Chapters Transcript Video Hypertension Research at Sentara Dr. John Brush describes the hypertension research taking place at Sentara and discusses the regional trends and disparities. I'm John Brush and I wanna welcome everyone to um cardiology grand rounds at Centa Norfolk General Hospital. Um It's my pleasure to be here today. I wanna remind you that uh next week, Div Patel will be talking on ablation treatment for cardio uh for neurocardiogenic syncope. So be sure to return a week from now. Um Many of you may know me because I've been a cardiologist here for 31 years and about uh two years ago, a little less than two years ago, I moved over to uh be the Chief research officer at Centa. And so um I wanted to talk to you this morning about hypertension research at Centa. Now, this is what I wanna leave you with. I I don't want to leave you with. Oh John Brush did this research. What I wanna leave you with is that we have built capacity to do outcomes research, um population science, data science, implementation research here at CTERA. And because we've built this capacity, I wanna leave it uh leave this with you that um we have the capability of doing this type of research at Serra. And so I, I hope that that puts uh some ideas in people's head. Uh so that uh maybe we can think about how to expand this research to other people and to develop into a, a full blown program. So, uh my disclosures, I, I wrote a book and I get royalties. Um otherwise I have no relationships with industry or conflicts of interest to report. So just a background hypertension is a persistent problem in the United States. Um And it disproportionately affects African Americans. So, what are we doing about this? Um In 2014, 10 years ago, the CDC along with the A H A and the AC C Advisory uh uh Council recommended using health systems approaches to address hypertension in America. So a health systems real world data could provide actionable insights and guide these approaches. Centa actually already is doing uh Centa cares where we're reaching out to the community. It's possible that by examining our own experience through looking at our own real world data, we might be able to focus that effort and uh improve those uh those efforts. So, uh what is uh Sana's Real World data source? Centa was one of the first um health systems in the country. Actually to go on an electronic health system, we went up and running on epic in 2007. So our goal was to leverage this data for research. How did we do this? We were able to take our epic data extract it, transform it and load it into a common data model called OOP OOP stands for Observational Medical Outcomes Partnership op uh is, is a data format that was designed by researchers for researchers. Um It's a common data model that uh where the data is harmonized and standardized in a data format that is really designed for research. So this sort of compares epic clarity on the left which is a little bit cluttered. Um Epic clarity has 20,000 tables. You have to write sequel code to find data within Epic clarity. It's uh in an epic clarity reports to do a research study using Epic Clarity. Basically, you do a custom clarity report, writing lines of sequel code. Um op is sort of on, on the, on the right. It's organized, it's, it's uh harmonized op has 38 tables as opposed to 20,000 tables. And so you still have the right sequel code. But um you have to write less lines of sequel code because the data is easier to locate it's more efficient uh to use op than Epic Clarity for research. So this, these are the tables, uh the 38 tables in op. And uh so you can see that um you know, they have a number of different names and what have you conditions, uh devices, exposures, me, medications, people, um and so on and so forth. But all of these tables are all connected. Uh so that uh um it's um there, it's a relational database where all of the tables are connected. So this is a, you can't see, you can't read this because it's too zoomed out. But this is something that Mark Anderson at our place put together that shows the relationships of the tables in op and you can see that there are a number of different lines connecting all of these tables. And the table in the center is the concept ID table. A concept in op A concept is a clinical event or fact. Um And a concept ID is the unique identifier that identifies a clinical event or fact. So, atrial fibrillation, for example, the concept ID is 31 32 17. That's a unique number that identifies uh atrial fibrillation. But the concept uh table also shows where that data came from. It's a snow meed, it came from a snow med uh uh number. Uh and um and which was is, and that's the standard concept for this particular concept ID. So this is how uh op sort of organizes and, and uh standardizes the ta uh uh the information through these concept I DS. So we mapped over 13 years of epic data into the op common data model. We hired a company uh actually a Yale start up called Refractor Health. And the mapping, the mapping is um what what happens with the mapping is you convert source concepts to standard concepts and then once it's uh you have the standard concept, then you can attach, then you can uh uh attach that to the concept ID number. So uh for example, the Snow med uh for a FB is the standard concept and then it's uh it's uh labeled with the concept ID in op So to do