Okay, while we're waiting for other people to log on, let me go through a couple of housekeeping items and go ahead and get started. Ben, could you go to the next slide? This is what the screen looks like now. If you look to the bottom of your screen, you'll see two buttons that we'll use today. One is the chat button, which you see should have a bubble with a number two or number one, a chat button where Medaxium will share the link to the presentation. You can find the presentation, the slides there, and download them from there. Next to that button, you see a Q&A button, or right close to that button, you see a Q&A button. That is where you can pose questions to the speaker, and we will hold those questions, generate them, and then our speaker will answer them live at the end of the webinar. Please go ahead and type your questions there. Looking forward to your comments and questions. All right, so let's go ahead and get started. Today's presentation is Unveiling HeartX Innovation with NanoWare, one of the handful of companies selected in the 2023 cohort for HeartX. NanoWare is an AI-based remote diagnostics tool using cloth nanotechnology, capturing medical-grade biomarkers directly from the skin, giving a more robust picture of a patient's cardiopulmonary health. We're pleased to welcome NanoWare to HeartX, to the Medaxium community, and we're pleased to welcome Venk Varadhan, co-founder and CEO of NanoWare, to share with us how they're advancing patient care with state-of-the-art technology. Venk, welcome. Thank you, Claudia, and thank you to Medaxium, American College of Cardiology, and for all of you attending today. Hope we can get into a fun and robust discussion in the Q&A, and feel free, as Claudia mentioned, to fire questions during the chat. We're NanoWare, based here in New York. As Claudia mentioned in the intro, we are a connected care platform or a wearable platform that you are familiar with, whether it's a consumer device or an arrhythmia patch or an ambulatory blood pressure monitor. We follow the same tech stack. We capture data off the body, push it up to the cloud, run insights using analytical tools, and then give the cardiologist or clinical investigator insight. Where we differ is from the ground floor of our technology, and that separates us compared to every wearable or connected care platform out there, in that we utilize our proprietary invention of what we call cloth-based nanotechnology. This is the first and only cloth or textile-based sensor in the world that has been 510k cleared by FDA for capture and transmission of essential performance biomarkers. And long story short, this enables us to get a very elongated breadth and a very deep depth of biomarkers across the heart, lungs, central vascular, and hemodynamic system. And because it's closed source in our own server, it enables us to do some very cool things with AI and machine learning tools to drive very unique diagnostics. So we call ourselves Healthcare at Home Remote Diagnostics for that reason, utilizing cloth nanotechnology software as a service and ultimately AI and ML diagnostic tools. The quick sensor technology that we cover, this is an electron microscope or SEM picture of our sensors in the center of the screen. You'll see that it looks like all billions of vertically standing sensors or nanosensors per centimeter of surface area. That overwhelmingly expansive footprint of sensors across a small surface area of one centimeter really enables us to cut through the typical sources of noise that your electrodes or sensors or even invasive monitoring tools will run into. So hair or sweat or motion were significantly reduced in some cases eliminated. You do not need to shave your chest or sand the dead skin down. Moisture or sweat, which can cause rashes or cause electrodes to fall off with other adhesive or gel-based electrodes, cloth and sweat or natural adherence. So our diagnostic yield actually goes up with no limiting human factors with rashes or skin conditions. It is machine washable and multi-parametric in the sense that it's capturing electrical signals, potentially skin conditions, heart sound, acoustics, hemodynamics, eventually electrochemical signals, and the ability with different vector orientation for imaging analysis based on those sensors. And so while there's a lot of places we could have started on what we're using this core technology to detect, we are focused on that sort of irreversible journey from late stage or irreversible or labile uncontrolled hypertension that will lead to structural heart disorders and or heart failure. Many of these patients you are treating over multiple years and what you know as cardiologists, what's missing across that multi-year journey is the ability to frequently and contiguously do cardiopulmonary assessments of their state to therefore mitigate that continued regression with medication adjustment, lifestyle adjustment. You may not be seeing these patients more than once every six months or once every 12 months just to understand where the murmurs of their heart are with the stethoscope or the rhythm with their electrocardiographs or their blood pressure either through a cough or an A-line, hemodynamics either through a bedside monitor or an invasive right heart catheterization with the swan gans, their respiration, their breaths per minute, their pulse ox, their activity, movement, and posture. Our thesis is what if we could take this cloth nanotechnology and start to combine all seven of these first-line diagnostic tools into a single firehose of data that not only offers a safer and non-invasive way to do these full sort of first-line diagnostic tools and cardiopulmonary assessments but