've seen better results in existing patients, 10:59 patients that have been using the machines for two to four years. 11:03 They rated our algorithm better. 11:07 And we were using a sleep tracker ring called Sleep Image Ring that through cardiopulmonary coupling, as you're familiar, 11:14 was scoring sleep staging. 11:18 And, we saw significant improvements there as well. 11:22 So I'm assuming that the patient experience is better because of a lower mean pressure that's able to be delivered through the AI algorithm. 11:33 I also know that looking at some of the other algorithms that are available on the market today, some are flow-based. 11:40 There's also volume that makes up some the auto-algorithms, but then, you know, I've even heard recently where there's a work of breathing, 11:50 type of algorithm that's being developed, is maybe Noverest takes all of those things into account. 11:57 Can you tell us as much as you can about that specific part of the algorithm? 12:02 So in lump sum, yes, but our approach is through using AI. 12:07 And let me elaborate here. 12:09 We've designed it in a way that while our IP covers the use of other inputs, CMAP was designed for CPAPs in way, that would only use the sensors that is 12:22 available on any CPOP machine in the market. 12:24 So it could be applicable to them. 12:27 And what it does is that it predicts apneas before they would have, it's trained by breathing patterns of, um, you know, 12:34 millions of samples of breathing pattern. 12:37 Uh, so it generates probabilities of the future. 12:41 It's like a weather forecast. 12:42 If you, the rain is coming, You sort of set up your yard for the ring that's coming. 12:47 So it constantly generates probability, uh, in terms of what's going to happen in the. 12:52 Um, now any sort breathing and abnormality or predicting that there's gonna be normal breathing. 12:58 By doing so, when these probabilities cross a certain threshold, let's say we know an obstructive event is coming, you can intervene gently before that
Sleep staging trial results and comfort improvements 13:09 apnea to prevent apneas. 13:10 Now we've done trials to prove that this prevention is possible because first we realized that you could predict apnias, 13:17 we were thinking, how come Nobody else did this. 13:21 There must be a, there must have been something wrong with this, so we had to design a trial to see, are we actually preventing apneas? 13:27 We did that at the hospital with full-on polysomnography and proved that in another trial back in 2020. Now, then in 2023, we focused on actually lowering 13:42 the pressure, not just preventing the apneas that are coming on top of APAP. 13:47 And what it does is that, it's one thing to prevent apnea, but still it is inevitable that some apnoas would happen, right? 13:56 So you need to have the reactive APP enabled to keep therapy safe. 14:01 The difference is, when pressure goes up after an apnea, now because you have confidence in these probabilities, instead of decaying the pressure for the 14:11 next 40 minutes, an hour, hoping that no more apnea would happen, you can drop the pleasure back down and look at the probabilities. 14:18 So that's where this happens. 14:22 And then also it's adapting on the patient where it tries to find the right pressure, which is what APAP tried to do, but in a reactive way, 14:30 this one is trying to it in proactive way. 14:36 So it finds the right pressure, the lowest pressure possible for that patient. 14:42 Just to add the possibilities that would come out of this, we've trained this algorithm with the general population data, 14:50 so it would be applicable to the Think about a future when actual application of AI is possible through the regulatory pathways. 15:05 Now, all of a sudden, if you can categorize phenotype patients into groups, which we have IP on as well and we've tested it, 15:16 There's a couple of abstracts that I'll be presenting at the American Thoracic Society that's upcoming on this. 15:22 You can categorize patients into groups like patients with diabetes, patients, with cardiovascular health, Patients that have a high hypoxic burden. 15:30 you could categorise those patients and then develop their own algorithm with the breathing patterns specific to that population. 15:37 So that would be the phenotyping part. 15:39 And then think of the individual. 15:40 Every individual is breathing different from the other. 15:44 We can't have a one-size-fits-all type of solution. 15:47 On the individual, if you train our algorithm with, you take the general one, and then you feed more and more of the individuals' breathing pattern on it, 15:56 now all of a sudden, we can push an update to the CPAP machine. 16:00 where it treats the individual's type of breathing. 16:04 And then over time also, we're not always breathing the same. 16:08 Over time, I could be developing asthma, COPD. 16:10 All of a sudden, my breathing patterns are changing. 16:13 There could updates applied. 16:16 So the possibilities are endless. 16:18 But for now, it's a fixed model that can predict and prevent and find the lowest pressure possible for the patient during therapy. 