this, we're, we're doing team science. Uh we were able to work with, with collaborators at Yale while we build our own internal capacity to do uh to work with this database. Um We were able to hire some people at Yale, I, I was able to get funding from the Baton Foundation to hire um to um to um uh enable Yuan Liu who's pictured here, an epidemiologist at Yale to work with us. And also um Yin Tin Liu is an analyst who's been working with us. You may know Harlan Krumholz has been working with us. So this is the Yale Yale team. There are many other analysts also in addition to Indian at, at Yale, who have virtual windows desktop access to our data in the cloud so that they can do the analytics from where they are it uh in New Haven or for or or actually wherever and help us do the analytics on the Centra team. Uh We have uh John Burroughs who's a cloud architect who helped uh uh set this up in the Azure cloud. Uh We have Mark Anderson, a software engineer between uh John Burroughs and Mark Anderson. They can do just about anything related to data, setting up data platforms. Uh I'm the content expert here locally and we too have uh several Refactor engineers. We meet uh every week, every Friday at noon, we meet and uh all of the people that you see here and more people are all on those meetings so that we can really develop team science. We wouldn't be able to do it without all of the uh specialty uh uh the specialties and, and the expertise uh that you see uh here uh involved. So we have big data. Uh We have actually data. It's actually up to almost 8 million individually identifiable people. Now, some of those people, we only had just a light touch, maybe they got a vaccine or something and, and we don't have very much data on them, but we have uh we have measurements and we have uh visits on about uh 3.5 million people. We've got 52 billion measurements in this database. So uh we wanted to look at hypertension first as our first use case hypertension is a, is a pretty good thing to look at with a computer because blood pressure is a number and computers are pretty good with numbers. And so we wanted to figure out how to how to analyze this data. This data is it's a block of data that's um 1.7 terabytes. Uh We have columns and rows and tables, but then over time and over uh 1212 years. So what we did is we slice that data into two year segments to do a serial cross sectional study. This is quite similar to the way that Nahan study uh examines hypertension nationwide. The only difference is that NA Haines extrapolates from a small sample whereas we're looking at the entire sample. So where do you start? Um we th this is just a distribution of systolic and diastolic blood pressure in our database. And you can see that we've got data on, we've got 100 and 34 million blood pressure measurements in our database. So we have to make some operational definitions and some decisions up front what we need to do when you're doing data analysis like this, it's quite similar to designing a clinical trial, clinical trial, you design a protocol. Well, we have to design a protocol as well. How do you go about systematically looking at your data? So uh to do that, we, you need to know something about how the data is structured in the electronic health record. So you can look at measurements like uh like blood pressure, you can look at it at the measurement level, at the visit level, at the date level and in the patient level. So how did we uh how did we look at it? What we did is if a patient had more than one measurement at a visit, we did what we would do clinically, we would throw out the first measurement and take the average of the res uh the remaining measurements. So that would give us a visit level uh measurement. And then if we a patient had more than one visit on a day, we would uh we, we uh took the average of the visit level measurements for blood pressure for a day. So we had uh blood pressure per day per patient that we were analyzing. So, uh we also wanted to make a computational diagnosis of hypertension. Uh We could have used the coded diagnosis of hypertension, but we would underdiagnose hypertension in our, in our uh data set because not everybody gets coded with hypertension. So we, we had to make some operational definitions if, if someone had was on an anti hypertensive medication, we felt that that was uh one part of the, of the definition. But we, we also had several other uh potential ways that we could define hypertension if during a two years uh period of time, if any blood pressure was greater than either one systolic, greater than 140 or diastolic, greater than 90. Uh We, we counted that person as having hypertension. Another way is if, if the first blood pressure measurement during a two year cycle was elevated. The third way is if a random blood pressure uh measurement during the two year cycle was elevated, this is actually quite similar to the Nahan study because Nahas went out and randomly chose people took their blood pressure uh at that random uh occurrence. And if it was a greater than 1 40/90 or they used antihypertensives, uh they were considered uh as to have hypertension in the hand study. And the final way is if any two blood pressure measurements during a two year cycle were elevated or they were on anti hypertensive medicine. So this is uh similar to the way we uh the, the way our