CHEST Physician

The pandemic not only disrupted sleep but may have also triggered an uptick in the use of wearable tech. Sleep tracking was featured at the Cardiovascular Health Tech virtual conference 2022, sponsored by the Institute of Electrical and Electronics Engineers Engineering in Medicine & Biology Society technical committee on Cardiopulmonary Systems and Physiology-Based Engineering.

This news organization interviewed presenter Kelly Glazer Baron, PhD, MPH, DBSM, an associate professor at the University of Utah, Salt Lake City, and a clinical psychologist specializing in behavioral sleep medicine.

The interview has been edited for length and clarity.
 

Question: Are consumer sleep trackers mainly divided into “nearables” – things that you put at the side of the bed or under the pillow – vs. wearables?

Dr. Baron: There are so many different devices these days. There are things that you put under your mattress or pillow; there are bedside recording devices; then there are headbands, rings, wrist-worn, all kinds of things.

Q: At the conference, Philip de Chazal, PhD, (University of Sydney) described the evidence on sleep tracking smartphone apps as woeful. Would you agree with that?

A: Yes. I would agree if you’re looking at how accurate they are at recording sleep, particularly compared with what we would define as the gold standard, which is a sleep study wherein you have electrodes on the scalp and you’re measuring the electrical activity directly.

Devices that go under the pillow are extremely poor at deciphering sleep from wake time, which is really the main goal. They are best at detecting when you get into the bed and when you get out. But even then, there isn’t good evidence that they do that accurately when there are two people in the bed.

Overall, they may give you a general gist of what’s happening in terms of time in and out of bed, but we’re doubtful on their recording ability to tell sleep from wake time.
 

Q: Are the wrist-worn devices better for sleep tracking?

A: They’re getting better. We’ve used wrist activity monitors in research for years. They use an accelerometer to measure movement, and then an algorithm determines whether an interval of time is called sleep or wake.

Recently, they’ve incorporated more sensors, such as heart rate, and they can more accurately decipher rapid eye movement (REM) sleep from non-REM. They’re still not as good as doing a full sleep study. But they’re getting closer.
 

Q: If asked how you slept, most of us think we can answer without needing to look at a smartphone, but maybe not. Can you explain “paradoxical insomnia”?

A: You can’t really know if you’re sleeping because if you know you’re asleep, then you can’t be asleep because it’s a state of unconsciousness. How people decide whether they had a good night’s sleep probably depends on a lot of things about how they feel when they wake up in the morning or if they remember being up in the night.

Quality of sleep is not really something that people can directly ascertain. There is a selection of people who feel awake all night but they actually are sleeping. They feel that their sleep quality is poor: They’re suffering; they have insomnia, but from the objective data, they are sleeping fine.
 

Q: Is this related to non-REM stage 1 sleep, when you may not be aware that you’re asleep?

A: No. I’m talking about people who come into the sleep lab for an overnight study and get hooked up. And in the morning, they’ll tell the tech I was awake all night, but the tech will see that their sleep was just fine.

There is a disconnect between how people perceive their sleep and how they actually sleep. For most people it’s impossible to be completely accurate to know how much you’re sleeping. Then there are some people who perceive it very differently.

Sleep trackers don’t have the level of detail of sleep studies that use scalp electrodes. When we get into the details of sleep measurement, we’re measuring 30-second epochs (sampling periods), where we look at broad measures of electrical activity. There is even more detail there that can be pulled out using other techniques, such as analyzing the spectrum of the EEG. For example, some studies have found a beta frequency in the EEG of people with insomnia, so even though they are sleeping, they often feel awake.

Basically, the subjective experience of sleep somewhat overlaps with the objective recording of what’s happening on a sleep study, but not completely.
 

Q: You said that first thing in the morning might not be the best time to assess your sleep – if you wake up groggy and are already thinking, “The day is shot.”

A: In general, people really feel worst in the morning. Their circadian drive is low, especially if they’re a little sleep deprived. You shouldn’t judge the day on the first hour after waking – most people are pretty cognitively impaired. I tell people they need some boot-up time.

You feel differently as the day goes on and even at different points of the day. There’s a lull in the early afternoon because of your circadian dip and then we get a second wind in the evening. How you feel isn’t one flat line; it’s really a rhythm throughout the day
 

Q: Would you say that consumer sleep trackers are okay for individuals to use to see a pattern but are maybe not accurate enough to use more globally in research?

A: I think there is a huge opportunity to understand sleep at a population level. For example, if there’s been a hurricane or an earthquake or Superbowl Sunday, companies have an opportunity to look at the impact – say, daylight saving time and how it affects sleep across different countries, or men vs. women, or different age groups.

There was a paper about sleep among hospital workers in Wuhan during the outbreak of the pandemic. That was a creative use of wearable devices to look at sleep in a large population.

Now, of course, the devices are not given out randomly; the people who buy them are probably a little bit healthier, maybe a little bit younger – that sort of thing. It is a biased sample.
 

Q: As you note, mobile health trackers tend to be used by the “worried well.” Can you tell us about your paper that introduced the term “orthosomnia,” or “a perfectionistic quest for the ideal sleep in order to optimize daytime function”?

A: As these devices came out, more people were coming into the clinic and shoving their data in front of us saying, “I don’t feel well, and I don’t sleep 7 hours.” They were focused on this specific number. Back when we wrote this paper, the devices were primarily movement based (now the devices are a bit more accurate). Some would say, “My sleep is light, and it’s not deep.” We’d do a sleep study that showed that they have deep sleep, but they would still believe their device even though the device really wasn’t able to classify sleep accurately.

We even found people making their sleep worse because of the device. For example, trying to get the number higher by spending more time lying in bed trying to sleep which is the opposite of what you want someone with insomnia to do. These people held the data so tight and really felt that it characterized their experience, even though we sleep medicine practitioners didn’t find it very accurate and felt that it was somewhat unhelpful to their treatment.
 

Q: What advice would you give the harried primary care physician presented with a patient’s hypnogram or sleep pattern?

A: As someone once pointed out to me, it’s a conversation opener about their sleep. Did they buy the device because they’re worried about their sleep? It’s unlikely that you can glean anything clinically useful from the data.

I briefly look at it to see the duration of their sleep, the regularity in their sleep pattern – the pattern of awakenings during the night might suggest that they have some insomnia. But it doesn’t take the place of clinical assessment for conditions like sleep apnea: Are they snoring? Are they unrefreshed?

I had a patient in the orthosomnia study who was given a sleep tracker by a family member. He brought the data to his doctor who ordered a sleep study that found he had sleep apnea. He would say, “The device diagnosed my sleep apnea.” But that wasn’t actually the case; it just opened the conversation and the clinician said, “Well, let’s order a sleep study.”
 

Q: The device told him he wasn’t getting much sleep and then the sleep study told him it was apnea.

A: Right. It’s impossible to pick up sleep apnea. Some of the latest devices have some oximetry reading but it is not a clinically validated oximetry that could diagnose sleep apnea.

When these first came out I thought I’d get more referrals. So far, I haven’t had a single person come in and ask if they have sleep apnea. If you have a patient saying, “Hey, I’m worried about my oxygen level and here’s my data,” then the clinician should consider whether they need a sleep study for sleep apnea.
 

Q: You did a survey that suggests that clinicians are less keen on these devices than consumers. Conor Heneghan of Fitbit/Google also mentioned a study using the Fitbit Charge and a SleepLife portal. The patients were very engaged but only one physician (out of 49) logged into the portal to look at the data.

A: Our survey of sleep professionals (which we need to publish) showed that they were wary of the data. They found it frustrating in some ways because it took time out of the clinical encounter.

Some of them said that parents are putting trackers on their children and then catastrophizing their children’s sleep.
 

Q: Is there such a thing as an ideal hypnogram or does it vary by individual?

A: I would say that it depends on a lot of things. If you think about a hypnogram from a sleep study, the patient is not sleeping in their home environment, and it’s only one night. There’s a range of what would be considered normal, and it’s related to your sex and your age.

One night is not going to be sufficient to characterize your percentage in this or that sleep stage. Our patients come in saying, “I’m not getting enough REM.” But there isn’t a sleep disorder called lack of REM; there’s no treatment for that. It’s probably pretty normal for them or maybe they’re taking medications that suppress their REM, such as antidepressants.

The tech world is very interested to sense REM properly and to display it. But on the treatment side of things, there’s not much that we do with that data. We’re more interested in the consolidation of their sleep, the duration of their sleep, breathing-related sleep disorders, those sorts of things.
 

Q: Is there any reason to be concerned about the amount of REM sleep in terms of outcomes? We know that poor sleep can lead to bad cardiovascular outcomes, but has any of that correlated to sleep stage?

A: There are studies where they’ve experimentally deprived people of certain stages of sleep, but they’re not very useful in the real world. We’re looking at sleep holistically: Do you have a good sleep pattern? Any breathing-related sleep disorders? Insomnia? We don’t treat sleep by the stage.

Q: Any concern that people who are focused on a device may be ignoring the basic tenets of good sleep hygiene?

A: If people are doing things that are obviously bad for their sleep, like working too late, not exercising enough, sleeping in on weekends to compensate for being up late during the week, or probably the biggest thing contributing to insomnia – stress. A device itself won’t fix those things but it could show you the evidence.

If somebody really has a sleep disorder, then sleep hygiene alone is probably not going to be enough. They’re going to need to engage in a more extensive program to improve their sleep, such as cognitive-behavioral therapy for insomnia.
 

Q: Is there anything else you want to mention?

A: I don’t want to leave with a reputation of being against sleep trackers. I think they are a great opportunity for people to get excited about and learn about their sleep and try to improve it. We have a lot to learn about what people want from their data and how we can use that data to improve people’s sleep.

As providers, we can engage with our patients – sleep is an automatic process, but improving sleep takes some effort. Buying a device is not going to automatically make you sleep better. It takes work to establish a better sleep pattern; it may require some cognitive-behavioral therapy or treating a sleep disorder. That takes some work.

Dr. Baron reported no conflicts of interest.A version of this article first appeared on Medscape.com.

The pandemic not only disrupted sleep but may have also triggered an uptick in the use of wearable tech. Sleep tracking was featured at the Cardiovascular Health Tech virtual conference 2022, sponsored by the Institute of Electrical and Electronics Engineers Engineering in Medicine & Biology Society technical committee on Cardiopulmonary Systems and Physiology-Based Engineering.

This news organization interviewed presenter Kelly Glazer Baron, PhD, MPH, DBSM, an associate professor at the University of Utah, Salt Lake City, and a clinical psychologist specializing in behavioral sleep medicine.

The interview has been edited for length and clarity.
 

Question: Are consumer sleep trackers mainly divided into “nearables” – things that you put at the side of the bed or under the pillow – vs. wearables?

Dr. Baron: There are so many different devices these days. There are things that you put under your mattress or pillow; there are bedside recording devices; then there are headbands, rings, wrist-worn, all kinds of things.

Q: At the conference, Philip de Chazal, PhD, (University of Sydney) described the evidence on sleep tracking smartphone apps as woeful. Would you agree with that?

A: Yes. I would agree if you’re looking at how accurate they are at recording sleep, particularly compared with what we would define as the gold standard, which is a sleep study wherein you have electrodes on the scalp and you’re measuring the electrical activity directly.