16:30 Are you a sleep tech looking for new opportunities? 16:34 Well, MedBridge Healthcare is one of the largest employers of sleep technologists and they are growing. 16:39 If you are a Sleep Technologist interested in a new position, potential paid relocation, or looking a career advancement, 16:47 consider a Career with Medbridge Healthcare. 16:50 Now back to the show. 16:51 Doc, that's fascinating, absolutely fascinating what you're talking about. 16:55 I want to just kind of move a little bit now to sleep staging. 16:58 Could you talk a bit more about that? 17:00 That sounded also fascinating. 17:03 So just a disclaimer here, the obvious thing is that sleep staging with EEG is the most accurate sleep-staging, but we all know that doing trials at the 17:17 hospital with uncomfortable beds and EEGs, patients have a hard time sleeping. 17:21 So we designed the trial that we started and completed in 2024 to compare CMAP with APAP on the same machine with a switch on it. 17:33 Also to explain how we do this we modify a machine we go to Health Canada and ask for permission to run this on a limited number of patients, 17:40 just to compare performance of the common algorithm versus ours. 17:46 And then we recruited patients that already were used to their CPAP. 17:50 And to be honest, when we started the trial, we weren't expecting improvements in sleep staging. 17:54 We just started this trial on existing patients because they don't have leaks. 18:00 They don' have problems with their masks. 18:01 Now you can actually head to head compare algorithms. 18:05 So we did a trial where for nine days they would use APAP or our software. 18:11 It's a crossover randomized trial. 18:13 We threw out the first two days because it's acclimation. 18:16 Patients think we're trying two new algorithms on them. 18:19 They don't know one of them is the exact algorithm on their home machine. 18:23 In between transferring over to the other algorithm, they filled out the questionnaire firstly to rank how they did with the first algorithm. 18:34 And then the five nights of washout period where they go back on their own machine and then they turn the switch that's randomized and use the new algorithm 18:43 and we were tracking them the whole time. 18:45 They fill out a questionnaire at the end of that too. 18:47 This whole they're wearing the sleep imagery. 18:52 So we just did this to prove that we can reduce the pressure without compromising efficacy or AHI or SAHI. 18:59 SAHi is an AHi that's deep imaging outputs that is equivalent. 19:05 They've shown that it has over 90% correlation with the pulse ethnography AHIs. 19:11 So, we showed that could reduce pressure by 20%. There is between patients some more, some less, without comprising AH. 19:19 But then the staggering results were from sleep staging. 19:24 They spit out a number called sleep quality index. 19:29 That is a combination of REM sleep, fragmentation, stable sleep and unstable sleep. 19:34 So we significantly improved stable, we reduced unstable, improved REM, reduced fragmented.
Mask leak, manufacturer integration, and adherence trials 19:42 And we reduced something they call, I believe, wakefulness. 19:50 But it was correlating. 19:51 It's just arousals. 19:52 So we reduce the arusals, but then this correlated with the comfort and sleepiness question, upward sleep in this questionnaire that we sent to patients. 20:02 The results, while CPAP improves, sleepiness to the acceptable level and comfort to acceptable if you're compliant, there was a significant difference 20:16 between the level of comfort they experienced with ours and the sleep that was reduced on them. 20:23 This was on 50 patients. 20:25 Obviously, when this is applied to a specific hardware, it's going to be tested on a larger number of patients and And newer generations of CMAP are also 20:36 coming out, but it was pretty promising. 20:39 And that's when we gained a lot of attention as we presented this at European Respiratory Society Conference back in Vienna. 20:47 Fantastic. 20:48 I've got two questions for you, not necessarily related, but so one of the things that certainly auto algorithms have to manage for a patient is mask leak. 21:00 So maybe you could share a little bit of how the AI algorithm manages mask leaks. 21:07 But then secondly, from a from patient standpoint, is the Is the goal to have Novaris become an AI algorithm in a manufacturer platform or is it something 21:17 that a patient would be able to apply to their own existing CPAP device potentially in the future and change their therapy? 21:26 Great question. 21:28 Leak was significantly reduced in those patients. 21:32 One of the surprising comments we received from patients voluntarily was from their partners. 21:41 So our study coordinator would go to collect the device and then the partner calls her in and says, Hey, like I've been sleeping next to my wife or husband 21:51 for a while. 21:52 And, uh, this was the easiest I could sleep because I don't get the airdraft to. 21:58 As much a draft and the noise was less. 22:02 And looking into actual numbers from leak, we reduced the 95th percentile leak significant. 