guidelines tell us clinically to uh define hypertension. So, um we were able to look at over the over 12 years, we were looking at 1.3 al, almost 1.4 million acute uh are are unique patients over four years. And so you, these, the four year, 12 years, the these 12 years are, are divided up into two year cycles. And you can see that um that the, the number of people with each, within each cycle range from about 400,000 to over 600,000 people that we were looking at within each cycle. Unlike Na Haines, na Haines, uh we extrapolated their data to nationwide uh demographics and 15% were African Americans. Well, in our real world database, uh it's 25 to 26% of patients are African Americans. And so this is important African Americans are more likely to have hypertension. So this will affect our local prevalence rates. Um age uh gradually went up uh during the time uh time frame from 112 year cycle to the next. And so therefore, we had to age adjust all of our uh our analysis. This slide shows the age adjusted hypertension prevalence rates by operational definition. So this is one blood pressure, first blood pressure, random blood pressure and uh two blood pressures uh greater than 1 40/90 our first line uh anti hypertensives. And so you can see that no matter. And you can see first of all that, the prevalence of hypertension vary depending upon our operational definition. Some operational nation definitions were uh more uh more sensitive and others were more specific. Um And this is the Na Haines uh uh the one that's most similar to the Nahan study. You can see also that over the 12 year period of time, the uh prevalence of hypertension in our database in our region went up by about 5%. This shows the age adjusted hypertension prevalence rates by race and ethnicity and by operational definition, African Americans are shown in green. You can see that no matter how you define it. African Americans in our database in our region, African Americans have uh a 12 to 14% higher prevalence rate of hypertension compared to other races and ethnicities. So, and this is highly statistically significant. The way we analyze this is using a generalized estimation equation method which accounts for possible unmeasured correlation between observations between different time points. Some patients may have been seen during multiple two year periods of time. And so, uh we had to use uh the GEE method for statistical analysis to make comparisons. This shows the age adjusted blood pressure control rates by operational definition. So you can see that um our control rates range from about 60% to maybe 73%. Lee is showing us that we've got about, you know, 30% that are uncontrolled, 30 to 4 40% of our population in our region uh is uh is uncontrolled. And um, as you might expect, uh again, African Americans are shown in green African Americans are less likely compared to other races and ethnicities to have controlled blood pressure. This shows the age adjusted means systolic and diastolic blood pressures by ra race and ethnicity. And you can see that African Americans have significantly higher systolic and diastolic blood pressure compared to other uh races and ethnicities. This slide shows the age adjusted hypertension coding rates by operational definition. So, not everybody, as we suspected, not everybody is getting coded there. We, we've actually done another study where we're looking at the delay between a computational diagnosis of hypertension and the coding, uh the, the date of the code. And it's about a year, almost a year until the patient actually is coded as having hypertension. So, um, so you can see that um there, there's opportunities to uh to, to officially make uh recognize that people have hypertension in our region. Interestingly enough, African Americans had a higher coding rate compared to white, significantly higher coding rate. So we, we weren't uh they, they don't have worse hypertension uh and, and higher blood pressure because we missed the diagnosis. We, we appear to have made the diagnosis in African Americans actually uh more commonly um than, than um than, than whites and Asians here. So we presented this data along with uh two other abstracts uh on regional trends and disparities at the American Heart Association Conference uh in Boston. Um several months ago, you see Harlan Krumholz, uh Yuan Lou, Erica Spatz is also at Yale and she's involved in um another study, the porry study that I'm gonna tell you about in just a few minutes. Uh You may recognize Jaime Marrio here uh who uh used to work here. He also uh trained at Yale. And so it was nice to see Jaime at that conference as well. Um This work is now has been accepted for publication in the journal of the American Heart Association. This is a screenshot of the galley proof that I received just last week. And so, um we, we've uh uh we're waiting for to make final approval of the galleys and then we anticipate that this will be published uh very soon. Um The Jaha is a um open source uh uh web only uh journal. And so as soon as we uh finish up with the galley proofs. Uh it should be published uh very quickly. So we asked, you know, could racial disparities in hypertension be related to social determinants of health and social vulnerability? We know that you only about 3 to 5% of the variation in blood pressure can be attributed to genetics. Furthermore, we know