Devices that go under the pillow are extremely poor at deciphering sleep from wake time, which is really the main goal. They are best at detecting when you get into the bed and when you get out. But even then, there isn’t good evidence that they do that accurately when there are two people in the bed.

Overall, they may give you a general gist of what’s happening in terms of time in and out of bed, but we’re doubtful on their recording ability to tell sleep from wake time.
 

Q: Are the wrist-worn devices better for sleep tracking?

A: They’re getting better. We’ve used wrist activity monitors in research for years. They use an accelerometer to measure movement, and then an algorithm determines whether an interval of time is called sleep or wake.

Recently, they’ve incorporated more sensors, such as heart rate, and they can more accurately decipher rapid eye movement (REM) sleep from non-REM. They’re still not as good as doing a full sleep study. But they’re getting closer.
 

Q: If asked how you slept, most of us think we can answer without needing to look at a smartphone, but maybe not. Can you explain “paradoxical insomnia”?

A: You can’t really know if you’re sleeping because if you know you’re asleep, then you can’t be asleep because it’s a state of unconsciousness. How people decide whether they had a good night’s sleep probably depends on a lot of things about how they feel when they wake up in the morning or if they remember being up in the night.

Quality of sleep is not really something that people can directly ascertain. There is a selection of people who feel awake all night but they actually are sleeping. They feel that their sleep quality is poor: They’re suffering; they have insomnia, but from the objective data, they are sleeping fine.
 

Q: Is this related to non-REM stage 1 sleep, when you may not be aware that you’re asleep?

A: No. I’m talking about people who come into the sleep lab for an overnight study and get hooked up. And in the morning, they’ll tell the tech I was awake all night, but the tech will see that their sleep was just fine.

There is a disconnect between how people perceive their sleep and how they actually sleep. For most people it’s impossible to be completely accurate to know how much you’re sleeping. Then there are some people who perceive it very differently.

Sleep trackers don’t have the level of detail of sleep studies that use scalp electrodes. When we get into the details of sleep measurement, we’re measuring 30-second epochs (sampling periods), where we look at broad measures of electrical activity. There is even more detail there that can be pulled out using other techniques, such as analyzing the spectrum of the EEG. For example, some studies have found a beta frequency in the EEG of people with insomnia, so even though they are sleeping, they often feel awake.

Basically, the subjective experience of sleep somewhat overlaps with the objective recording of what’s happening on a sleep study, but not completely.
 

Q: You said that first thing in the morning might not be the best time to assess your sleep – if you wake up groggy and are already thinking, “The day is shot.”

A: In general, people really feel worst in the morning. Their circadian drive is low, especially if they’re a little sleep deprived. You shouldn’t judge the day on the first hour after waking – most people are pretty cognitively impaired. I tell people they need some boot-up time.

You feel differently as the day goes on and even at different points of the day. There’s a lull in the early afternoon because of your circadian dip and then we get a second wind in the evening. How you feel isn’t one flat line; it’s really a rhythm throughout the day
 

Q: Would you say that consumer sleep trackers are okay for individuals to use to see a pattern but are maybe not accurate enough to use more globally in research?

A: I think there is a huge opportunity to understand sleep at a population level. For example, if there’s been a hurricane or an earthquake or Superbowl Sunday, companies have an opportunity to look at the impact – say, daylight saving time and how it affects sleep across different countries, or men vs. women, or different age groups.

There was a paper about sleep among hospital workers in Wuhan during the outbreak of the pandemic. That was a creative use of wearable devices to look at sleep in a large population.

Now, of course, the devices are not given out randomly; the people who buy them are probably a little bit healthier, maybe a little bit younger – that sort of thing. It is a biased sample.
 

Q: As you note, mobile health trackers tend to be used by the “worried well.” Can you tell us about your paper that introduced the term “orthosomnia,” or “a perfectionistic quest for the ideal sleep in order to optimize daytime function”?

A: As these devices came out, more people were coming into the clinic and shoving their data in front of us saying, “I don’t feel well, and I don’t sleep 7 hours.” They were focused on this specific number. Back when we wrote this paper, the devices were primarily movement based (now the devices are a bit more accurate). Some would say, “My sleep is light, and it’s not deep.” We’d do a sleep study that showed that they have deep sleep, but they would still believe their device even though the device really wasn’t able to classify sleep accurately.

We even found people making their sleep worse because of the device. For example, trying to get the number higher by spending more time lying in bed trying to sleep which is the opposite of what you want someone with insomnia to do. These people held the data so tight and really felt that it characterized their experience, even though we sleep medicine practitioners didn’t find it very accurate and felt that it was somewhat unhelpful to their treatment.
 

Q: What advice would you give the harried primary care physician presented with a patient’s hypnogram or sleep pattern?

A: As someone once pointed out to me, it’s a conversation opener about their sleep. Did they buy the device because they’re worried about their sleep? It’s unlikely that you can glean anything clinically useful from the data.

I briefly look at it to see the duration of their sleep, the regularity in their sleep pattern – the pattern of awakenings during the night might suggest that they have some insomnia. But it doesn’t take the place of clinical assessment for conditions like sleep apnea: Are they snoring? Are they unrefreshed?

I had a patient in the orthosomnia study who was given a sleep tracker by a family member. He brought the data to his doctor who ordered a sleep study that found he had sleep apnea. He would say, “The device diagnosed my sleep apnea.” But that wasn’t actually the case; it just opened the conversation and the clinician said, “Well, let’s order a sleep study.”
 

Q: The device told him he wasn’t getting much sleep and then the sleep study told him it was apnea.

A: Right. It’s impossible to pick up sleep apnea. Some of the latest devices have some oximetry reading but it is not a clinically validated oximetry that could diagnose sleep apnea.

When these first came out I thought I’d get more referrals. So far, I haven’t had a single person come in and ask if they have sleep apnea. If you have a patient saying, “Hey, I’m worried about my oxygen level and here’s my data,” then the clinician should consider whether they need a sleep study for sleep apnea.
 

Q: You did a survey that suggests that clinicians are less keen on these devices than consumers. Conor Heneghan of Fitbit/Google also mentioned a study using the Fitbit Charge and a SleepLife portal. The patients were very engaged but only one physician (out of 49) logged into the portal to look at the data.

A: Our survey of sleep professionals (which we need to publish) showed that they were wary of the data. They found it frustrating in some ways because it took time out of the clinical encounter.

Some of them said that parents are putting trackers on their children and then catastrophizing their children’s sleep.
 

Q: Is there such a thing as an ideal hypnogram or does it vary by individual?

A: I would say that it depends on a lot of things. If you think about a hypnogram from a sleep study, the patient is not sleeping in their home environment, and it’s only one night. There’s a range of what would be considered normal, and it’s related to your sex and your age.

One night is not going to be sufficient to characterize your percentage in this or that sleep stage. Our patients come in saying, “I’m not getting enough REM.” But there isn’t a sleep disorder called lack of REM; there’s no treatment for that. It’s probably pretty normal for them or maybe they’re taking medications that suppress their REM, such as antidepressants.

The tech world is very interested to sense REM properly and to display it. But on the treatment side of things, there’s not much that we do with that data. We’re more interested in the consolidation of their sleep, the duration of their sleep, breathing-related sleep disorders, those sorts of things.
 

Q: Is there any reason to be concerned about the amount of REM sleep in terms of outcomes? We know that poor sleep can lead to bad cardiovascular outcomes, but has any of that correlated to sleep stage?

A: There are studies where they’ve experimentally deprived people of certain stages of sleep, but they’re not very useful in the real world. We’re looking at sleep holistically: Do you have a good sleep pattern? Any breathing-related sleep disorders? Insomnia? We don’t treat sleep by the stage.

Q: Any concern that people who are focused on a device may be ignoring the basic tenets of good sleep hygiene?

A: If people are doing things that are obviously bad for their sleep, like working too late, not exercising enough, sleeping in on weekends to compensate for being up late during the week, or probably the biggest thing contributing to insomnia – stress. A device itself won’t fix those things but it could show you the evidence.

If somebody really has a sleep disorder, then sleep hygiene alone is probably not going to be enough. They’re going to need to engage in a more extensive program to improve their sleep, such as cognitive-behavioral therapy for insomnia.
 

Q: Is there anything else you want to mention?

A: I don’t want to leave with a reputation of being against sleep trackers. I think they are a great opportunity for people to get excited about and learn about their sleep and try to improve it. We have a lot to learn about what people want from their data and how we can use that data to improve people’s sleep.

As providers, we can engage with our patients – sleep is an automatic process, but improving sleep takes some effort. Buying a device is not going to automatically make you sleep better. It takes work to establish a better sleep pattern; it may require some cognitive-behavioral therapy or treating a sleep disorder. That takes some work.

Dr. Baron reported no conflicts of interest.A version of this article first appeared on Medscape.com.

The pandemic not only disrupted sleep but may have also triggered an uptick in the use of wearable tech. Sleep tracking was featured at the Cardiovascular Health Tech virtual conference 2022, sponsored by the Institute of Electrical and Electronics Engineers Engineering in Medicine & Biology Society technical committee on Cardiopulmonary Systems and Physiology-Based Engineering.

This news organization interviewed presenter Kelly Glazer Baron, PhD, MPH, DBSM, an associate professor at the University of Utah, Salt Lake City, and a clinical psychologist specializing in behavioral sleep medicine.

The interview has been edited for length and clarity.
 

Question: Are consumer sleep trackers mainly divided into “nearables” – things that you put at the side of the bed or under the pillow – vs. wearables?

Dr. Baron: There are so many different devices these days. There are things that you put under your mattress or pillow; there are bedside recording devices; then there are headbands, rings, wrist-worn, all kinds of things.

Q: At the conference, Philip de Chazal, PhD, (University of Sydney) described the evidence on sleep tracking smartphone apps as woeful. Would you agree with that?

A: Yes. I would agree if you’re looking at how accurate they are at recording sleep, particularly compared with what we would define as the gold standard, which is a sleep study wherein you have electrodes on the scalp and you’re measuring the electrical activity directly.

Devices that go under the pillow are extremely poor at deciphering sleep from wake time, which is really the main goal. They are best at detecting when you get into the bed and when you get out. But even then, there isn’t good evidence that they do that accurately when there are two people in the bed.

Overall, they may give you a general gist of what’s happening in terms of time in and out of bed, but we’re doubtful on their recording ability to tell sleep from wake time.
 

Q: Are the wrist-worn devices better for sleep tracking?

A: They’re getting better. We’ve used wrist activity monitors in research for years. They use an accelerometer to measure movement, and then an algorithm determines whether an interval of time is called sleep or wake.

Recently, they’ve incorporated more sensors, such as heart rate, and they can more accurately decipher rapid eye movement (REM) sleep from non-REM. They’re still not as good as doing a full sleep study. But they’re getting closer.
 

Q: If asked how you slept, most of us think we can answer without needing to look at a smartphone, but maybe not. Can you explain “paradoxical insomnia”?

A: You can’t really know if you’re sleeping because if you know you’re asleep, then you can’t be asleep because it’s a state of unconsciousness. How people decide whether they had a good night’s sleep probably depends on a lot of things about how they feel when they wake up in the morning or if they remember being up in the night.