also giving a continuous reading that's home-based. It's in the real world. It's not in your office. It's probably more of a natural assessment over a longer period of time while they're in their real-world environment of their home or their office. What if that platform's same sort of foundational technology can start to individualize different risk signatures? If you have a heart failure patient and you've got an injection fraction of X and you've got BNP levels of Y, that does not suggest that those numbers are absolute in you giving the exact same therapeutic regimen to one patient versus another if they showed the same numbers. Our bodies all compensate differently. Our heart, lungs, vascular, and hemodynamic system all work contiguously in a different way. So is there a way to kind of get that individualized picture by looking at all those systems at the same time, time synchronously and contiguously? And then obviously, the main aspect after you've solved the top two is can this reduce cost for you or potentially replace some tools for you so you can focus on the higher-level priorities of procedures as opposed to the pre-op and post-operative consults, which will take up time in your waiting room as well as your office to do more of the procedures as more become necessary and relevant? Can we save money up front on those pre-operative, perioperative, and post-operative consults and not just focus on the value-based care down the road? Because you're not going to take the burden of fronting the cost up front. And so we believe we've come up with that solution addressing those three main issues. We're the first solution that offers a routine in-person cardiopulmonary assessment, self-administered at home, with our cloth-based wearable, which is a gender-neutral, size-adjustable, shorter sash you see on the right-hand side of the screen. The patient gets this upon prescription, downloads the app to their own smartphone from Google Play or iOS. So there's no base station. There's no Bluetooth sort of hub that you have to pair to. Just like you do with your own phone, left-hand side of the screen is what you will see as a physician. Please reach out if you'd like a live demo. We'd be happy to do that over virtual or in-person if schedules align. This is the first tech stack that combines that nanotechnology all the way through a standard connected care environment. But that last part, our ability to do AI-based softwares and medical device diagnostics based on our unique data lake is what sets us apart. We just commercially launched last year with a $3 billion publicly traded company. The fact that we are on these patients for multiple years, and that's the focus of our target patient group, and the fact that our device is reusable, we give our device away for free, or the hardware for free, and charge a data services model. And the reason we kind of do that is that the market has kind of seeped us. Because we replace all of these first-line diagnostic tools, there is obviously not individual reimbursement that we can do on a point solution, 30-second reading, or the RPM codes, which many of you have probably played around with, that require a certain length of monitoring at a certain time. Again, that is being too prescriptive to you as the cardiologist. You know each patient should not be sort of being monitored and managed the exact same way. A class 2A heart failure patient versus a class 3C is a very different treatment regimen and requires more management and consultation. So our initial business model is actually selling into cardiovascular device companies, diagnostic companies, therapeutics, or pharma, and biotech companies that want to understand the safety and efficacy of their own products, either in phase 2A or B feasibility studies, phase 3 pivotal studies, or phase 4 post-market studies. And we can give them data-driven insights upstream, these AI diagnostics downstream. Clinical trial costs are actually through the roof now. Many of you are researchers. You know that the burden of cost is quite high now due to the pharma companies really ratcheting up the price since COVID. But more importantly, there's value drivers where medtech and biotech and even mid-cap pharma companies are looking to save but also drive value in data. And that will ultimately get us to where we can establish our own reimbursement. For those of you that use a company like iRhythm or Preventus, they were kind of middling around great product after FDA approval until three years later when they got their own codes with enough real-world evidence. That is our goal, that we could get our own codes for you to use in your general population. Right now, we can't, but we are starting a business model in the clinical research space where we are paid for those data services to collect that real-world evidence on the sickest of sick patients. So one day, we're going to have several thousand patients of labile uncontrolled hypertension. One day, we're going to have several thousand patients of heart failure. One day, we're going to have several patients of data on valvular disease or structural heart disorder patients. And we'll be able to take that data to CMS and get our own code. So it's kind of a two-phase commercial model. There are companies that have done this in the past in monetizing their data, Foxella Technologies in the cardiovascular space, which long exited, and then Tempest in the oncology space. And this is really the value we do for clinical studies and ultimately for your practices. We decrease the amount of physical labor you need on a consistent basis. You can start to triage and do digital concierge with your staff because these patients can