2.4 Why HRV Is Not Utilized in Clinical Medicine & Factors Affecting HRV

Learn some reasons why there is a lack of HRV utilized in clinical medicine as well as the factors affecting heart rate variability.

Video Breakdown:

0:00 - 4:49 Lack of HRV in Clinical Medicine
4:50- 16:58 Factors affecting HRV

Why is Heart Rate Variability not utilized in clinical medicine? Well, it's just seemingly irrelevant in the era of coronary revascularization medical optimization. I had mentioned before that heart rate variability was no longer specific had a low positive predictive value. You know, we just did too good a job when we're addressing acute MI. And even if we have low heart rate variability, it really doesn't change what we do in the hospital. So that's the thing that doesn't really change whether we were trying to revascularize whether we would administer beta blockers, ACE inhibitors, aspirin the whole bit. So even if it's low, it doesn't change management. And so that's why our rate variability is just not utilized, particularly in the hospital setting.
This gets to the slide that I showed on the first talk, and why heart rate variability is considered antiquated. From the conventional medical community, it's just seemed like it was pertinent into the 1980s and 1990s, when cardiac care wasn't that optimal, but you have a different perspective of those people who are studying this at home. The other reason why Harvard girls is not utilized in clinical medicine is that it's just cumbersome for data collection, storage and analysis. This is something that we'd like to address in physio Q. And so hopefully, that's not going to be an issue in the future.
Remember that EKGs is particularly 24, EKGs, or Holter. Monitoring. This is Holter, when he first discovered it was really cumbersome and difficult. And the first one, I think it was in the 1960s, it weighed about 80 pounds, this is how it looked like. And then in the 1980s and 90s, you still have to use these reel to reel devices. And also cassette recorders when you're carrying this around. And in order to analyze this data, this was all analog data. This was not digital. So it's something that you had to take a look visualize, they have to have special programs to figure out when was the b2b interval, very hard to do. And that's why not so many people in the 1980s and 90s, were capable of doing this, they didn't have the tools, they didn't have the analytical system to make it happen. And during a time when I was training, and then in the late 1990s, and then 2000s, this is what we used. This was something that you put around the neck, it was cumbersome, you couldn't sort of wear it around, it was hard to take a shower with it.
Now things have changed. So you could have these wearables, you have the watches, this is the XyO patch, you could put on the skin last time for 14 days, the pole or you can place on there for a day, if you wanted to kiss it depends on the battery life and whether your comfort level. But even with these devices, it's really difficult to incorporate EKG data. Because we're in the era now of electronic medical records, and electronic medical records as a satellite system. Each hospital has their own system, a lot of them have epic, Meditec Cerner, etc, it's very hard to acquire or integrate some of this data that you get at home and incorporated into the electronic medical record. So there is a, you know, a difficulty in incorporating this in putting into practice.
The other thing is, it's difficult to integrate heart rate variability into a disease centric hospital based system. Heart rate variability is somebody say, is much more of a health positive health marker. And so we're not really designed in medicine to address a positive mark, we're very much focused on diseases. The other thing is that it's hard to as I mentioned, acquire this an outpatient setting, it's not reimbursed. So if we measure heart rate variability, we don't get reimbursed by insurance companies. And the other thing is primary care doctors, clinicians are overwhelmed. They just don't want to deal with this extra data that comes in to them and you know, can be complex datasets. And one primary care doctor said that they didn't want to get this data because they didn't want to be liable for it, if something was shown a heart rate variability and show that something was wrong, they don't want to miss it. So you know, it's a stressed, very busy, disease centric hospital based system. And so something like heart rate variability is probably not going to be incorporated for a while for that reason.
The final thing is Heart Rate Variability tends to be very messy, because there are numerous confounding factors that doesn't discount it. It's just that it is highly sensitive. So there are many things that could affect it. And this is where I'm going to sort of spend the last part of this arc to to talk about what factors influence heart rate variability.