Quality of sleep is not really something that people can directly ascertain. There is a selection of people who feel awake all night but they actually are sleeping. They feel that their sleep quality is poor: They’re suffering; they have insomnia, but from the objective data, they are sleeping fine.
 

Q: Is this related to non-REM stage 1 sleep, when you may not be aware that you’re asleep?

A: No. I’m talking about people who come into the sleep lab for an overnight study and get hooked up. And in the morning, they’ll tell the tech I was awake all night, but the tech will see that their sleep was just fine.

There is a disconnect between how people perceive their sleep and how they actually sleep. For most people it’s impossible to be completely accurate to know how much you’re sleeping. Then there are some people who perceive it very differently.

Sleep trackers don’t have the level of detail of sleep studies that use scalp electrodes. When we get into the details of sleep measurement, we’re measuring 30-second epochs (sampling periods), where we look at broad measures of electrical activity. There is even more detail there that can be pulled out using other techniques, such as analyzing the spectrum of the EEG. For example, some studies have found a beta frequency in the EEG of people with insomnia, so even though they are sleeping, they often feel awake.

Basically, the subjective experience of sleep somewhat overlaps with the objective recording of what’s happening on a sleep study, but not completely.
 

Q: You said that first thing in the morning might not be the best time to assess your sleep – if you wake up groggy and are already thinking, “The day is shot.”

A: In general, people really feel worst in the morning. Their circadian drive is low, especially if they’re a little sleep deprived. You shouldn’t judge the day on the first hour after waking – most people are pretty cognitively impaired. I tell people they need some boot-up time.

You feel differently as the day goes on and even at different points of the day. There’s a lull in the early afternoon because of your circadian dip and then we get a second wind in the evening. How you feel isn’t one flat line; it’s really a rhythm throughout the day
 

Q: Would you say that consumer sleep trackers are okay for individuals to use to see a pattern but are maybe not accurate enough to use more globally in research?

A: I think there is a huge opportunity to understand sleep at a population level. For example, if there’s been a hurricane or an earthquake or Superbowl Sunday, companies have an opportunity to look at the impact – say, daylight saving time and how it affects sleep across different countries, or men vs. women, or different age groups.

There was a paper about sleep among hospital workers in Wuhan during the outbreak of the pandemic. That was a creative use of wearable devices to look at sleep in a large population.

Now, of course, the devices are not given out randomly; the people who buy them are probably a little bit healthier, maybe a little bit younger – that sort of thing. It is a biased sample.
 

Q: As you note, mobile health trackers tend to be used by the “worried well.” Can you tell us about your paper that introduced the term “orthosomnia,” or “a perfectionistic quest for the ideal sleep in order to optimize daytime function”?

A: As these devices came out, more people were coming into the clinic and shoving their data in front of us saying, “I don’t feel well, and I don’t sleep 7 hours.” They were focused on this specific number. Back when we wrote this paper, the devices were primarily movement based (now the devices are a bit more accurate). Some would say, “My sleep is light, and it’s not deep.” We’d do a sleep study that showed that they have deep sleep, but they would still believe their device even though the device really wasn’t able to classify sleep accurately.

We even found people making their sleep worse because of the device. For example, trying to get the number higher by spending more time lying in bed trying to sleep which is the opposite of what you want someone with insomnia to do. These people held the data so tight and really felt that it characterized their experience, even though we sleep medicine practitioners didn’t find it very accurate and felt that it was somewhat unhelpful to their treatment.
 

Q: What advice would you give the harried primary care physician presented with a patient’s hypnogram or sleep pattern?

A: As someone once pointed out to me, it’s a conversation opener about their sleep. Did they buy the device because they’re worried about their sleep? It’s unlikely that you can glean anything clinically useful from the data.

I briefly look at it to see the duration of their sleep, the regularity in their sleep pattern – the pattern of awakenings during the night might suggest that they have some insomnia. But it doesn’t take the place of clinical assessment for conditions like sleep apnea: Are they snoring? Are they unrefreshed?

I had a patient in the orthosomnia study who was given a sleep tracker by a family member. He brought the data to his doctor who ordered a sleep study that found he had sleep apnea. He would say, “The device diagnosed my sleep apnea.” But that wasn’t actually the case; it just opened the conversation and the clinician said, “Well, let’s order a sleep study.”
 

Q: The device told him he wasn’t getting much sleep and then the sleep study told him it was apnea.

A: Right. It’s impossible to pick up sleep apnea. Some of the latest devices have some oximetry reading but it is not a clinically validated oximetry that could diagnose sleep apnea.

When these first came out I thought I’d get more referrals. So far, I haven’t had a single person come in and ask if they have sleep apnea. If you have a patient saying, “Hey, I’m worried about my oxygen level and here’s my data,” then the clinician should consider whether they need a sleep study for sleep apnea.
 

Q: You did a survey that suggests that clinicians are less keen on these devices than consumers. Conor Heneghan of Fitbit/Google also mentioned a study using the Fitbit Charge and a SleepLife portal. The patients were very engaged but only one physician (out of 49) logged into the portal to look at the data.

A: Our survey of sleep professionals (which we need to publish) showed that they were wary of the data. They found it frustrating in some ways because it took time out of the clinical encounter.

Some of them said that parents are putting trackers on their children and then catastrophizing their children’s sleep.
 

Q: Is there such a thing as an ideal hypnogram or does it vary by individual?

A: I would say that it depends on a lot of things. If you think about a hypnogram from a sleep study, the patient is not sleeping in their home environment, and it’s only one night. There’s a range of what would be considered normal, and it’s related to your sex and your age.

One night is not going to be sufficient to characterize your percentage in this or that sleep stage. Our patients come in saying, “I’m not getting enough REM.” But there isn’t a sleep disorder called lack of REM; there’s no treatment for that. It’s probably pretty normal for them or maybe they’re taking medications that suppress their REM, such as antidepressants.

The tech world is very interested to sense REM properly and to display it. But on the treatment side of things, there’s not much that we do with that data. We’re more interested in the consolidation of their sleep, the duration of their sleep, breathing-related sleep disorders, those sorts of things.
 

Q: Is there any reason to be concerned about the amount of REM sleep in terms of outcomes? We know that poor sleep can lead to bad cardiovascular outcomes, but has any of that correlated to sleep stage?

A: There are studies where they’ve experimentally deprived people of certain stages of sleep, but they’re not very useful in the real world. We’re looking at sleep holistically: Do you have a good sleep pattern? Any breathing-related sleep disorders? Insomnia? We don’t treat sleep by the stage.

Q: Any concern that people who are focused on a device may be ignoring the basic tenets of good sleep hygiene?

A: If people are doing things that are obviously bad for their sleep, like working too late, not exercising enough, sleeping in on weekends to compensate for being up late during the week, or probably the biggest thing contributing to insomnia – stress. A device itself won’t fix those things but it could show you the evidence.

If somebody really has a sleep disorder, then sleep hygiene alone is probably not going to be enough. They’re going to need to engage in a more extensive program to improve their sleep, such as cognitive-behavioral therapy for insomnia.
 

Q: Is there anything else you want to mention?

A: I don’t want to leave with a reputation of being against sleep trackers. I think they are a great opportunity for people to get excited about and learn about their sleep and try to improve it. We have a lot to learn about what people want from their data and how we can use that data to improve people’s sleep.

As providers, we can engage with our patients – sleep is an automatic process, but improving sleep takes some effort. Buying a device is not going to automatically make you sleep better. It takes work to establish a better sleep pattern; it may require some cognitive-behavioral therapy or treating a sleep disorder. That takes some work.

Dr. Baron reported no conflicts of interest.A version of this article first appeared on Medscape.com.

Before working to create standards for electronic prior authorization, the entire process itself needs some work.

That was the message from groups representing physicians, medical practices, and hospitals in response to a request for input from the Office of the National Coordinator for Health Information Technology (ONC). In January, ONC requested public feedback on how making the process for insurer approvals digital can “ease the burden of prior authorization tasks on patients, providers, and payers.”

According to a study conducted by America’s Health Insurance Plans, 71% of providers who implemented electronic prior authorization experienced “faster time to patient care.” The organization, which represents many of the nation’s health insurers, also reported that electronic prior authorization reduced the time it took to receive a decision by a health plan by 69%.

In its response to ONC, the American Association of Family Physicians (AAFP) called out prior authorization as a “leading cause of physician burden” and wrote that the organization is “strongly supportive of efforts to reform and streamline the prior authorization process.”

AAFP, which represents 127,600 family physicians, residents, and students, cited in its comments an AMA survey in which 88% of physicians said that prior authorization “generates high or extremely high administrative burden” for their practices. Practices are responsible for an average of 41 prior authorizations per physician each week, which can take almost 2 days of a physician’s time each week, according to the AAFP.

Delayed care, increased confusion, reduced treatment adherence, and even discontinuation of treatment are some of the harms prior authorization causes patients, wrote AAFP board chair Ada D. Stewart, MD.

Electronic prior authorization is “just one step in addressing the flaws of utilization management practices, and comprehensive reform is needed to reduce the volume of prior authorizations and ensure patients’ timely access to care,” wrote Dr. Stewart.
 

AHA: Most common prior auth means are phones, fax

The American Hospital Association (AHA) highlighted the variety of prior authorization requests from different payers, writing, “While some plans accept electronic means, the most common method remains using fax machines and contacting call centers, with regular hold times of 20 to 30 minutes.”

The AHA’s Senior Vice President Ashley Thompson wrote that the various prior authorization processes required by payers take up staff time and increase the chance of data entry errors.

To fix this, the AHA calls for an “end-to-end automated prior authorization process that integrates with clinicians’ EHR workflow.” According to the AHA, this approach can help physicians have access to the required prior authorization information during treatment planning.

In response to the federal agency’s question about the functional capabilities for certified health IT modules to facilitate electronic prior authorization, the AAFP wrote that the standards should include communicating to providers the expected timeline from a payer on a response, the ability to access payers’ reasoning for denials, and the creation of a process for appealing decisions.

The ONC also asked for input on the use of three fast health care interoperability resources (FHIR)–based Da Vinci implementation guides in electronic prior authorization.

Developed by the Da Vinci Project in coordination with the HL7 Clinical Decision Support Workgroup, the FHIR-based implementation guides create a mechanism for reducing the burden on provider organizations and simplifying processes by establishing electronic versions of administrative and clinical requirements that are a part of providers’ workflow.

In its response, the AHA requested that prior authorization solutions “be fully developed and tested prior to wide scale industry rollout.”

The AAFP largely agreed with the AHA in its response, writing, “Only standards and [implementation guides] that have been proven effective and adoptable in real world testing should be candidates for mandatory certification and utilization, including the Da Vinci standards.”

The Medical Group Management Association (MGMA), which represents more than 60,000 medical practice administrators, executives, and leaders, supports the idea that electronic prior authorization “has the potential to decrease administrative burden through automation but only if implemented properly.”

In its comments, the MGMA called for broader reform of prior authorization. One way to accomplish that goal is by aligning electronic prior authorization standards “with payment and quality reporting programs, as well as care delivery models, to minimize burden and overhead costs.”

A version of this article first appeared on Medscape.com.

Before working to create standards for electronic prior authorization, the entire process itself needs some work.

That was the message from groups representing physicians, medical practices, and hospitals in response to a request for input from the Office of the National Coordinator for Health Information Technology (ONC). In January, ONC requested public feedback on how making the process for insurer approvals digital can “ease the burden of prior authorization tasks on patients, providers, and payers.”