start self-administering at home. You can start to kind of free up your office space for more procedures as PFA is coming on, more ablations, more catheter-based procedures. The facility costs around just these pre-op and post-operative consults. You're using different devices, a different ABPM, a different sort of blood pressure tool that's calibrated at different times. So you may not be getting consistency across your patient pool over time. And then obviously, based on the time or the length of a very long shift with nurses that are very limited as far as staffing numbers go, you will reduce the human error because you've got a single source of data with quality management systems embedded in. We do not send wrong values. We do not send calculated values. If your ECG strip is flatlined or your blood pressure line is flatlined, we say that we are not getting adequate signals due to noise or someone putting on the patient backwards or inside out and send them instructions there. So you can trust the data well. And then the indirect costs are obviously what you need when you are actually dealing with the new age of digital health, which is confounder analysis and heterogeneity. Has this product been tested on adequate Black Americans and adequate Hispanic Americans and adequate women, men, gender, age, BMI differences? We can do that because this is all self-administered at home. We can help those products achieve adaptation between phase one and phase four, giving that product the likelihood to go to market and then the long term value for peer review publications. This can happen in your offices hopefully sooner than later, whether you're conducting research right now and or down the road when we have adequate reimbursement. These are slides for the Q&A, which we're moving into right now. So I won't spend too much time. But these are the biomarkers that are clinically validated, 85 of them that we have that no other wearable or connected care platform has. The other aspect of our data platform is that it's customizable. So pending on your practice needs or your research needs, you don't have to use our app or our web portal if you don't want to. If you are used to using Medtronic Care Link, you are used to using Latitude, you are used to using the Impella feature, you're used to using the Edward Swan Gantz, we can plug right into those directly. Our ingestion pipeline at the server level is really the value there. We don't really care how the data is delivered. And we were listening to you from Digital Health Tools where you were getting exhausted about using more apps and more portals. We don't have to if you don't want that. I'll cover this in the Q&A. We are the first company that received a recent AI diagnostic and FDA clearance for our continuous blood pressure and hypertension diagnostic algorithm. You've seen a lot of FDA clearances for cuffless blood pressure. We have the data to back that up, which I would love to share. Most of those companies used regular technological principles and only tested on healthy patients. We did the opposite. We tested on 70% hypertensive patients. And we're gonna take that same algorithmic approach to right heart pressures. Now that FDA is comfortable with our approach, we are gonna take that and hopefully replace the Swann-Ganz catheter. We've got some very good early data on a clinical study of 24 patients where the correlations look fantastic working through FDA and are bringing more sites on with that. Quick summary of the company for you. We are firmly into commercial stage. Four FDA clearances, very patented, robust patent technology. We're one of seven companies that Google invited to their AI scholarship program and obviously getting the right people from the right places. And while we love podcasts and Wall Street Journal articles, we point our customer community and cardiology community to our peer review publications and our associations with the Physician Society first and only tier one publications. And then really have a good standing on the policy side with AI and digital health, having been named to the AvaMed board and working with FDA's new digital health council that they'll be naming here in a few months. So with that, again, I wanna thank MedAxiom and American College of Cardiology and the HeartX program for having us on today and happy to answer any questions. Venk, thank you. Very impressive. I'm sure we have tons of questions from the audience. I have one I'll start off with right away. You mentioned it briefly a slide or two ago on the cuffless monitors that currently have FDA clearance. Tell me more about how your product SimpleSense is different. How and why is it different? Yeah, great question. I will go back to that slide quickly. We did three different study arms. The other cuffless blood pressure tools that you're probably familiar with use a technological principle called pulse wave transit time or pulse decompensation time. And they use that through an optical sensor or a PPG sensor. What you in your practices have probably realized that while that technological principle is understood by FDA, it is only really adequate for patients where blood pressure is linear, healthy patients and prehypertensive patients. As a cardiologist, you know as well as I do that blood pressure is linear until it's not. At the edges of stage one and stage two, it becomes logarithmic. Same with the hypotensive patients. And so what a PPG sensor or an optical sensor does is it really just moves the y-axis of its ability to track a boundary of changes based on that change in pulse decompensation time. We used machine learning and AI techniques. So that's a new technological principle. We did