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A Diabetes Drug for Gout; Time Spent on the EHR: It’s PodMed Double T!

PodMed Double T is a weekly podcast from Texas Tech. In it, Elizabeth Tracey, director of electronic media for Johns Hopkins Medicine, and Rick Lange, MD, president of the Texas Tech University Health Sciences Center in El Paso, look at the top medical stories of the week. A transcript of the podcast is below the summary.

This week’s topics include preventing gout with a diabetes medication, physician time on electronic health records (EHRs) in outpatient visits, ageotypes, and FDA changes to the approval process and outcomes.

Program notes:

0:48 Personal aging markers and ageotypes

1:48 184 molecules associated with age

2:48 Clinical implications unclear

3:49 Individuals vary a lot

4:17 Physician time on the EHR

5:15 17% of time ordering

6:15 Falls on physician to enter

6:42 Two types of diabetes meds and gout risk

7:43 40% reduction with SGLT2 inhibitors

8:43 Gout affects almost 10 million people

9:06 Looked at 25 years of FDA approval process

10:06 All attempts haven’t improved overall process

11:06 Fewer studies and fewer patients

12:07 End


Elizabeth Tracey: Is one agent better than another for people with type 2 diabetes if they’re also at risk for gout?

Rick Lange, MD: FDA approval and regulation of pharmaceutical drugs.

Elizabeth: What do we know about aging and so-called ageotypes?

Rick: And when your physician sees you as an outpatient, how much time does she or he spend on the electronic health record?

Elizabeth: That’s what we’re talking about this week on PodMed TT, your weekly look at the medical headlines from Texas Tech University Health Sciences Center in El Paso. I’m Elizabeth Tracey, a medical journalist at Johns Hopkins, and this will be posted on January 17th, 2020.

Rick: And I’m Rick Lange, President of the Texas Tech University Health Sciences Center in El Paso, where I’m also Dean of the Paul L. Foster School of Medicine.

Elizabeth: Rick, let’s turn first to Nature Medicine. This is entitled, “Personal Aging Markers and Ageotypes.” That’s what type of aging person are you, revealed by what’s called “deep longitudinal profiling.” Wow! What in the world does that mean? They had 106 healthy individuals who ranged in age from 29 to 75 years and they took a look at different types of what they called “omic” measurements: genomics, proteomics, metabolomics. These included transcripts, proteins, metabolites, cytokines, and microbes that were resident in them, and clinical values obtained from the laboratory.

They looked at these folks over about 4 years, and in toto, they gathered 18 million data points on all of these people. I just find that absolutely remarkable. They also corrected for body mass index and gender, and they found 184 molecules from different “omes” — that’s the “omics” — that showed different trends and levels of association with age. They also found that several microbes that are resident on our bodies changed as folks aged.

The other factor that they brought in here was insulin resistance, and they found 10 molecules that were significant in the insulin-resistant and insulin-sensitive groups. They tried to correct for lifestyle and medication changes for people during the course of this study, as well as physical activity and BMI [body mass index] changes. They did see that some of these factors that they associated with age changed in a positive or negative direction often, depending on whether this person lost weight.

Ultimately, they came up with what they called these “ageotypes.” Those were immunity, metabolic, liver dysregulation, and kidney dysregulation. At this point, it’s unclear to me what kind of clinical implications that might have. They noted that almost all of these factors that they identified could potentially be modified with lifestyle and weight loss kinds of changes. Not really clear to me exactly how it’s going to be relevant, but points to, if we take a look at these factors, we could maybe interrupt the trajectory.

Rick: I agree with you. This was an interesting study trying to look at what the normal aging process was, how that might differ among individuals because we age differently, and perhaps identify dysfunctional aging processes. Since our aging is a reflection of our genetics, environmental factors, our lifestyle, and exposures, it’s very heterogeneous. Perhaps by following some of these “omics” or some of these ageotypes, when we realize we’re getting into a dysfunctional or bad ageotype, maybe we can change things. We can monitor it to see whether we’re headed into a more healthy aging process.

Elizabeth: One of the things that was absolutely abundantly clear is that individuals varied a lot, and so that speaks to me for the need to actually monitor each individual even if they fit into a particular ageotype.

Rick: We talk about “big data” and how it can influence healthcare. This is personal “big data,” but we have the computer — the longitudinal profiling — to be able to assess that over a long period of time.

Elizabeth: More to come, undoubtedly. Let’s turn to the one that you were totally vested in — that was Annals of Internal Medicine — and this is a look at how much time physicians spend on the electronic health record while they are in a clinical encounter.

Rick: The amount of time that we spend with electronic health records is one of the major causes of physician burnout in every survey that’s ever been done. What these authors attempted to do was to quantitate the amount of physician time that was spent using the EHR — electronic health record — during outpatient encounters. They had software that allowed them to analyze by keystroke and movements of the mouse and precisely how much time physicians spent using the electronic medical record. They surveyed over 155,000 physicians. There were over 100 million patient encounters from 417 different health systems. Physicians spent an average time of 16 minutes and 14 seconds per encounter. They spent about a third of that time doing chart review, about 24% of that time doing documentation, about 17% of the time doing ordering.

Elizabeth: And what was the average length of the visit out of which this 18 minutes came?