According to a study conducted by America’s Health Insurance Plans, 71% of providers who implemented electronic prior authorization experienced “faster time to patient care.” The organization, which represents many of the nation’s health insurers, also reported that electronic prior authorization reduced the time it took to receive a decision by a health plan by 69%.

In its response to ONC, the American Association of Family Physicians (AAFP) called out prior authorization as a “leading cause of physician burden” and wrote that the organization is “strongly supportive of efforts to reform and streamline the prior authorization process.”

AAFP, which represents 127,600 family physicians, residents, and students, cited in its comments an AMA survey in which 88% of physicians said that prior authorization “generates high or extremely high administrative burden” for their practices. Practices are responsible for an average of 41 prior authorizations per physician each week, which can take almost 2 days of a physician’s time each week, according to the AAFP.

Delayed care, increased confusion, reduced treatment adherence, and even discontinuation of treatment are some of the harms prior authorization causes patients, wrote AAFP board chair Ada D. Stewart, MD.

Electronic prior authorization is “just one step in addressing the flaws of utilization management practices, and comprehensive reform is needed to reduce the volume of prior authorizations and ensure patients’ timely access to care,” wrote Dr. Stewart.
 

AHA: Most common prior auth means are phones, fax

The American Hospital Association (AHA) highlighted the variety of prior authorization requests from different payers, writing, “While some plans accept electronic means, the most common method remains using fax machines and contacting call centers, with regular hold times of 20 to 30 minutes.”

The AHA’s Senior Vice President Ashley Thompson wrote that the various prior authorization processes required by payers take up staff time and increase the chance of data entry errors.

To fix this, the AHA calls for an “end-to-end automated prior authorization process that integrates with clinicians’ EHR workflow.” According to the AHA, this approach can help physicians have access to the required prior authorization information during treatment planning.

In response to the federal agency’s question about the functional capabilities for certified health IT modules to facilitate electronic prior authorization, the AAFP wrote that the standards should include communicating to providers the expected timeline from a payer on a response, the ability to access payers’ reasoning for denials, and the creation of a process for appealing decisions.

The ONC also asked for input on the use of three fast health care interoperability resources (FHIR)–based Da Vinci implementation guides in electronic prior authorization.

Developed by the Da Vinci Project in coordination with the HL7 Clinical Decision Support Workgroup, the FHIR-based implementation guides create a mechanism for reducing the burden on provider organizations and simplifying processes by establishing electronic versions of administrative and clinical requirements that are a part of providers’ workflow.

In its response, the AHA requested that prior authorization solutions “be fully developed and tested prior to wide scale industry rollout.”

The AAFP largely agreed with the AHA in its response, writing, “Only standards and [implementation guides] that have been proven effective and adoptable in real world testing should be candidates for mandatory certification and utilization, including the Da Vinci standards.”

The Medical Group Management Association (MGMA), which represents more than 60,000 medical practice administrators, executives, and leaders, supports the idea that electronic prior authorization “has the potential to decrease administrative burden through automation but only if implemented properly.”

In its comments, the MGMA called for broader reform of prior authorization. One way to accomplish that goal is by aligning electronic prior authorization standards “with payment and quality reporting programs, as well as care delivery models, to minimize burden and overhead costs.”

A version of this article first appeared on Medscape.com.

Before working to create standards for electronic prior authorization, the entire process itself needs some work.

That was the message from groups representing physicians, medical practices, and hospitals in response to a request for input from the Office of the National Coordinator for Health Information Technology (ONC). In January, ONC requested public feedback on how making the process for insurer approvals digital can “ease the burden of prior authorization tasks on patients, providers, and payers.”

According to a study conducted by America’s Health Insurance Plans, 71% of providers who implemented electronic prior authorization experienced “faster time to patient care.” The organization, which represents many of the nation’s health insurers, also reported that electronic prior authorization reduced the time it took to receive a decision by a health plan by 69%.

In its response to ONC, the American Association of Family Physicians (AAFP) called out prior authorization as a “leading cause of physician burden” and wrote that the organization is “strongly supportive of efforts to reform and streamline the prior authorization process.”

AAFP, which represents 127,600 family physicians, residents, and students, cited in its comments an AMA survey in which 88% of physicians said that prior authorization “generates high or extremely high administrative burden” for their practices. Practices are responsible for an average of 41 prior authorizations per physician each week, which can take almost 2 days of a physician’s time each week, according to the AAFP.

Delayed care, increased confusion, reduced treatment adherence, and even discontinuation of treatment are some of the harms prior authorization causes patients, wrote AAFP board chair Ada D. Stewart, MD.

Electronic prior authorization is “just one step in addressing the flaws of utilization management practices, and comprehensive reform is needed to reduce the volume of prior authorizations and ensure patients’ timely access to care,” wrote Dr. Stewart.
 

AHA: Most common prior auth means are phones, fax

The American Hospital Association (AHA) highlighted the variety of prior authorization requests from different payers, writing, “While some plans accept electronic means, the most common method remains using fax machines and contacting call centers, with regular hold times of 20 to 30 minutes.”

The AHA’s Senior Vice President Ashley Thompson wrote that the various prior authorization processes required by payers take up staff time and increase the chance of data entry errors.

To fix this, the AHA calls for an “end-to-end automated prior authorization process that integrates with clinicians’ EHR workflow.” According to the AHA, this approach can help physicians have access to the required prior authorization information during treatment planning.

In response to the federal agency’s question about the functional capabilities for certified health IT modules to facilitate electronic prior authorization, the AAFP wrote that the standards should include communicating to providers the expected timeline from a payer on a response, the ability to access payers’ reasoning for denials, and the creation of a process for appealing decisions.

The ONC also asked for input on the use of three fast health care interoperability resources (FHIR)–based Da Vinci implementation guides in electronic prior authorization.

Developed by the Da Vinci Project in coordination with the HL7 Clinical Decision Support Workgroup, the FHIR-based implementation guides create a mechanism for reducing the burden on provider organizations and simplifying processes by establishing electronic versions of administrative and clinical requirements that are a part of providers’ workflow.

In its response, the AHA requested that prior authorization solutions “be fully developed and tested prior to wide scale industry rollout.”

The AAFP largely agreed with the AHA in its response, writing, “Only standards and [implementation guides] that have been proven effective and adoptable in real world testing should be candidates for mandatory certification and utilization, including the Da Vinci standards.”

The Medical Group Management Association (MGMA), which represents more than 60,000 medical practice administrators, executives, and leaders, supports the idea that electronic prior authorization “has the potential to decrease administrative burden through automation but only if implemented properly.”

In its comments, the MGMA called for broader reform of prior authorization. One way to accomplish that goal is by aligning electronic prior authorization standards “with payment and quality reporting programs, as well as care delivery models, to minimize burden and overhead costs.”

A version of this article first appeared on Medscape.com.

A controlled study of sleep-deprived young adults has provided the first causal evidence linking the lack of sleep to abdominal obesity and harmful visceral, or “belly” fat. In what the researchers claim is the first-ever study evaluating the relationship between sleep restriction and body fat distribution, they’ve reported the novel finding that the expansion of abdominal adipose tissue, and especially visceral fat, occurred as a function of shortened sleep.

Naima Covassin, PhD, a researcher in cardiovascular medicine at Mayo Clinic in Rochester, Minn., led the randomized, controlled study of 12 healthy, nonobese people randomized to controlled sleep restriction – 2 weeks of 4 hours of sleep a night – or controlled sleep of 9 hours a night, followed by a 3-day recovery period. The study was conducted in the hospital, monitored participants’ caloric intake, and used accelerometry to monitor energy expense. Participants ranged in age from 19 to 39 years.

“What we found was that at the end of 2 weeks these people put on just about a pound, 0.5 kg, of extra weight, which was significant but still very modest,” senior author Virend K. Somers, MD, PhD, said in an interview. “The average person who sleeps 4 hours a night thinks they’re doing OK if they only put on a pound.” Dr. Somers is the Alice Sheets Marriott Professor in Cardiovascular Medicine at Mayo Clinic.

“The problem is,” he said, “that when you do a more specific analysis you find that actually with the 1 pound the significant increase of the fat is in the belly area, particularly inside the belly.”

The study found that the patients on curtailed sleep ate on average an additional 308 calories a day more than their controlled sleep counterparts (95% confidence interval, 59.2-556.8 kcal/day; P = .015), and while that translated into a 0.5-kg weight gain (95% CI, 0.1-0.8 kg; P = .008), it also led to a 7.8-cm² increase visceral adipose tissue (VAT) (95% CI, 0.3-15.3 cm²; P = .042), representing an increase of around 11%. The study used CT on day 1 and day 18 (1 day after the 3-day recovery period) to evaluate the distribution of abdominal fat.

VAT findings post recovery

After the recovery period, however, the study found that VAT in the sleep-curtailed patients kept rising, yet body weight and subcutaneous fat dropped, and the increase in total abdominal fat flattened. “They slept a lot, they ate fewer calories and their weight came down, but, very importantly, their belly fat went up even further,” Dr. Somers said. On average, it increased another 3.125 cm² by day 21.

The findings raised a number of questions that need further exploration, Dr. Somers said. “There’s some biochemical message in the body that’s continuing to send fat to the visceral compartment,” he said. “What we don’t know is whether repetitive episodes of inadequate sleep actually accumulate over the years to give people a preponderance of belly fat.”

The study also showed that the traditional parameters used for evaluating cardiovascular risk are not enough, Dr. Somers said. “If we just did body weight, body mass index, and overall body fat percentage, we’d completely miss this,” he said.

Future investigations should focus on two points, he said: identifying the mechanisms that cause VAT accumulation with less sleep, and whether extending sleep can reverse the process.

“The big worry is obviously the heart,” Dr. Somers said. “Remember, these are not sick people. These are young healthy people who are doing the wrong thing with their body fat; they’re sending the fat to the completely wrong place.”

In an invited editorial, endocrinologist Harold Bays, MD, wrote that the study confirmed the need for evaluating sleep disorders as a potential cause of accumulated VAT. Dr. Bays of the University of Louisville (Ky.) is medical director and president of the Louisville Metabolic and Atherosclerosis Research Center.

“The biggest misconception of many clinicians, and some cardiologists, is that obesity is not a disease,” Dr. Bays said in an interview. “Even when some clinicians believe obesity is a disease, they believe its pathogenic potential is limited to visceral fat.” He noted that subcutaneous fat can lead to accumulation of VAT and epicardial fat, as well as fatty infiltration of the liver and other vital organs, resulting in increased epicardial adipose tissue and indirect adverse effects on the heart.

“Thus, even if disruption of sleep does not increase body weight, if disruption of sleep results in fat dysfunction – “sick fat” or adiposopathy – then this may result in increased CVD risk factors and unhealthy body composition, including an increase in visceral fat,” Dr. Bays said.

The study received funding from the National Institutes of Health. Dr. Somers disclosed relationships with Baker Tilly, Jazz Pharmaceuticals, Bayer, Sleep Number and Respicardia. Coauthors had no disclosures. Dr. Bays is medical director of Your Body Goal and chief science officer of the Obesity Medical Association.