from the human genomes pro uh human genome uh project that there is more genomic variability within races than there is between races. Um Race is really a social construct. It's not the, the the problems even though it runs in families. Um It's not, it does not appear uh hypertension runs in families, but it does not appear to be largely explained by genes. Other things run in families as well, particularly social vulnerability. If we, we know also that acute stress can bring somebody's blood pressure up. Well, how about chronic stress, the chronic weathering related to social stresses and vulnerability, economic stress and uh other other um social determinants. So we wanted to look at this um as you know, those social determinants of health aren't readily available in the electronic health record. There may be a time where we're required to um to measure this and record this. Uh but very few race is is there. But, but otherwise, uh that's not there. And so what we did is is we used uh information that comes from the American Community survey. This, this is the CDC C uh conducts the American Community survey every five years, they go into neighborhoods and they ask people about their social determinants of health. And so they've created a social vulnerability index which is uh divided up into four themes, socio, economic status, household characteristics, racial and ethnic, uh minority status, and housing type and transportation. And in each one of those themes are particular uh questions that, that are asked of patients. So that um and they go into uh particularly geographic areas until they, they interview enough people to have statistical relevance within each geographic area and they report it out by census tract. So what's the census track? This, this is a, a map of our area showing the census tracts in our area. Our census is taken every 10 years and every uh 10 years, the census tracks are recalculated. Census tracks are subdivisions of counties. Unlike a zip code, zip code is a um I is a postal code but a census track is a geographic uh uh division of counties where approximately uh where it's, it's calculated with each census where approximately 4000 people are in each census track. It's reported out as a pips code. Phipps code stands for federal Information processing standard. It's an 11 digit code that uh includes state county and census tract. So what we were able to do is we were able to take all the addresses of people in our database and we were able to convert that into a GEO code, uh latitude and longitude. And then based on the GEO code, the location that the patient lives in, we were able to identify the census tract that they live in. So we were able to uh we were able to apply a PIPS code to the patients that we have in our population. Now, most recently, we've got about uh 7.9 million people in our population. We were able to assign PIPS codes to um to 83% of them. Um 17% did not have the PIPS code because they had AAA post office number or maybe they had an inaccurate uh address. But in our longitudinal cohort, which I'll tell you about in a second, our FS match rate to our patients was greater than 95%. So we were able to identify the actual neighborhood that they live in and over 95% of the patients that we're gonna really look at. Look at intensively. This slide shows the social vulnerability index. This slide comes from the CDC. It shows the social vulnerability index by census tract in our area. So this is uh the, the colors represent quar tiles of uh the social vulnerability index. It's, it's actually it's reported out as a percentile, but it's purported out on a, on a scale of 0 to 1. So zero being um not very, totally, not vulnerable and uh uh one being a very vulnerable area. So uh you can see that there are areas in the Berkeley section or the Portsmouth or, or Newport News that are extremely uh vulnerable by the social vulnerability index where there are other areas say in Virginia Beach and Chesapeake that are not vulnerable. You can also see that the size of the census track varies uh where uh in the country, you know, it's, it's a very big area uh because it's uh not a densely populated area, but in the densely populated areas, the census tracks are quite small. Uh In fact, uh neighborhood level uh uh uh uh uh geographic areas. So we asked, is there a correlation between neighborhood level, social vulnerability and cardiovascular outcomes in hypertensive patients? So, if you wanna look at outcomes, if you wanna predict outcomes, we can't do the serial cross sectional study that we did before because that was just taking slices and, and, and analyzing to your slices individually. What we have to do here is to create a longitudinal cohort. So we had to carve out of this big block of data, this longitudinal cohort. So how do we do that? We wanted to build this cohort by having people that were seen for an adequate length of time. So we had, we had 2.3 million people who had at least two all type visits on different days. And 1.1 of these 1.1 million people who had uh multiple visits over a minimum of five years. So the first criteria was that um we wanted to have people that were seen at least uh over a period of five years. But then how about the intensity of ho how much we're seeing them? So, so for that, we invented something called the rate of years with visits. So you