The first one that's really important to recognize is that there is a significant correlation with heart rate. And you can see this this graph where R is low, which is A inverse of heart rate. And you can see high frequency. And you can see that the higher the heart rate, or lower the R interval, the lower the high frequency that you see here. And this sort of goes a little bit to what escala was asking about, like, well, Doesn't this mean that heart rate then is essentially equivalent to high frequency that low heart rates is suggests High Heart frequency, high frequency, etc? I think yes, that's just shows some things. But as you see here, that there's much more variability on the on the vertical graph here, that you'll see a lot more of a range and the high frequency, so high frequency tends to be much more sensitive. And it has much more of a specific focus on parasympathetics. Whereas heartbeat itself is a balance between person pathetic and sympathetic, is this just goes to show you, the faster the heart rates, lower the heart rate variability.
The other thing that it wants to say was heart rate variability sometimes actually has different information, that heart rate alone, and heart rate is much more component of tonic control. It's sort of the mean vagal versus sympathetic tone. Whereas heart rate variability, particularly high frequency, which is the respiratory sinus arrhythmia is a modulation or respiratory modulation of the heartbeat. And when you have things that, you know, when you're at rest, you're relaxing your sleep that causes both your RSA, so your high frequency to go up, your heart rate could go down. And then conversely, when you're under stress, when you are exercising, you're you're tense, so you have heart failure, you're basically your heart high frequency variability goes down, and your heart rate goes up. So in both these situations, you have sort of similar effects. However, when you have increased blood pressure as an example, there is some evidence to suggest that it has the reverse effect. So high blood pressure will cause your blood pressure to go down. So it causes more bradycardia. But at the same time, it reduces your high frequency component of frequency, heart rate variability, so it's a little different. So your heart rate and heart rate variability responds differently to blood pressure increase. And the other thing is that the heart rate variability response to your co2 levels, whereas heart rate, not so much. So this is how our rate variability and heart rates are different.
So from my perspective, I think you should get both heart rate and heart rate variability, because they both sort of incorporate different information. But there are some, some coherence, there are some links to both of them. The other factor is posture. This is an example of someone who's laying down at rest, and then when you tilt them up, you can see the changes in the arm interval. So when they're laying down the already fully elevated, in other words, the heart rate is lower. And then when you're sitting up the blood pools down to your legs, and then your heart rate goes up. So it's about 106. And when you do the frequency analysis of this, you can see that actually, the high frequency components are going down, the low frequency theoretically should go up. Because if you call, the low frequency is a pseudo marker of the sympathetic nervous system. And when you're sitting up, your blood vessels need to contract in order for that to happen, the sympathetic nervous system needs to kick in. So naturally, the low frequency needs to be high when the sympathetic nervous system in there.
But when you calculate the actual area under the curve, you will see that there isn't much of a difference, because the low frequency is actually lower at 413. Compared to here, even though it looks larger, the overall area is actually smaller than this region. And the reason is, when you're setting up, your heart rate goes up, your heart rate variability goes down. And so proportionately, the large light, low frequency plays a larger role, but overall, the total power decreases. So this is why posture is an important thing to consider when you are doing heart rate variability.
And this is why when you're doing frequency domain analysis, you will normalize the data, you you know, you can see, for instance, at rest when you're laying down the total power, the frequency is elevated, but when you're setting up the total power is decreased. But you can see that the overall contribution low frequency is larger when you're an upright position. So this is the focus on this path of vagal balance this low frequency versus high frequency ratio. And this is why this has been used because it is supposed to account for the changes in that Old power that you get with postural change. So this is what you can use. The other factor that comes into play was respiratory rates. This is fast breathing, going to slow breathing. And you can see that the changes with our our interval, when you take a breathe in, your auric field goes down or heart rate goes up, and you're gonna breathe out, your heart rate goes down. And you can see that the magnitude the amplitude is much greater when you breathe, take slower breaths. So this is another thing to consider. When you're taking slower breaths, your heart rate variability is going to increase particularly focusing on the respiratory sinus arrhythmia. Stress is the other thing at rest, you could see that the heart rate variability has a nice fluctuations. When you're doing mental arithmetic stress, you have a heart rate increase but significant reduction in the fluctuations. And then same for exercise in for those people with severe CHF or congestive heart failure basically have no variability in this example here.