A controlled study of sleep-deprived young adults has provided the first causal evidence linking the lack of sleep to abdominal obesity and harmful visceral, or “belly” fat. In what the researchers claim is the first-ever study evaluating the relationship between sleep restriction and body fat distribution, they’ve reported the novel finding that the expansion of abdominal adipose tissue, and especially visceral fat, occurred as a function of shortened sleep.

Naima Covassin, PhD, a researcher in cardiovascular medicine at Mayo Clinic in Rochester, Minn., led the randomized, controlled study of 12 healthy, nonobese people randomized to controlled sleep restriction – 2 weeks of 4 hours of sleep a night – or controlled sleep of 9 hours a night, followed by a 3-day recovery period. The study was conducted in the hospital, monitored participants’ caloric intake, and used accelerometry to monitor energy expense. Participants ranged in age from 19 to 39 years.

“What we found was that at the end of 2 weeks these people put on just about a pound, 0.5 kg, of extra weight, which was significant but still very modest,” senior author Virend K. Somers, MD, PhD, said in an interview. “The average person who sleeps 4 hours a night thinks they’re doing OK if they only put on a pound.” Dr. Somers is the Alice Sheets Marriott Professor in Cardiovascular Medicine at Mayo Clinic.

“The problem is,” he said, “that when you do a more specific analysis you find that actually with the 1 pound the significant increase of the fat is in the belly area, particularly inside the belly.”

The study found that the patients on curtailed sleep ate on average an additional 308 calories a day more than their controlled sleep counterparts (95% confidence interval, 59.2-556.8 kcal/day; P = .015), and while that translated into a 0.5-kg weight gain (95% CI, 0.1-0.8 kg; P = .008), it also led to a 7.8-cm² increase visceral adipose tissue (VAT) (95% CI, 0.3-15.3 cm²; P = .042), representing an increase of around 11%. The study used CT on day 1 and day 18 (1 day after the 3-day recovery period) to evaluate the distribution of abdominal fat.

VAT findings post recovery

After the recovery period, however, the study found that VAT in the sleep-curtailed patients kept rising, yet body weight and subcutaneous fat dropped, and the increase in total abdominal fat flattened. “They slept a lot, they ate fewer calories and their weight came down, but, very importantly, their belly fat went up even further,” Dr. Somers said. On average, it increased another 3.125 cm² by day 21.

The findings raised a number of questions that need further exploration, Dr. Somers said. “There’s some biochemical message in the body that’s continuing to send fat to the visceral compartment,” he said. “What we don’t know is whether repetitive episodes of inadequate sleep actually accumulate over the years to give people a preponderance of belly fat.”

The study also showed that the traditional parameters used for evaluating cardiovascular risk are not enough, Dr. Somers said. “If we just did body weight, body mass index, and overall body fat percentage, we’d completely miss this,” he said.

Future investigations should focus on two points, he said: identifying the mechanisms that cause VAT accumulation with less sleep, and whether extending sleep can reverse the process.

“The big worry is obviously the heart,” Dr. Somers said. “Remember, these are not sick people. These are young healthy people who are doing the wrong thing with their body fat; they’re sending the fat to the completely wrong place.”

In an invited editorial, endocrinologist Harold Bays, MD, wrote that the study confirmed the need for evaluating sleep disorders as a potential cause of accumulated VAT. Dr. Bays of the University of Louisville (Ky.) is medical director and president of the Louisville Metabolic and Atherosclerosis Research Center.

“The biggest misconception of many clinicians, and some cardiologists, is that obesity is not a disease,” Dr. Bays said in an interview. “Even when some clinicians believe obesity is a disease, they believe its pathogenic potential is limited to visceral fat.” He noted that subcutaneous fat can lead to accumulation of VAT and epicardial fat, as well as fatty infiltration of the liver and other vital organs, resulting in increased epicardial adipose tissue and indirect adverse effects on the heart.

“Thus, even if disruption of sleep does not increase body weight, if disruption of sleep results in fat dysfunction – “sick fat” or adiposopathy – then this may result in increased CVD risk factors and unhealthy body composition, including an increase in visceral fat,” Dr. Bays said.

The study received funding from the National Institutes of Health. Dr. Somers disclosed relationships with Baker Tilly, Jazz Pharmaceuticals, Bayer, Sleep Number and Respicardia. Coauthors had no disclosures. Dr. Bays is medical director of Your Body Goal and chief science officer of the Obesity Medical Association.

A controlled study of sleep-deprived young adults has provided the first causal evidence linking the lack of sleep to abdominal obesity and harmful visceral, or “belly” fat. In what the researchers claim is the first-ever study evaluating the relationship between sleep restriction and body fat distribution, they’ve reported the novel finding that the expansion of abdominal adipose tissue, and especially visceral fat, occurred as a function of shortened sleep.

Naima Covassin, PhD, a researcher in cardiovascular medicine at Mayo Clinic in Rochester, Minn., led the randomized, controlled study of 12 healthy, nonobese people randomized to controlled sleep restriction – 2 weeks of 4 hours of sleep a night – or controlled sleep of 9 hours a night, followed by a 3-day recovery period. The study was conducted in the hospital, monitored participants’ caloric intake, and used accelerometry to monitor energy expense. Participants ranged in age from 19 to 39 years.

“What we found was that at the end of 2 weeks these people put on just about a pound, 0.5 kg, of extra weight, which was significant but still very modest,” senior author Virend K. Somers, MD, PhD, said in an interview. “The average person who sleeps 4 hours a night thinks they’re doing OK if they only put on a pound.” Dr. Somers is the Alice Sheets Marriott Professor in Cardiovascular Medicine at Mayo Clinic.

“The problem is,” he said, “that when you do a more specific analysis you find that actually with the 1 pound the significant increase of the fat is in the belly area, particularly inside the belly.”

The study found that the patients on curtailed sleep ate on average an additional 308 calories a day more than their controlled sleep counterparts (95% confidence interval, 59.2-556.8 kcal/day; P = .015), and while that translated into a 0.5-kg weight gain (95% CI, 0.1-0.8 kg; P = .008), it also led to a 7.8-cm² increase visceral adipose tissue (VAT) (95% CI, 0.3-15.3 cm²; P = .042), representing an increase of around 11%. The study used CT on day 1 and day 18 (1 day after the 3-day recovery period) to evaluate the distribution of abdominal fat.

VAT findings post recovery

After the recovery period, however, the study found that VAT in the sleep-curtailed patients kept rising, yet body weight and subcutaneous fat dropped, and the increase in total abdominal fat flattened. “They slept a lot, they ate fewer calories and their weight came down, but, very importantly, their belly fat went up even further,” Dr. Somers said. On average, it increased another 3.125 cm² by day 21.

The findings raised a number of questions that need further exploration, Dr. Somers said. “There’s some biochemical message in the body that’s continuing to send fat to the visceral compartment,” he said. “What we don’t know is whether repetitive episodes of inadequate sleep actually accumulate over the years to give people a preponderance of belly fat.”

The study also showed that the traditional parameters used for evaluating cardiovascular risk are not enough, Dr. Somers said. “If we just did body weight, body mass index, and overall body fat percentage, we’d completely miss this,” he said.

Future investigations should focus on two points, he said: identifying the mechanisms that cause VAT accumulation with less sleep, and whether extending sleep can reverse the process.

“The big worry is obviously the heart,” Dr. Somers said. “Remember, these are not sick people. These are young healthy people who are doing the wrong thing with their body fat; they’re sending the fat to the completely wrong place.”

In an invited editorial, endocrinologist Harold Bays, MD, wrote that the study confirmed the need for evaluating sleep disorders as a potential cause of accumulated VAT. Dr. Bays of the University of Louisville (Ky.) is medical director and president of the Louisville Metabolic and Atherosclerosis Research Center.

“The biggest misconception of many clinicians, and some cardiologists, is that obesity is not a disease,” Dr. Bays said in an interview. “Even when some clinicians believe obesity is a disease, they believe its pathogenic potential is limited to visceral fat.” He noted that subcutaneous fat can lead to accumulation of VAT and epicardial fat, as well as fatty infiltration of the liver and other vital organs, resulting in increased epicardial adipose tissue and indirect adverse effects on the heart.

“Thus, even if disruption of sleep does not increase body weight, if disruption of sleep results in fat dysfunction – “sick fat” or adiposopathy – then this may result in increased CVD risk factors and unhealthy body composition, including an increase in visceral fat,” Dr. Bays said.

The study received funding from the National Institutes of Health. Dr. Somers disclosed relationships with Baker Tilly, Jazz Pharmaceuticals, Bayer, Sleep Number and Respicardia. Coauthors had no disclosures. Dr. Bays is medical director of Your Body Goal and chief science officer of the Obesity Medical Association.

 Further support for the benefits of regular exercise in reducing severe COVID-19 outcomes has come from a large study, the first to directly measure physical activity in its participants.

Researchers identified 65,361 members of a South African private health plan who had a COVID-19 diagnosis from March 2020 to June 2021 and matched them with physical activity data during the 2 years prior to the country’s March 2020 lockdown captured by smart devices, and clocked gym attendance and mass event participation in a voluntary healthy lifestyle behavior program linked to the insurer.

UberImages/iStock/Getty Images

In all, 20.4% of participants had engaged in low levels of at least moderate-intensity physical activity per week (0-59 minutes), 34.5% in moderate levels (60-149 minutes), and 45.1% in high levels (150 minutes or more).

Overall, 11.1% were hospitalized as a result of COVID-19, 2.4% were admitted to the ICU, 1.3% required a ventilator, and 1.6% died.

As reported in the British Journal of Sports Medicine, analyses adjusted for demographic and other risk factors showed that, with COVID-19 infection, people with high versus low physical activity had a 34% lower risk for hospitalization (risk ratio, 0.66; 95% confidence interval, 0.63-0.70), a 41% lower risk for ICU admission (RR, 0.59; 95% CI, 0.52-0.66), a 45% lower risk of requiring ventilation (RR, 0.55; 95% CI, 0.47-0.64), and a 42% lower risk for death (RR, 0.58; 95% CI, 0.50-0.68).

Even moderate physical exercise, below the recommended guidelines of at least 150 minutes per week, was associated with several benefits, such as a 13% lower risk for hospitalization (RR, 0.87; 95% CI, 0.82-0.91), a 20% lower risk for ICU admission (RR, 0.80; 95% CI, 0.71-0.89), a 27% lower risk of requiring ventilation (RR, 0.73; 95% CI, 0.62-0.84), and a21% lower risk for death (RR, 0.79; 95% CI, 0.69-0.91).

“Should we come across further waves of this pandemic, our advice from a medical point of view should be to promote and facilitate exercise,” senior author Jon Patricios, MD, Wits Sport and Health, University of the Witwatersrand, Johannesburg, South Africa, said in an interview. “The likelihood is that exercise and vaccination are going to be the two most significant interventions in terms of helping to offload the health care system rather than face the catastrophic events endured a year or so ago.”

The study showed that males are at greater risk than females for severe COVID-19 outcomes, as were patients with essential hypertension, diabetes, and chronic renal disease.

It also suggests that the protective benefit of exercise extends to HIV-positive patients and those with rheumatoid arthritis, two groups previously not evaluated, the authors noted.

The results are comparable with previous reports of self-reported exercise and COVID-19 from the United States and South Korea, although the effect of even moderate exercise was more significant, possibly due to the use of direct measures of exercise rather than self-report, Dr. Patricios suggested.