can see in patient, a this person has been seen over five years and was seen every year, over five years. So the RYV is 5/5. Uh patient B was seen over six years, seen uh four years out of those six years. So the Royv is four out of six and patient c was seen over 12 years but had only, you know, pretty rare uh visits. And so this person had an Royv of two out of 12. So if we look at our launch to a cohort, we've got 2.3 million people um with at least two visits, 1.1 with uh two visits over five years. And so we had 875,000 people who are seen at least half the years that they were seen. Um There are other ways that we could look at that, you know, whether the outpatients or, or Ryv of one, but we chose to um I chose the longitude, the cohort where uh that we're seeing at least five years and at least seen at least uh 50% of those years. So we're looking at length and intensity of follow up and, and using that definition, we had a launch due cohort that's 875,549 patients. We're calling this a Digital Framingham study. Uh The only difference is the original Framingham cohort was 5209 subjects. So our real world data cohort is actually 100 and 68 times larger than the Framingham study. So this, we're hoping that uh this will enable us to make some uh discoveries over the, over the length of time that we've seen in these patients uh about uh about outcomes and hypertension. So for this, we defined hypertension in our longitudinal cohort as again if they were on a blood pressure medication, uh that uh was one part of it and it, and at least two blood pressures at least 30 days apart and within two years. So this is our computational diagnosis of hypertension for the longitudinal cohort. And using this uh uh co uh computational diagnosis, uh we were able to identify a, a group of people with hypertension. Uh that um the cohort size was 544,000 people. This shows uh the launch a cohort by uh uh social vulnerability uh quartile. So you can see that the distribution of uh the social vulnerability within our, our cohort, uh most of them were uh uh were relatively uh uh not vulnerable, whereas 16% had the highest um and 22% had the second highest social vulnerability index in our, in our cohort. So this slide shows a composite outcome of myocardial infarction CV, uh stroke or heart failure. This is the incident, this is new onset of heart attack, stroke or heart failure among the 543,000 hypertensive patients by social vulnerability quartile. So you can see that there is a step wise dose response curve here if you will uh with social vulnerability and adverse outcomes uh for uh in this hypertensive uh uh population, this shows the Kappa Meyer curves uh of uh vent free survival uh uh by social vulnerability quartile. And you can see uh red is the least and and purple is the worst uh in terms of social vulnerability. And you can see that this is highly statistically significant. We're looking at hundreds of thousands of patients here and it's highly statistically significant. We also looked at another index. There's another index called the area deprivation index. Uh It's uh it's somewhat similar to the social vulnerability index. And with the exception, it doesn't include race, but it includes many of the other economic and uh educational factors uh that the uh social vulnerability index uses. And you can see that uh there's also um a uh highly statistically significant correlation between the area deprivation index and uh and survival free of of stroke, uh heart attack and heart failure in hypertensive patients this is the cox uh model of the social vulnerability ex uh index. Um We age adjusted the uh the findings in this. And you can see that um uh compared to social vulnerability index uh in the first quartile, the second quartile um had a statistically significant ha higher hazard ratio of 1.2 uh third quartile 1.4 and fourth quartile 1.6. Um uh compared to uh uh the first quartile of the social vulnerability index. Um patients who had missing, who we didn't have addresses on um and had missing had a uh also had a higher uh social v um Hi, higher chance of having uh uh uh adverse outcome um compared to uh patients with low social vulnerability. So, um I mentioned that that the social vulnerability index has four themes. So this looks at it by theme and you can see that the odds ratio, the, the most significant theme uh is the socio economic status. Um And the second most important theme was the housing type and transportation theme ends up, the racial theme uh is peculiar. Uh you know, it's either it's either an offer on and so it really didn't lend itself to statistical evaluation. And then in fact, en ends up it went the opposite direction and we're still working through what, what exactly this means. But, but clearly, there's um you know, AAA highly statistically significant correlation between socio-economic status and housing type. And um and the outcomes uh based on, on um on our, on our analysis, this shows mean systolic and mean diastolic blood pressure by uh social vulnerability index, quartile. Um the, the numbers are small but they're not trivial. And because the we're looking at hundreds of thousands of people, these are highly statistically significant and it goes, it goes a step wise, it's again, it's a dose response uh curve. And so uh really starts to look like, well, maybe this is the, this, this