not use an optical sensor or pulse wave transit time. We used all of the inputs of these 85 biomarkers from our device and did correlative analysis across three robust study arms to validate against the gold standard sigma nanometer as well as the ABPM. And so that first study arm was, again, this was a lot of work with FDA over three years. We thought we'd be done after study arm one. We were not. We did induce change the same way a blood pressure medication would. So we didn't restrict whether you were on medication or you were not. So we heated up people's hands to reduce negative change of blood pressure. And then we would test them with two independent nurses while we were collecting continuous data with our device. Then we would do the opposite. We would induce positive change with cold stimulus in their hands. Then we would have them do a brisk walk with the cold stimulus in their hands. And all the while we're taking these multiple measurements, 36 of a time, of the gold standard. After that study arm one, FDA was like, you need more geographies. You need more heterogeneity. So we did study arm two and really focused on the change aspects of the technology because that's where PPG or optical sensors or pulse wave transit time cuffless blood pressure tools are limited. They are not capable and are not labeled for what we have, which you'll see on the left-hand side. We are capable of tracking changes of plus or minus 15 millimeters of mercury, irrespective of your hypertensive classification on systolic and plus or minus 10 of diastolic, irrespective of your hypertensive classification. We proved that by doing a mirror of the US census, right? We did equal levels or a minimum level of 25 to 30 patients in each class of hypertension, but mirrored the ethnicities, mirrored the ages, mirrored the BMI of the middle 90% of the US census. And we showed 232 occurrences of these significant changes. That is very meaningful from a data standpoint. In addition, as you guys probably know, you take a 30 second reading and you say, okay, you're 135 over 82 and you are therefore prehypertensive. They were prehypertensive at that point in time, right? We also showed 130 occurrences, which is not gonna be surprising to you, but no one's been able to quantify this before. We've shown occurrences of people moving in and out of different hypertensive classifications, 130 occurrences of that. Started at prehypertensive, moved to stage one, started at stage two, dropped down to prehypertensive as they got comfortable and were breathing. Very important data to understand that induced change. And then obviously the third study arm, while we don't use a calibration value per se, like an optical sensor, because there's no drift in the actual optical sensor of pulse wave transit time, that was unfortunately something we had to work and compromise with FDA, just because that is the technological principle that a trusted or known historical principle is used. We do expect that we will expand that to the shelf life of the device, which will be six months. Right now it is cleared to be used for about a month, but we expect that to be cleared just because we know that there is no drift there. So the data really stands up to itself. We expect to have this peer review published post the FDA approval here shortly, which we will certainly be passing along MedAxium and ACC's channels when it's done. But I hope this was a helpful answer to the initial question, Claudia, on really differentiating our blood pressure approval, the value of the continuous nature and its ability to track these significant changes in and out of different classes of hypertension across all ethnicities, ages, and ranges that the US Census represents. Great, thank you. Very helpful, Venk. I'm gonna encourage the audience and remind the audience that you can use the Q&A button at the bottom of your screen. If you hover over the bottom of the screen, please go ahead and type in your questions there. I do have some in front of me here. The second question I have is, how did you create SimpleSense? What's the story behind the founding of nanoware? Yeah, I love telling this story. The cloth nanotechnology was invented, which is the foundational tech of the SimpleSense device and platform, was invented by our co-founder and chief innovation officer, Dr. Vijay Varadhan. If you're paying attention to my last name, we share a last name. He is my father, 40-plus-year academic researcher, predominantly at Penn State University and the University of Arkansas. This was almost a culmination of his life's work. He started working on nanotechnology in the 90s when nanotechnology wasn't even really a word. Using the concept of expanding footprint on a sensor modality on a space that you can't expand the actual ground of. So a submarine or sonar detection or a high-altitude plane, you can't make the submarine bigger or a plane bigger to capture more data from a sensing standpoint. But if you put nanosensor technology on, you're using the same real estate of the plane or the subway, but you're getting billions of more sensors there. So Dr. Varadhan had the same concept with the human body, an equally hard-to-detect environment, which is the human body. There's a lot of sources of noise here, right? Can you use the nanotechnology and that same principle to sort of cut down that noise and give very, very accurate, high-sensitive, very high-fidelity signals off the body non-invasively? And so while we are a young company, Dr. Varadhan, and who soon became our founding engineers, his master's students at the time, they had been working on this core concept of the technology for some time in the halts of academia with