Previous data suggest that regular physical activity may protect against many viral infections including influenza, rhinovirus, and the reactivation of latent herpes viruses, he noted. However, emerging evidence also points to significant decreases in physical activity during the pandemic.

“Regular physical activity should be a message that is strongly, strongly advocated for, particularly in less well-developed countries where we don’t have access or the resources to afford pharmacological interventions in many of these scenarios,” Dr. Patricios said. “It’s frustrating that the message is not driven strongly enough. It should be part of every government’s agenda.”

The cohort all being members of a medical insurance plan could imply some selection bias based on affordability and limit generalizability of the results, the authors noted. Other limitations include a lack of data on sociodemographic criteria such as education, income, and race, as well as behavioral risk factors such as smoking and diet.

Dr. Patricios and one coauthor are editors of the British Journal of Sports Medicine. Several coauthors are employees of Discovery Health, Johannesburg.

A version of this article first appeared on Medscape.com.

 Further support for the benefits of regular exercise in reducing severe COVID-19 outcomes has come from a large study, the first to directly measure physical activity in its participants.

Researchers identified 65,361 members of a South African private health plan who had a COVID-19 diagnosis from March 2020 to June 2021 and matched them with physical activity data during the 2 years prior to the country’s March 2020 lockdown captured by smart devices, and clocked gym attendance and mass event participation in a voluntary healthy lifestyle behavior program linked to the insurer.

UberImages/iStock/Getty Images

In all, 20.4% of participants had engaged in low levels of at least moderate-intensity physical activity per week (0-59 minutes), 34.5% in moderate levels (60-149 minutes), and 45.1% in high levels (150 minutes or more).

Overall, 11.1% were hospitalized as a result of COVID-19, 2.4% were admitted to the ICU, 1.3% required a ventilator, and 1.6% died.

As reported in the British Journal of Sports Medicine, analyses adjusted for demographic and other risk factors showed that, with COVID-19 infection, people with high versus low physical activity had a 34% lower risk for hospitalization (risk ratio, 0.66; 95% confidence interval, 0.63-0.70), a 41% lower risk for ICU admission (RR, 0.59; 95% CI, 0.52-0.66), a 45% lower risk of requiring ventilation (RR, 0.55; 95% CI, 0.47-0.64), and a 42% lower risk for death (RR, 0.58; 95% CI, 0.50-0.68).

Even moderate physical exercise, below the recommended guidelines of at least 150 minutes per week, was associated with several benefits, such as a 13% lower risk for hospitalization (RR, 0.87; 95% CI, 0.82-0.91), a 20% lower risk for ICU admission (RR, 0.80; 95% CI, 0.71-0.89), a 27% lower risk of requiring ventilation (RR, 0.73; 95% CI, 0.62-0.84), and a21% lower risk for death (RR, 0.79; 95% CI, 0.69-0.91).

“Should we come across further waves of this pandemic, our advice from a medical point of view should be to promote and facilitate exercise,” senior author Jon Patricios, MD, Wits Sport and Health, University of the Witwatersrand, Johannesburg, South Africa, said in an interview. “The likelihood is that exercise and vaccination are going to be the two most significant interventions in terms of helping to offload the health care system rather than face the catastrophic events endured a year or so ago.”

The study showed that males are at greater risk than females for severe COVID-19 outcomes, as were patients with essential hypertension, diabetes, and chronic renal disease.

It also suggests that the protective benefit of exercise extends to HIV-positive patients and those with rheumatoid arthritis, two groups previously not evaluated, the authors noted.

The results are comparable with previous reports of self-reported exercise and COVID-19 from the United States and South Korea, although the effect of even moderate exercise was more significant, possibly due to the use of direct measures of exercise rather than self-report, Dr. Patricios suggested.

Previous data suggest that regular physical activity may protect against many viral infections including influenza, rhinovirus, and the reactivation of latent herpes viruses, he noted. However, emerging evidence also points to significant decreases in physical activity during the pandemic.

“Regular physical activity should be a message that is strongly, strongly advocated for, particularly in less well-developed countries where we don’t have access or the resources to afford pharmacological interventions in many of these scenarios,” Dr. Patricios said. “It’s frustrating that the message is not driven strongly enough. It should be part of every government’s agenda.”

The cohort all being members of a medical insurance plan could imply some selection bias based on affordability and limit generalizability of the results, the authors noted. Other limitations include a lack of data on sociodemographic criteria such as education, income, and race, as well as behavioral risk factors such as smoking and diet.

Dr. Patricios and one coauthor are editors of the British Journal of Sports Medicine. Several coauthors are employees of Discovery Health, Johannesburg.

A version of this article first appeared on Medscape.com.

 Further support for the benefits of regular exercise in reducing severe COVID-19 outcomes has come from a large study, the first to directly measure physical activity in its participants.

Researchers identified 65,361 members of a South African private health plan who had a COVID-19 diagnosis from March 2020 to June 2021 and matched them with physical activity data during the 2 years prior to the country’s March 2020 lockdown captured by smart devices, and clocked gym attendance and mass event participation in a voluntary healthy lifestyle behavior program linked to the insurer.

UberImages/iStock/Getty Images

In all, 20.4% of participants had engaged in low levels of at least moderate-intensity physical activity per week (0-59 minutes), 34.5% in moderate levels (60-149 minutes), and 45.1% in high levels (150 minutes or more).

Overall, 11.1% were hospitalized as a result of COVID-19, 2.4% were admitted to the ICU, 1.3% required a ventilator, and 1.6% died.

As reported in the British Journal of Sports Medicine, analyses adjusted for demographic and other risk factors showed that, with COVID-19 infection, people with high versus low physical activity had a 34% lower risk for hospitalization (risk ratio, 0.66; 95% confidence interval, 0.63-0.70), a 41% lower risk for ICU admission (RR, 0.59; 95% CI, 0.52-0.66), a 45% lower risk of requiring ventilation (RR, 0.55; 95% CI, 0.47-0.64), and a 42% lower risk for death (RR, 0.58; 95% CI, 0.50-0.68).

Even moderate physical exercise, below the recommended guidelines of at least 150 minutes per week, was associated with several benefits, such as a 13% lower risk for hospitalization (RR, 0.87; 95% CI, 0.82-0.91), a 20% lower risk for ICU admission (RR, 0.80; 95% CI, 0.71-0.89), a 27% lower risk of requiring ventilation (RR, 0.73; 95% CI, 0.62-0.84), and a21% lower risk for death (RR, 0.79; 95% CI, 0.69-0.91).

“Should we come across further waves of this pandemic, our advice from a medical point of view should be to promote and facilitate exercise,” senior author Jon Patricios, MD, Wits Sport and Health, University of the Witwatersrand, Johannesburg, South Africa, said in an interview. “The likelihood is that exercise and vaccination are going to be the two most significant interventions in terms of helping to offload the health care system rather than face the catastrophic events endured a year or so ago.”

The study showed that males are at greater risk than females for severe COVID-19 outcomes, as were patients with essential hypertension, diabetes, and chronic renal disease.

It also suggests that the protective benefit of exercise extends to HIV-positive patients and those with rheumatoid arthritis, two groups previously not evaluated, the authors noted.

The results are comparable with previous reports of self-reported exercise and COVID-19 from the United States and South Korea, although the effect of even moderate exercise was more significant, possibly due to the use of direct measures of exercise rather than self-report, Dr. Patricios suggested.

Previous data suggest that regular physical activity may protect against many viral infections including influenza, rhinovirus, and the reactivation of latent herpes viruses, he noted. However, emerging evidence also points to significant decreases in physical activity during the pandemic.

“Regular physical activity should be a message that is strongly, strongly advocated for, particularly in less well-developed countries where we don’t have access or the resources to afford pharmacological interventions in many of these scenarios,” Dr. Patricios said. “It’s frustrating that the message is not driven strongly enough. It should be part of every government’s agenda.”

The cohort all being members of a medical insurance plan could imply some selection bias based on affordability and limit generalizability of the results, the authors noted. Other limitations include a lack of data on sociodemographic criteria such as education, income, and race, as well as behavioral risk factors such as smoking and diet.

Dr. Patricios and one coauthor are editors of the British Journal of Sports Medicine. Several coauthors are employees of Discovery Health, Johannesburg.

A version of this article first appeared on Medscape.com.

Five international cardiac surgery associations have banded together to address “substantive concerns” regarding the recently updated Valve Academic Research Consortium 3 (VARC-3) clinical endpoint definitions for aortic valve research.

The VARC-3 update was a multidisciplinary effort that included more than a dozen new or modified definitions for use in transcatheter and surgical aortic valve replacement (TAVR/SAVR) clinical trials, but drew criticism last year from surgeons that some of its definitions favor TAVR over surgery and that its writing committee had deep ties to industry and lacked diversity.

The new surgical associations’ position statement calls out five specific VARC-3 definitions – rehospitalization, valve thrombosis, bleeding, myocardial infarction (MI), and left bundle-branch block (LBBB).

The statement was jointly issued by the Society of Thoracic Surgeons (STS), the American Association for Thoracic Surgery, the European Association for Cardio-Thoracic Surgery, the Asian Society for Cardiovascular and Thoracic Surgery, and the Latin American Association of Cardiac and Endovascular Surgery.

It was copublished in Annals of Thoracic Surgery, the Journal of Thoracic and Cardiovascular Surgery, the European Journal of Cardio-Thoracic Surgery, and the Asian Cardiovascular and Thoracic Annals.

“We hope that this message can be seen, even if it’s somewhat difficult to hear sometimes, as positive constructive criticism compared to some of the dialogue that we’ve had on social media,” lead author Patrick O. Myers, MD, Lausanne (Switzerland) University Hospital, said in an interview. “It’s not criticizing people or the process but just trying to make these definitions better to ensure the good design of clinical trials.”

The president of each surgical association recommended representatives to help write the position statement, and once completed over Zoom meetings, it received formal endorsement from each association prior to publication, he said.

Reached for comment, VARC-3 lead author Philippe Généreux, MD, Gagnon Cardiovascular Institute, Morristown (N.J.) Medical Center, said, “I was pleasantly surprised that their comments were actually pretty minor and that most of these comments are really more a reflection, not of the validity of the definitions, but rather their applications.”

He noted that all the potential issues with the definitions were already discussed during the making of VARC-3 and resolved by consensus of more than 50 experts including the STS president at the time, Food and Drug Administration officials, and experts from the community.

“To be quite honest, I’m not sure they have consensus,” Dr. Généreux said. He added that the writing committee welcomes input from anyone, but “we’re not going to change the definitions to please eight individuals if we strongly believe by consensus of experts in the field that this is not the right thing to do.”

Rehospitalizations and valve thrombosis

The surgical associations praise VARC-3 for providing a standardized definition of bioprosthetic valve failure, but say they will not endorse the inclusion of rehospitalization as a component of the primary efficacy composite endpoint along with all-cause mortality, stroke, and quality of life.

They note that rehospitalizations outnumber mortality events, especially in short follow-up trials, and that the superiority of TAVR at 1 year in the PARTNER 3 trial of low-risk patients was driven primarily by more rehospitalizations in the surgical arm, but that this superiority was waning at 2 years of follow-up.