is causa causality. It's not just correlation but perhaps causality because of the dose response relationship here between mean systolic and diastolic pressure by social vulnerability index. And this slide shows the percentage of people who, who's mean blood pressure was uncontrolled. Um And you can see that there is a uh a highly si uh significant step wise uh increase in uncontrolled blood pressure rate by social vulnerability quartile. So, in summary, uh of uh you know, in this part of our, my, my presentation in our region hypertension prevalence is increasing. There are racial disparities in prevalence in blood pressure levels and in blood pressure control. And our findings are unique to our region. Uh Our findings are are slightly different than what the NAHAN study found nationwide. And it's largely because we have more African Americans and more and higher hypertension prevalence in our region. So this is important to know uh uh for our regional approaches. Social vulnerability does affect hypertension outcomes in our region. So focusing on our region will lead to actionable insights. We can focus and perhaps develop new ways and better ways that we can address hypertension in our region. So our next steps, we're gonna be looking at neighborhood level, social vulnerability, uh vulnerability and outcomes in maternal fetal medicine. Uh and, and also in diabetes. Um We've, because we've got geo coded data, we're also gonna be doing an environmental study looking whether you say the coal piers or the flooding or those type of things that happen in uh environmentally to our patients may affect outcomes. And we're also uh we've been able to use our own data platform working with Yale to help them develop electronic clinical quality measures uh in a contract they have for C MS. So we're doing other work with this database as well. So what we've been able to do is we've been able to make our data useful. That's the essence of data science. The essence of data science is the process of making your data useful. It's as simple as that. And so um the important thing is is to come up with the use case and useful data so that it all fits together very well. And so, you know, for this hypertension use case um and the op database, it really uh really fit together quite well and it was a really good opportunity to do some useful research. But so what we've done uh with this big data uh outlook. It, it's sort of an aerial view of big data. But we're also um uh planning some uh some um research that's more of a boots on the ground approach. And I wanted to tell you a little bit about that. We were able to uh obtain a grant from P Coy uh $3.4 million for our, our grant over five years along with uh Yale Mass General and Houston Methodist. The, the project is called Pressure Check. And um the porry uh call for proposals was to study hypertension in vulnerable populations. So, um and, and to improve blood pressure control and vulnerable populations. So, the goal of this project that we're doing with these uh three other institutions around the country is to ask a question, can blood pressure control be improved by remote blood pressure monitoring and uh remote blood pressure monitoring plus community health workers. So, to do this, to find vulnerable populations, uh What we're doing is we're reaching out to people through community based organizations. All of us have uh different ways of doing this in, in, in Boston. It's, it's food banks and, and, and Houston, it's a different way in our area. What we've done is we've reached out to African American churches. So Jen May and I have visited uh many African American churches had multiple visits have had the honor and privilege of meeting uh the pastors of these African American churches. And now we have contractual arrangements with uh with 10 African American churches in Norfolk, Virginia Beach, Newport News and Hampton. Um who are our collaborators uh in this effort to uh recruit patients and to uh carry out this study. So this is uh uh a group of our collaborators. It, it is just such a privilege to work with these people. And I have to tell you, I wish that I'd done this study 31 years ago when I first came to Hampton Roads because um I've learned so much more about the communities that we work with and the patients uh and where patients come from. Uh I think it's, it's been a, a remarkable uh experience. Um uh This shows Doctor uh Sharon Riley from uh Faith Deliverance, uh Christian Center, uh Doctor Jeffrey Guns from Second uh Calvary Baptist Church in Norfolk. Um truly uh wonderful collaborators and um and really uh becoming uh good friends. And so it's, it's just a privilege to work with these people on this project. We've been uh now we're now Jen and I are visiting churches on Sunday mornings to an announce uh our project. And it's just been remarkable what a welcome we've received from uh these churches in our area. Um And, but also the enthusiasm uh in the engagement of these churches uh because they all know what a problem hypertension is among African Americans. One of the things that I do is I say, you know, if you know somebody that uh you know, have a family member has hypertension, raise your hand, everyone in the congregation raises their hand and they are all riveted when we tell them about this project there. And when we're almost overwhelmed with people