22:10 And a 95% leak as you know is the leak that blows out of the mask and wakes up yourself and your partner. 22:17 So that was another sign of, okay, what we're probably gonna do well with adherence. 22:23 If there's time, I'll explain our adherents trial as well on brand new patients. 22:27 Well, Jerry is always our timekeeper. 22:29 And I thought that we would probably be getting to the end of, but the second part of the question was where you see the auto algorithm being administered 22:37 to a patient, whether it's through your device or a patients self application of their CPAP unit. 22:46 Absolutely. 22:47 So firstly, our team is very purpose driven. 22:53 The final answer is it's going to be on manufacturers' devices. 22:58 But the team is purpose-driven. 23:00 We want this to reach millions of patients, the current and future versions of the algorithm. 23:06 So the fastest way to get it to patients is through you know, manufacturers that already have access to patients. 23:13 It's the most logical path for us. 23:17 In 2023, when I was telling you that we were fully developing the algorithm, that was tests on our own parents, our on and on calls until it got to a place 23:25 then we could go to legit trial. 23:28 We do have a lot of interesting talks with the manufacturers personally, and the team is focused on running and finishing our adherence trial and keep 23:38 tracking our long-term adherents results. 23:41 But there's a lotta talks, so I think we'll be on A, or potentially multiple manufacturers' machines in the future. 23:50 So, Doc, we are getting close to time, but I did have a question for you, because Emerson's not here today, and you said one word that would have perked 23:58 his ears, that was phenotyping. 24:00 Can you speak a little bit more about that, just because? 24:05 I can talk more about it now because we have filed the patent for it, but as we were collecting data on patients, the importance of phenotyping, 24:16 I don't know if your audience knows the important of it. 24:21 sleep and neurology doctors are emphasizing on the importance of not applying CPAP therapy as a one-size-fits-all. 24:31 We notice differences in the breathing patterns of patients and we know the important of linking that into health metrics and biometrics that come from patients. 24:40 As I just said, we have a big database now after the comfort study we continued collecting data. 24:48 And we have a big database of patients with their biometrics and comorbidities. 24:52 And through that, we managed to create a map of comorbitities and health metrics that would match with specific categorized clusters of patient that have 25:05 specific breathing pattern or apnea signatures. 25:09 Through that we know that either during the diagnosis or the first bit of therapy we can categorize patients into different groups. 25:18 the regulatory pathway is available, we could apply their own versions of algorithm to them for better outcomes. 25:28 Could I quickly explain how we're seeing long-term adherence? 25:33 Please do, please do. 25:35 Yeah, so our most important trial started back in the summer, where we have patients that are in month eight of trial. 25:43 These are our brand new patients. 25:45 We're following a US DME protocol, were we merged the versions of several large DMEs in US, and a single study coordinator and single RT to assist all patients, 25:57 this is on 200 patients 100 go on APAP and 100 on CMAP. 26:01 both on a brand new looking device. 26:05 They just got diagnosed, these patients. 26:07 We're halfway done with this trial with some patients in month eight. 26:11 Because we're half way done, I can't give percentages, but short term and long term adherence are looking extremely promising. 26:17 Long term, adherents, to our surprise, is actually distancing itself from APAP further. 26:24 we thought it might merge, even larger. 26:28 The other results, like the labor costs at DME, the amount that our team needs to call the patients and adjust things or patients approach them for adjusting 26:41 is much less due to less leak and lower pressures. 26:46 And the questionnaires are looking pretty promising as well. 26:50 But the most important metric I will give you today is patients are sleeping much, much longer on the CMAP arm compared to the ATV arm. 27:00 Those are amazing results. 27:02 That's exactly what we need. 27:04 I think in the world of this novel pharmaceutical treatment for sleep apnea that seems to be on the horizon with more and more candidates in that space, 27:15 we, need better PAP therapy so that, because we ultimately know that it's still the gold standard of treatment, but we to make it so more tolerant for 27:24 patients to be able to utilize and comfortable and not something that patients dread the thought of. 27:30 So thank you for being a part of developing the future. 27:36 I agree. 27:37 We thank you so much for that. 27:38 I mean, people are talking right now, we have all this technology, why is CPAP just exactly the same as it was 30, 40, 50 years ago, 27:47 just the reverse vacuum cleaner.