“The first thing we are calling for is that it shouldn’t be part of the primary composite outcome measure,” Dr. Myers said. But if it really has to be included, a 30-day blanking period for rehospitalization “would acknowledge that there’s a greater risk of rehospitalization during the acute phase of recovering from surgery.”

Dr. Généreux said that VARC-3 provides granular details for defining the different types of hospitalizations, but that a 30-day blanking period makes no sense. “If you close your eyes to anything within 30 days because you don’t like it, you’re missing the opportunity to improve your procedure, to improve your treatment, and to characterize precisely what happened with your patient.”

The new document lauds VARC-3’s focus on patient-centered and clinically relevant endpoints but questions the definition of valve thrombosis as a “clinically significant” thrombus. It points out that the incidence of valve thrombosis was significantly higher with TAVR versus SAVR in PARTNER 3 using the older VARC-2 definition, which did not require evidence of clinical sequelae (2.6% vs. 0.7%; P = .02). Under the new definition, however, half of the thrombi would be relabeled as “nothing there,” Dr. Myers said.

“As we’re doing this in younger and younger patients who will survive longer, there is a question of thrombus having an effect on the valve and leading to earlier structural valve deterioration,” he added. “All this is conjecture. We don’t have the data. So mainly what we’re advocating is that all thrombi should be reported.”
 

MIs, bleeding, and LBBB

The policy statement also criticizes VARC-3’s decision to define periprocedural (type 5) MI using a biomarker-only definition without need of clinical confirmation. Such definitions have been shown to have a very poor prognostic significance in surgical series compared with the Universal Definitions of Myocardial Infarction, Dr. Myers said.

“What’s interesting is that for thrombus and bleeding, they require clinical correlation, but on the perioperative MI they now use a definition that does not require clinical significance, meaning no ECG changes, no regional wall motion abnormalities or things like that,” he observed.

The decision also seems to disregard the EXCEL trial controversy that illustrated how outcomes and a trial’s message can change depending on which definition of periprocedural MI is used.

With regard to bleeding, the surgical associations agree with the VARC-3 recommendation to use different thresholds when bleeding is integrated into a composite endpoint (type 2 or greater for TAVR and types 3 or greater for SAVR) but suggest this important point should be featured in the chapter on bleeding rather than the section on composite endpoints.

The surgical associations say VARC-3 also got it right adding the need for a new permanent pacemaker to the early composite safety endpoint, but that it was a “missed opportunity” not to include new left bundle-branch block in the safety composite, despite recognizing that this may become an important endpoint to consider in the future.

Dr. Myers said that left bundle-branch block could have implications for survival as TAVR moves into lower-risk, younger patients, as some data with 1-year follow-up suggest it has a prognostic impact, even in the higher-risk older patients with more competing risks.

Finally, the surgical associations point out that only two of the 23 VARC-3 authors were practicing cardiac surgeons and say that a more diverse writing group “may help mitigate issues related to the duality of interests.”

Dr. Généreux said that the final author list is not a reflection of the rigorous work done by 11 cardiac surgeons including the two surgeon authors. The VARC-3 writing committee also had a good representation of women, unlike the surgical position statement, which was penned by eight men.

Dr. Myers reported no relevant financial relationships. Coauthors disclosed ties with EACTS, Edwards Lifesciences, Medtronic, Abbott Vascular, Boston Scientific, CryoLife, Shockwave, and JenaValve. Dr. Généreux disclosed ties with Abbott Vascular, Abiomed, Boston Scientific, Cardinal Health, Cardiovascular Systems, Edwards Lifesciences, Medtronic, Opsens, Siemens, SoundBite Medical Solutions, Sig.Num, Saranas, Teleflex, Tryton Medical, Pi-Cardia, and Puzzle Medical.

A version of this article first appeared on Medscape.com.

Five international cardiac surgery associations have banded together to address “substantive concerns” regarding the recently updated Valve Academic Research Consortium 3 (VARC-3) clinical endpoint definitions for aortic valve research.

The VARC-3 update was a multidisciplinary effort that included more than a dozen new or modified definitions for use in transcatheter and surgical aortic valve replacement (TAVR/SAVR) clinical trials, but drew criticism last year from surgeons that some of its definitions favor TAVR over surgery and that its writing committee had deep ties to industry and lacked diversity.

The new surgical associations’ position statement calls out five specific VARC-3 definitions – rehospitalization, valve thrombosis, bleeding, myocardial infarction (MI), and left bundle-branch block (LBBB).

The statement was jointly issued by the Society of Thoracic Surgeons (STS), the American Association for Thoracic Surgery, the European Association for Cardio-Thoracic Surgery, the Asian Society for Cardiovascular and Thoracic Surgery, and the Latin American Association of Cardiac and Endovascular Surgery.

It was copublished in Annals of Thoracic Surgery, the Journal of Thoracic and Cardiovascular Surgery, the European Journal of Cardio-Thoracic Surgery, and the Asian Cardiovascular and Thoracic Annals.

“We hope that this message can be seen, even if it’s somewhat difficult to hear sometimes, as positive constructive criticism compared to some of the dialogue that we’ve had on social media,” lead author Patrick O. Myers, MD, Lausanne (Switzerland) University Hospital, said in an interview. “It’s not criticizing people or the process but just trying to make these definitions better to ensure the good design of clinical trials.”

The president of each surgical association recommended representatives to help write the position statement, and once completed over Zoom meetings, it received formal endorsement from each association prior to publication, he said.

Reached for comment, VARC-3 lead author Philippe Généreux, MD, Gagnon Cardiovascular Institute, Morristown (N.J.) Medical Center, said, “I was pleasantly surprised that their comments were actually pretty minor and that most of these comments are really more a reflection, not of the validity of the definitions, but rather their applications.”

He noted that all the potential issues with the definitions were already discussed during the making of VARC-3 and resolved by consensus of more than 50 experts including the STS president at the time, Food and Drug Administration officials, and experts from the community.

“To be quite honest, I’m not sure they have consensus,” Dr. Généreux said. He added that the writing committee welcomes input from anyone, but “we’re not going to change the definitions to please eight individuals if we strongly believe by consensus of experts in the field that this is not the right thing to do.”

Rehospitalizations and valve thrombosis

The surgical associations praise VARC-3 for providing a standardized definition of bioprosthetic valve failure, but say they will not endorse the inclusion of rehospitalization as a component of the primary efficacy composite endpoint along with all-cause mortality, stroke, and quality of life.

They note that rehospitalizations outnumber mortality events, especially in short follow-up trials, and that the superiority of TAVR at 1 year in the PARTNER 3 trial of low-risk patients was driven primarily by more rehospitalizations in the surgical arm, but that this superiority was waning at 2 years of follow-up.

“The first thing we are calling for is that it shouldn’t be part of the primary composite outcome measure,” Dr. Myers said. But if it really has to be included, a 30-day blanking period for rehospitalization “would acknowledge that there’s a greater risk of rehospitalization during the acute phase of recovering from surgery.”

Dr. Généreux said that VARC-3 provides granular details for defining the different types of hospitalizations, but that a 30-day blanking period makes no sense. “If you close your eyes to anything within 30 days because you don’t like it, you’re missing the opportunity to improve your procedure, to improve your treatment, and to characterize precisely what happened with your patient.”

The new document lauds VARC-3’s focus on patient-centered and clinically relevant endpoints but questions the definition of valve thrombosis as a “clinically significant” thrombus. It points out that the incidence of valve thrombosis was significantly higher with TAVR versus SAVR in PARTNER 3 using the older VARC-2 definition, which did not require evidence of clinical sequelae (2.6% vs. 0.7%; P = .02). Under the new definition, however, half of the thrombi would be relabeled as “nothing there,” Dr. Myers said.

“As we’re doing this in younger and younger patients who will survive longer, there is a question of thrombus having an effect on the valve and leading to earlier structural valve deterioration,” he added. “All this is conjecture. We don’t have the data. So mainly what we’re advocating is that all thrombi should be reported.”
 

MIs, bleeding, and LBBB

The policy statement also criticizes VARC-3’s decision to define periprocedural (type 5) MI using a biomarker-only definition without need of clinical confirmation. Such definitions have been shown to have a very poor prognostic significance in surgical series compared with the Universal Definitions of Myocardial Infarction, Dr. Myers said.

“What’s interesting is that for thrombus and bleeding, they require clinical correlation, but on the perioperative MI they now use a definition that does not require clinical significance, meaning no ECG changes, no regional wall motion abnormalities or things like that,” he observed.

The decision also seems to disregard the EXCEL trial controversy that illustrated how outcomes and a trial’s message can change depending on which definition of periprocedural MI is used.

With regard to bleeding, the surgical associations agree with the VARC-3 recommendation to use different thresholds when bleeding is integrated into a composite endpoint (type 2 or greater for TAVR and types 3 or greater for SAVR) but suggest this important point should be featured in the chapter on bleeding rather than the section on composite endpoints.

The surgical associations say VARC-3 also got it right adding the need for a new permanent pacemaker to the early composite safety endpoint, but that it was a “missed opportunity” not to include new left bundle-branch block in the safety composite, despite recognizing that this may become an important endpoint to consider in the future.

Dr. Myers said that left bundle-branch block could have implications for survival as TAVR moves into lower-risk, younger patients, as some data with 1-year follow-up suggest it has a prognostic impact, even in the higher-risk older patients with more competing risks.

Finally, the surgical associations point out that only two of the 23 VARC-3 authors were practicing cardiac surgeons and say that a more diverse writing group “may help mitigate issues related to the duality of interests.”

Dr. Généreux said that the final author list is not a reflection of the rigorous work done by 11 cardiac surgeons including the two surgeon authors. The VARC-3 writing committee also had a good representation of women, unlike the surgical position statement, which was penned by eight men.

Dr. Myers reported no relevant financial relationships. Coauthors disclosed ties with EACTS, Edwards Lifesciences, Medtronic, Abbott Vascular, Boston Scientific, CryoLife, Shockwave, and JenaValve. Dr. Généreux disclosed ties with Abbott Vascular, Abiomed, Boston Scientific, Cardinal Health, Cardiovascular Systems, Edwards Lifesciences, Medtronic, Opsens, Siemens, SoundBite Medical Solutions, Sig.Num, Saranas, Teleflex, Tryton Medical, Pi-Cardia, and Puzzle Medical.

A version of this article first appeared on Medscape.com.

Five international cardiac surgery associations have banded together to address “substantive concerns” regarding the recently updated Valve Academic Research Consortium 3 (VARC-3) clinical endpoint definitions for aortic valve research.

The VARC-3 update was a multidisciplinary effort that included more than a dozen new or modified definitions for use in transcatheter and surgical aortic valve replacement (TAVR/SAVR) clinical trials, but drew criticism last year from surgeons that some of its definitions favor TAVR over surgery and that its writing committee had deep ties to industry and lacked diversity.

The new surgical associations’ position statement calls out five specific VARC-3 definitions – rehospitalization, valve thrombosis, bleeding, myocardial infarction (MI), and left bundle-branch block (LBBB).

The statement was jointly issued by the Society of Thoracic Surgeons (STS), the American Association for Thoracic Surgery, the European Association for Cardio-Thoracic Surgery, the Asian Society for Cardiovascular and Thoracic Surgery, and the Latin American Association of Cardiac and Endovascular Surgery.