government funding, DOD and NSF and NIH funding. And then with my business hat on, thinking that there was a good opportunity to get this technology outside of concept and into physician and patient hands is when we started the company in late 2014 and went through clearing the cloth with FDA in 2016, then getting the multi-parametric device approved in 2020, then the full software platform approved by FDA in 2021, setting us up for these software-as-a-medical-device diagnostics, which has always been our goal. We want to be a platform for cardiovascular disease that is really lifting the burden off of the cardiology community to treat as opposed to monitor and manage. And we're approving that with the fourth approval that we just received this year, which is that first AI diagnostic in hypertension. Hope to have more coming soon in heart failure, in structural heart disorders, and fringe cases on cardiology, fringe therapeutic verticals on cardiovascular disease, sleep apnea, diabetes. We all know there's connections between that and cardiovascular disease. That is unquestioned. Nobody's really been able to quantify that. So bringing in CGM data alongside all of our cardiopulmonary metrics is an example of where we're going to be able to divulge those individualized characteristics and quantifiable connections between cardiovascular disease and some of the comorbidities that are on the edges tangentially related. Great story. Decades long, and especially the last 10 years, an exciting trajectory. All right, I have another few questions here. Let me pull them up. Do you see nanoware ever being used as an RPM device? Just the device for research purposes or other use cases? Yeah, great question. I probably blew by it. We are starting on the research side simply because the economics and the business considerations kind of forced us to do that. We could have played around with existing RPM codes. I personally, and we'll decide, we'll see whether I made the right decision on it or not. I personally have steered away from the RPM codes. I think there's still a pretty wild, wild, wild, pretty wild, wild west utilization of them is a weight scale qualifying for RPM codes versus pure hemodynamics. Right now, you would fit under the same bucket. And the minute we start jumping in there, we're basically saying we are of the same therapeutic or treatment value as a weight scale or a standard arrhythmia device. And we just do a lot more than that. So I think we will follow the iRhythm and Preventis and biotelemetry pathway, which is collecting enough real world evidence over the next couple of years and establishing our own codes with reimbursement. And those will be some form of RPM codes. They may not be what you are familiar with today, the nine, nine codes that are, in fact, I assume they won't be because they'll be specific for the applications of hypertension, structural heart, heart failure, potentially sleep apnea and diabetes as opposed to generalized RPM codes. So there's a plan to get there. We unfortunately can't be there right today just because the business considerations limit our ability to use the codes that are out there right now. And the last thing I'll say about that is, is that I do think from a cardiology community and user community, and I think this will change over time, the per use basis of reimbursement, I think is probably gonna shift a little bit. I think it would have to with a device like ours. I mean, are we gonna charge for how many times you use the device when it's reusable? I don't think we will, right? I mean, we're not shipping a 14 day disposable patch that has to be charged for that use case in time. In theory, your patient could use this once a month or once every two weeks for five years, right? So how somebody is being paid for that probably should be more on a per patient, per license, per patient license basis. And those are discussions not of your problem at the cardiology community. That's what industry and Center for Medicare and Medicaid really needs to start tackling. Because I think that's probably the main issue that digital health tools have not been widely adopted. Number one, they're probably all not super efficacious and the physician community points that out. So there were a lot of gen one, gen two companies that sounded really cool in what they were delivering, but really didn't provide the promise from a technology standpoint. But even if they did, there was no mechanism for them to get paid. And so that burden was coming on the health systems who were trying to do upfront costs on value-based care. And that unfortunately didn't work, right? So I think replacement costs need to be proved to CMS, long-term value, but in addition, alleviating the burden upfront and saving the cardiology clinics, the health systems, even the ambulatory surgical centers, where a lot of you are gonna start doing your procedures, if not already. Are we saving that money upfront to be a pure replacement tool, which we're pretty excited about? Well, that is very exciting. We have reached time. I have other questions, but I will share those with you after the webinar, Venk. Thank you so much for your time. Thank you for the information and thank you NanoAir for being a part of MedAx. And MedAxium, ACC, Hardax, feel free to share our contact information with anybody that's interested in membership. Yes, anybody who wants to reach out, please just reach out to your contact at MedAxium and we will get you Venk's information. Thank you very much. Thank you for taking the time to hear our story today. Thank you.

NanoWare

HeartX Innovation

SimpleSense device

cloth nanotechnology

remote diagnostics

AI

continuous monitoring