Future of PAP therapy and closing remarks 27:50 So why aren't we doing that? 27:52 And for you to harness, like you said, millions and millions of breathing patterns and use leveraging AI for, that we thank so you much, 28:00 for the direction that you're taking, taking the therapy. 28:03 Thank you. 28:04 What we're doing is just something that makes sense. 28:07 A lot of the artists tell us, oh, we see the pattern with our own eyes. 28:11 We know it's coming. 28:12 So machines can do it too. 28:14 It's kind of evolved. 28:16 Well, thank you so much, Doc. 28:18 And we are out of time. 28:19 If people want to find more information, where can they go? 28:23 Novoest.ai is our website. 28:26 Is where the information could be found about us. 28:30 Excellent. 28:31 So you heard it here, folks, NovoResp.ai, and we're going to have more information in the show notes for you. 28:37 But until next time, we say thank you so much for joining us. 28:40 Thank you for all the likes, all subscriptions, And most importantly, All the shares. 28:44 Huge shout out to our sponsors, without whom we can't do this. 28:48 Be sure to check out the sponsors as well. 28:51 And until Next time we Say Before we go, We would like to thank our sponsor, MedBridge Healthcare. 28:58 MedBridge Healthcare is developing innovative inpatient, post-discharge, and population health programs to screen comorbid conditions, 29:07 diagnose and treat sleep disorders. 29:10 Learn more about their innovative solutions and career opportunities at MedbridgeHealthcare.com. 29:18 Once again, you can learn more about their innovative solutions and career opportunities at medbridgehealthcare.com. 29:25 All right then. 29:26 That was quite a show. 29:28 So that means it's time for some post calibrations. 29:31 Robert, what do you think? 29:32 You know, we're so close to having more tools, more sophistication as it relates to the delivery of CPAP therapy. 29:43 And I just can't wait till these things are commercially available. 29:47 It sounds like they're nearing the end of some of their clinical trials at Novoresp. 29:52 I did ask the question, is it something that a patient's going to add to their machine or would it be potentially in a manufacturer's PAP device? 30:01 It sounds like the manufacturer is certainly the route that they're going take. 30:07 I'm interested to see what that's gonna look like. 30:11 We know who the manufacturers are that are out there. 30:13 and there are many others that are trying to get through the FDA approval. 30:16 So, you know, it feels like we're in a race to a better patient experience. 30:23 I agree. 30:24 It's really exciting. 30:25 And yeah, we are this close. 30:29 Once these kind of things happen, it's just going to get more and more. 30:32 I think it is going be even better for the patients. 30:36 You had mentioned this during the show about the novel therapies that are coming out. 30:41 Then you have something like this on the other side of it. 30:45 I wouldn't say combat that, but to have another opportunity for patients to try CPAP once again, to make it more comfortable without the risk of the side 30:54 effects that you have with an orally-ingested therapy. 30:57 So I think it's just exciting overall for us to see the direction that things are going. 31:03 There was a time when CPap was about having a tower next to your bed, and it got to be so small that could carry it around anywhere. 31:12 But the basic technology in there remained the same. 31:15 Now to hear something else that changes so that it predicts what you need and delivers the therapy so it's not remaining super high during the night and 31:25 makes the patient... 31:27 And the other exciting part is not just the patients, but also the bed partner say that, it wasn't blowing in my face. 31:35 That's really something to be grateful for. 31:39 I absolutely agree. 31:41 He mentioned the fact that the response to sleep disorder breathing events with an algorithm has historically left the pressure at elevated levels potentially 31:54 over a longer period of time when maybe that's not exactly what's necessary to maintain a patient's airway. 32:01 The delivery of lower pressure is certainly going to drive patient adherence and adoption. 32:09 If there was better technology, my guess is that less patients would seek out some of these other potential pharmaceutical treatments and that sort of 32:18 thing for sleep apnea. 32:20 Stay tuned, America. 32:23 Lots of technology on the CPAP therapy forefront that's on their horizon. 32:30 So we're excited to be a part of it. 32:32 And you heard it here first, most of the time, that we sort of brought K-PAP before. 32:38 Many people had heard about it in the industry. 32:40 We had the Vortex PAP researcher on them on program, and now we have Novoresp and their CMAP algorithm. 32:48 so we are excited be part the future of sleep medicine. 32:52 Agreed. 32:52 And it's not just Stay Tuned America. 32:54 Don't forget, he presented this at the European Respiratory Society. 32:58 So it is going to be going out to the rest of the world as well. 33:01 Very, very exciting times. 33:03 Absolutely. 33:04 And I mean, shout out to Emerson, man, I wish he was here. 33:08 I think he would have really enjoyed this. 33:13 Dr. 33:13 Hanafi, he spoke about phenotyping, and I know that's Emersons thing. 33:17 It was a fun part, it caught my attention that I immediately zoned in that, oh, Emberson should have been here, Hey, Emerson, 33:26 this one's for you, all right? 33:27 That's right. 33:29 All right, folks out there, thank you so much once again. 33:34 And I hope you enjoyed this show and the latest news in CPAP. 33:42 So until next time, we say cheers. 33:45 Take care. 33:45 Sleep Tech Talk is sponsored by React Health. 33:49 Within our LUNA PAP device line, each offering is FDA approved, encompassing CPAP, APAP by-level and by level ST models. 33:57 Our thoughtfully selected array of PAPP interfaces and accessories complement our Luna Papp device-line while accommodating diverse patient requirements.
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