Rick: They couldn’t quantitate that, but the physicians doing primary care spent more time doing EHR than other specialties. Now you have better access to information with the electronic health record, but you spend a lot more time on it, and it’s a huge patient dissatisfier. In my entire time of training, I don’t remember of any physician ever complaining about paper medical records being a physician dissatisfier as we’re discovering with the EHR.

Elizabeth: Clearly, we know lots of things about the EHR and it’s a really a billing device. It’s not really a clinical care device, and so one of my questions is how can we modify it such that the data that’s gathered by the physician is a whole lot more relevant to the clinical care?

Rick: We are now required to submit more data for data analytics for insurance companies and hospital systems and things that don’t really contribute to patient care. But it’s required to be submitted and oftentimes it falls on the physician.

Elizabeth: I, for one, am optimistic that we’re going to develop programs that are really good at taking audio-spoken information and translating them into the record.

Rick: There are some very robust translation services available. However, in my opinion, we need to do less documentation about things that really don’t contribute to patient care.

Elizabeth: I agree. Let’s remain in Annals of Internal Medicine and let’s take a look at this issue of two different types of medications for type 2 diabetes and their impact on the risk for developing gout. It turns out that this hyperuricemia is common in patients with type 2 diabetes and that particular condition is absolutely related to the development ultimately of gout. In this case, they took a look at people with type 2 diabetes who were newly prescribed either one type of medicine — an SGLT2 inhibitor — or a GLP-1 agonist. They were able to accrete almost 300,000 adults with this particular condition, newly prescribed one of those two medicines who had type 2 diabetes. How often did these folks develop gout?

For those who were on the SGLT2 inhibitor, they experienced 4.9 events per 1,000 person-years in contrast to those with the GLP-1 agonist who developed 7.8 events per 1,000 person-years. So, a 40% reduction. One of the things that was really interesting to me and it makes sense from a biological perspective, because the sodium glucose cotransporter 2 or SGLT2 inhibitors block the resorption of glucose at the proximal convoluted tubule in the kidney, that’s what the mechanism is for making sure that uric acid is secreted into the urine. They did take a look, finally, at the very end of the paper at something that, I think, is highly relevant and that’s the cost. They really turn out to be just about the same when we look at those two types of medicines.

Rick: For people that have type 2 diabetes that’s not controlled with lifestyle and they have to add an oral agent, there are two or three different oral agents that can be used. It’d be nice to be able to use one to have some additional benefit. For example, the SGLT2 in people with heart failure, we know it prevents heart failure complications. What this would suggest is it can also prevent gout, so this is just an added benefit. I think this is pretty important. You know, gout affects almost 10 million individuals. Just having gout increases your risk of having cardiovascular disease by 30%.

Elizabeth: Interestingly, I noted that they identify a study that’s underway where they’re employing this agent to see if it can help to prevent gout, even in those who don’t have type 2 diabetes.

Rick: Absolutely, and so that would be very interesting.

Elizabeth: Okay, let’s turn to our final one. Back to you, Rick.

Rick: This is a study that’s looked over the last 25 years — that is, from 1983 to 2018 — to see what the FDA has done to try to improve approval of pharmaceutical agents: that is, to try to shorten the amount of time that these things come to market without increasing the risk. There have been a number of legislative and regulatory initiatives that have really changed drug approval at the FDA. Did we actually increase the number of medications approved from the FDA from 1983 to 2018, and did we shorten the time? Unfortunately, what we found out is that, for example, from 1990 to ’99, there were 34 products that were approved by the FDA. In the next 10 years, 25, and in the next 10 years, 41, so not a substantial increase.

When you looked at the time it took, it actually increased from about 8 years to 9 years — now, a shorter period of time spent in the FDA approval process, but the overall length not really very different. They’re approving these drugs with fewer studies and with the same number of patients. So, in essence, all the things that the FDA has attempted to do probably hasn’t significantly affected, in the overall picture, the approval process. Now, there are specific medications we can point to, for example, the one for spinal muscle atrophy we talked before about that really did an expedited review, and it was approved within months on the basis of less than 100 patients being studied and not even all the results being available. But, in general, when you look at the total picture, all these things haven’t been as successful as we’d like.

Elizabeth: Let’s mention that this is in the Journal of the American Medical Association. I think one of the things in one of the other studies we were talking about this week that was mentioned is the FDA’s real-life surveillance program, where they follow up with the release of some of these things with taking a look at real-life data and what happens when we actually get the thing out there. That’s not, of course, going to change anything relative to approval or times for all of that. My hope is that even though approvals are based on fewer studies that it will improve safety.

Rick: You can make the case that if we do it with fewer studies, fewer numbers of patients, a shorter period of time for followup, the fact you won’t have all that safety data, and that’s why the postmarketing surveillance ends up being incredibly important. Now, the other thing that happens is if we’re trying to shorten the time for drug approval sometimes we don’t use very hard endpoints. We use surrogate endpoints.

You and I have discussed before that let’s say an agent lowers a sugar in someone with diabetes. Well, that’s really good. But the question is, if it lowers the sugar but somehow increases mortality, it’s of no benefit. For example, one of the drugs that we know that was approved to lower uric acid and to decrease gouty attacks looks like it increases cardiovascular mortality, so the surrogate endpoint looked good, but the hard endpoint, not so good.

Elizabeth: On that note, that’s a look at this week’s medical headlines from Texas Tech. I’m Elizabeth Tracey.

Rick: And I’m Rick Lange. Y’all listen up and make healthy choices.