afterwards who come to us and wanting to uh express interest in participating in this study. One of the uh challenges is that um we've, we've had to figure out how to actually connect remote blood pressure monitoring uh with epic. So this shows that a Bluetooth enabled blood pressure cuff this uh um it communicates with something called a stell hub. Uh and a stell hub then takes that blood pressure and it communicates by cell phone to us uh through, through stell to our electronic health record. Well, ends up, you would think that that would be sort of a plug in and, and easily um accomplished, but it actually wasn't. There's, there are all kinds of things fir first of all, um the data comes in um it can't come in with PH I because it's coming in over cell phone. And so uh we, we, it comes in with a coded uh cell uh cell hub number. So we had issues with identity management. We had to create uh an order system, uh data interface, data routing flow sheets, validation, creating alerts all of this required multiple, multiple meetings with um our it folks and in addition to the ST Stel engineers, but once again, Sana's, it comes to the rescue. Sana's, it is a uh is a real resource at Centra. And uh we were able to uh put this, um we were able to work on this for months, actually develop and, and work through all of the technical challenges. And so now we, we have a, a very uh um uh cost effective and a very effective way that we can re uh remotely uh monitor people's blood pressure uh and have it flow into epic and, and monitor it uh through epic. So we're now in the process, Jen May is, is now doing Jen May is, has done a remarkable job uh on this. He's just been uh an incredible uh uh project manager for this uh this study. She is out doing blood pressure screenings and, and what have you, this, this number is now old. Uh we could uh screen many more than this and, and in our screening to show you how long our populations are uh over 60% 66% 70% of people who we screened are actually eligible for the study and that they have un uh relatively untreated blood pressure. And are our potential candidates? We're gonna be recruiting patients through, through these community based organizations. They're gonna uh the community based organized uh community based organizations are randomized to a specific sequence and we're recruiting through something called a stepped wedge approach where for the 1st 18 months, patients are recruited into care paths and then for the 2nd 18 months, uh they will be followed up um to, to measure the results. Uh So care path one is the usual care. Uh And so, uh we're not taking anything away from people, it is usual care. And so we're gonna be supporting the usual care that's more or less the control period of time that we're gonna be controlling against this uh care path too is the remote blood pressure monitoring where people will be asked multiple times per week to monitor their blood pressure remotely at home. Transmit that and then we will have a nurse practitioner that monitors that and responds to that if necessary to uh and to, to further engage the patient and care. Pa the three is remote blood pressure monitoring plus community health workers, we will uh hire community health workers, people that live and, and in their communities are, are accustomed to their communities who can contact the patients, talk to the patients about some of the social determinants, some of the stressors, some of the things that might be getting out of the way of proper uh blood pressure control. So both of these things we think will prob will hopefully increase engagement and and hopefully have an impact, a significant impact on blood pressure control. So, um to, to quote tip o'neill tip o'neill said all politics is local, I'd say that all medical care is local and personal. Uh There's more to learn about social determinants of hypertension at the neighborhood level. And at the individual level, research is le uh uh this research I think is leading to actionable insights on how we might improve blood pressure control and address racial disparities right here in our community. So um to conclude, um we're doing hypertension research at HY at, at Centa. But in doing so, we're building capacity, we're building research capacity. We now have this uh this a platform that can be used for other research projects by other people. And uh we also uh develop the capacity to do implementation research. We've all developed the capacity to process a federal, federal uh funding uh to fund a uh research here. So, in the process where I hope that we're, we're learning and growing and building our capacity capabilities to do research here at, at Centra, I'd like to acknowledge uh uh support and funding from the Batten Foundation uh from the Hampton Roads Biomedical Research Consortium from Porry and also through our, our um contract uh with Yale and, and, and uh indirectly through C MS. So um I'll pause there. I, I've got a couple of other slides about what happened during the pandemic if you'd like to see those. But I, but I'll pause there and, and, and open it up for questions and I thank you for your attention. Published April 17, 2024 Created by Related Presenters John Brush, M.D., F.A.C.C. Sentara Cardiology Specialists -Interventional Cardiology View full profile
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