In the next few slides, we'll talk about actually this great study that was just published in Lancet digital health in 2020. It was published by Fitbit research group. And this is what happens when you have access to millions of people who have data. I'm not sure if all of them had agreed to have this data analyzed. But basically, the 8.2 million Fitbit users had their data analyzed on September 1 2018. The data was collected for 74 countries, United States was well represented this dataset. And they were able to sort of assess the effects of heart rate variability, certain influences certain factors that influence heart heart rate variability. In this case, this was h you can see from age 20 to 60. And you can see the different measures that they used here, RM SSD and high frequency, they kind of shared a similar results because again, this is high frequency results. This is low frequency SDR. SDR in this situation, he took five minutes, portions in five minutes. Again, if I you can see my prior slides deals with low frequency. So you are seeing the low frequency range here. And so what do I get from this results, you can see that with H, your heart rate variability, unfortunately goes down. And that the heart rate variability tends to go down faster, with the high frequency compared to the low frequency. I mean, it doesn't seem like it here. But you'll have to take a look at the scales here. And this sort of magnifies the changes. But basically, we need to do the calculations, your high frequency decreases dramatically with age. And the author's had suggested, this tells you is that your parasympathetic declines more rapidly as you age compared to the sympathetic nervous system.
The other difference that's noted here is that the men, this is men, blue, female in red, and then the solid lines are 6am dotted dashed lines are at 6pm. And you can see that men tend to have larger low frequency power in both during the morning and evening, where there's not much of a difference in the high frequency. So you see the effects of age, and you see the effects of gender. And the other final thing that I just wanted to point out that there's huge inter individual variability, you could be what's considered normal in the 20 years of age or within this, like standard, one standard deviation could be here. And you could still be considered normal with the same value at the age of 60. So there's a lot of inter individual variability here. There's a circadian rhythm here as well. These are for high frequencies and low frequencies. And you can see that there's a peak at five to 8am and Nadir around seven to 8pm. For all the high frequency, all the frequency measures that you can hear interesting, the low frequency for whatever reason, you see an earlier phase shift to the left for the elderly, older people. I don't know why that is. But clearly there is a circadian shift here.
Fiscal activity also plays a significant role in affecting your heart rate variability, this study, so basically the average number of steps the check per day over the past 90 days, and then they evaluate the effect of that on your heart rate variability. And you can see for all the measures that they have obtained here, that the more steps you took, the greater your heart rate variability goes. So it's good exercise if you're focusing on increasing heart rate variability. And this is a comparison between the younger folks versus the older folks. And the numbers that I want you to focus on partially We're on the right hand side, which is shown by sigma, this tells you the number of steps you need to take in order to increase one millisecond squared, on average of your heart rate variability.
So if you're young, it will take, you know, only 30 steps, maybe in order for you to increase your heart rate variability one milliseconds, or if you're older, it requires 50, up to 300 steps for us to really increase your heart rate variability. And there's a difference also for us older folks, is that it's much harder to increase your high frequency power. So it's harder for us to increase our parasympathetics Do we need to do up to 300 steps, particularly men versus women, in order to increase our higher frequency. So you know, high frequency, it's sort of sad story for many of us elderly men, is that heart rate variability decreases fast, particularly the high frequency range and the parasympathetic acts as you age, and it takes much more work for us to regain that high heart high frequency variability. And this is a nice review that was done by Addison in 2016. That shows all the factors that come into play we talked a little bit age, gender, ethnicity is one thing that there's some consideration for lifestyle factors activity exercise, we talked about that physiological causes, you know, heart disease, diabetes, asthma, etc. And then mood like stress is another thing. And then some environmental factors like you know, whether you're exposed to pollution, etc.
So, this is a nice review, but it tells you the heart rate variability is a holistic marker that sort of stresses all these things, and you have to take these into consideration if you really want to research

Course episodes

2.1 HRV in Cardiology: Historical Context

2.2 HRV for Sudden Cardiac Death & Methods to Assess Autonomic Nervous System

2.3 HRV in Cardiology: Clinical Evidence & HRV in Diabetic Autonomic Neuropathy

2.4 Why HRV Is Not Utilized in Clinical Medicine & Factors Affecting HRV

2.5 Controlling Confounders in HRV & Part 2 Recap