It was copublished in Annals of Thoracic Surgery, the Journal of Thoracic and Cardiovascular Surgery, the European Journal of Cardio-Thoracic Surgery, and the Asian Cardiovascular and Thoracic Annals.

“We hope that this message can be seen, even if it’s somewhat difficult to hear sometimes, as positive constructive criticism compared to some of the dialogue that we’ve had on social media,” lead author Patrick O. Myers, MD, Lausanne (Switzerland) University Hospital, said in an interview. “It’s not criticizing people or the process but just trying to make these definitions better to ensure the good design of clinical trials.”

The president of each surgical association recommended representatives to help write the position statement, and once completed over Zoom meetings, it received formal endorsement from each association prior to publication, he said.

Reached for comment, VARC-3 lead author Philippe Généreux, MD, Gagnon Cardiovascular Institute, Morristown (N.J.) Medical Center, said, “I was pleasantly surprised that their comments were actually pretty minor and that most of these comments are really more a reflection, not of the validity of the definitions, but rather their applications.”

He noted that all the potential issues with the definitions were already discussed during the making of VARC-3 and resolved by consensus of more than 50 experts including the STS president at the time, Food and Drug Administration officials, and experts from the community.

“To be quite honest, I’m not sure they have consensus,” Dr. Généreux said. He added that the writing committee welcomes input from anyone, but “we’re not going to change the definitions to please eight individuals if we strongly believe by consensus of experts in the field that this is not the right thing to do.”

Rehospitalizations and valve thrombosis

The surgical associations praise VARC-3 for providing a standardized definition of bioprosthetic valve failure, but say they will not endorse the inclusion of rehospitalization as a component of the primary efficacy composite endpoint along with all-cause mortality, stroke, and quality of life.

They note that rehospitalizations outnumber mortality events, especially in short follow-up trials, and that the superiority of TAVR at 1 year in the PARTNER 3 trial of low-risk patients was driven primarily by more rehospitalizations in the surgical arm, but that this superiority was waning at 2 years of follow-up.

“The first thing we are calling for is that it shouldn’t be part of the primary composite outcome measure,” Dr. Myers said. But if it really has to be included, a 30-day blanking period for rehospitalization “would acknowledge that there’s a greater risk of rehospitalization during the acute phase of recovering from surgery.”

Dr. Généreux said that VARC-3 provides granular details for defining the different types of hospitalizations, but that a 30-day blanking period makes no sense. “If you close your eyes to anything within 30 days because you don’t like it, you’re missing the opportunity to improve your procedure, to improve your treatment, and to characterize precisely what happened with your patient.”

The new document lauds VARC-3’s focus on patient-centered and clinically relevant endpoints but questions the definition of valve thrombosis as a “clinically significant” thrombus. It points out that the incidence of valve thrombosis was significantly higher with TAVR versus SAVR in PARTNER 3 using the older VARC-2 definition, which did not require evidence of clinical sequelae (2.6% vs. 0.7%; P = .02). Under the new definition, however, half of the thrombi would be relabeled as “nothing there,” Dr. Myers said.

“As we’re doing this in younger and younger patients who will survive longer, there is a question of thrombus having an effect on the valve and leading to earlier structural valve deterioration,” he added. “All this is conjecture. We don’t have the data. So mainly what we’re advocating is that all thrombi should be reported.”
 

MIs, bleeding, and LBBB

The policy statement also criticizes VARC-3’s decision to define periprocedural (type 5) MI using a biomarker-only definition without need of clinical confirmation. Such definitions have been shown to have a very poor prognostic significance in surgical series compared with the Universal Definitions of Myocardial Infarction, Dr. Myers said.

“What’s interesting is that for thrombus and bleeding, they require clinical correlation, but on the perioperative MI they now use a definition that does not require clinical significance, meaning no ECG changes, no regional wall motion abnormalities or things like that,” he observed.

The decision also seems to disregard the EXCEL trial controversy that illustrated how outcomes and a trial’s message can change depending on which definition of periprocedural MI is used.

With regard to bleeding, the surgical associations agree with the VARC-3 recommendation to use different thresholds when bleeding is integrated into a composite endpoint (type 2 or greater for TAVR and types 3 or greater for SAVR) but suggest this important point should be featured in the chapter on bleeding rather than the section on composite endpoints.

The surgical associations say VARC-3 also got it right adding the need for a new permanent pacemaker to the early composite safety endpoint, but that it was a “missed opportunity” not to include new left bundle-branch block in the safety composite, despite recognizing that this may become an important endpoint to consider in the future.

Dr. Myers said that left bundle-branch block could have implications for survival as TAVR moves into lower-risk, younger patients, as some data with 1-year follow-up suggest it has a prognostic impact, even in the higher-risk older patients with more competing risks.

Finally, the surgical associations point out that only two of the 23 VARC-3 authors were practicing cardiac surgeons and say that a more diverse writing group “may help mitigate issues related to the duality of interests.”

Dr. Généreux said that the final author list is not a reflection of the rigorous work done by 11 cardiac surgeons including the two surgeon authors. The VARC-3 writing committee also had a good representation of women, unlike the surgical position statement, which was penned by eight men.

Dr. Myers reported no relevant financial relationships. Coauthors disclosed ties with EACTS, Edwards Lifesciences, Medtronic, Abbott Vascular, Boston Scientific, CryoLife, Shockwave, and JenaValve. Dr. Généreux disclosed ties with Abbott Vascular, Abiomed, Boston Scientific, Cardinal Health, Cardiovascular Systems, Edwards Lifesciences, Medtronic, Opsens, Siemens, SoundBite Medical Solutions, Sig.Num, Saranas, Teleflex, Tryton Medical, Pi-Cardia, and Puzzle Medical.

A version of this article first appeared on Medscape.com.

A congressional oversight subcommittee is investigating the Food and Drug Administration’s regulation of a high-risk heart pump, citing safety issues detailed by ProPublica.

The HeartWare Ventricular Assist Device, created to treat patients with severe heart failure, stopped meeting key federal standards as early as 2014. But the FDA took no decisive action even as those problems persisted, and thousands of Americans continued to be implanted with the pump.

By the end of 2020, the FDA had received more than 3,000 reports of deaths related to the HeartWare device, according to a ProPublica data analysis. A father of four died as his children tried to resuscitate him when his device suddenly stopped. A teenager died after vomiting blood in the middle of the night, while his mother struggled to restart a faulty pump.

“I am concerned by FDA’s slow action, over multiple administrations, to protect patients from this product despite early warning signs,” Rep. Raja Krishnamoorthi, D-Ill., said in a scathing letter sent March 22 to the agency’s commissioner, Robert Califf, MD.

Mr. Krishnamoorthi, the chairman of the U.S. House Committee on Oversight and Reform’s Subcommittee on Economic and Consumer Policy, requested information on how the FDA made regulatory decisions related to the HeartWare device and why it didn’t take further action.

The FDA did not provide comment to ProPublica on the subcommittee’s investigation and said it would respond directly to Mr. Krishnamoorthi. It also reiterated its response to ProPublica’s findings and said the agency had been closely overseeing the HeartWare device since 2012, with patient safety as its “highest priority.”

Medtronic, the company that acquired HeartWare in 2016, took the device off the market in June 2021. The company said that new data showed a competing heart pump had better outcomes. In response to the ProPublica investigation 2 months later, the company said it took the FDA’s inspections seriously and had worked closely with the agency to address issues with the device.

Medtronic declined to comment on the subcommittee’s investigation.

Mr. Krishnamoorthi asked in the letter if any steps were being taken to address how patients, doctors and other federal agencies are notified of problems that the FDA finds with medical devices.

Many patients told ProPublica they were never informed of issues with the HeartWare pump before or after their implants. Some people who still have the device said they weren’t told when it was taken off the market. Medtronic said in December it had confirmed 90% of U.S. patients had received notification of the HeartWare discontinuation, but that it was still working to reach the other 10%.

About 2,000 patients still had HeartWare pumps as of last year. The FDA and Medtronic recommended against removing those devices barring medical necessity because the surgery to do so carries a high risk.

In his letter, Mr. Krishnamoorthi gave the FDA a deadline of April 5 to respond.
 

This story was originally published on ProPublica. ProPublica is a nonprofit newsroom that investigates abuses of power. Sign up to receive their biggest stories as soon as they’re published.

A congressional oversight subcommittee is investigating the Food and Drug Administration’s regulation of a high-risk heart pump, citing safety issues detailed by ProPublica.

The HeartWare Ventricular Assist Device, created to treat patients with severe heart failure, stopped meeting key federal standards as early as 2014. But the FDA took no decisive action even as those problems persisted, and thousands of Americans continued to be implanted with the pump.

By the end of 2020, the FDA had received more than 3,000 reports of deaths related to the HeartWare device, according to a ProPublica data analysis. A father of four died as his children tried to resuscitate him when his device suddenly stopped. A teenager died after vomiting blood in the middle of the night, while his mother struggled to restart a faulty pump.

“I am concerned by FDA’s slow action, over multiple administrations, to protect patients from this product despite early warning signs,” Rep. Raja Krishnamoorthi, D-Ill., said in a scathing letter sent March 22 to the agency’s commissioner, Robert Califf, MD.

Mr. Krishnamoorthi, the chairman of the U.S. House Committee on Oversight and Reform’s Subcommittee on Economic and Consumer Policy, requested information on how the FDA made regulatory decisions related to the HeartWare device and why it didn’t take further action.

The FDA did not provide comment to ProPublica on the subcommittee’s investigation and said it would respond directly to Mr. Krishnamoorthi. It also reiterated its response to ProPublica’s findings and said the agency had been closely overseeing the HeartWare device since 2012, with patient safety as its “highest priority.”

Medtronic, the company that acquired HeartWare in 2016, took the device off the market in June 2021. The company said that new data showed a competing heart pump had better outcomes. In response to the ProPublica investigation 2 months later, the company said it took the FDA’s inspections seriously and had worked closely with the agency to address issues with the device.

Medtronic declined to comment on the subcommittee’s investigation.

Mr. Krishnamoorthi asked in the letter if any steps were being taken to address how patients, doctors and other federal agencies are notified of problems that the FDA finds with medical devices.

Many patients told ProPublica they were never informed of issues with the HeartWare pump before or after their implants. Some people who still have the device said they weren’t told when it was taken off the market. Medtronic said in December it had confirmed 90% of U.S. patients had received notification of the HeartWare discontinuation, but that it was still working to reach the other 10%.

About 2,000 patients still had HeartWare pumps as of last year. The FDA and Medtronic recommended against removing those devices barring medical necessity because the surgery to do so carries a high risk.

In his letter, Mr. Krishnamoorthi gave the FDA a deadline of April 5 to respond.
 

This story was originally published on ProPublica. ProPublica is a nonprofit newsroom that investigates abuses of power. Sign up to receive their biggest stories as soon as they’re published.

A congressional oversight subcommittee is investigating the Food and Drug Administration’s regulation of a high-risk heart pump, citing safety issues detailed by ProPublica.

The HeartWare Ventricular Assist Device, created to treat patients with severe heart failure, stopped meeting key federal standards as early as 2014. But the FDA took no decisive action even as those problems persisted, and thousands of Americans continued to be implanted with the pump.

By the end of 2020, the FDA had received more than 3,000 reports of deaths related to the HeartWare device, according to a ProPublica data analysis. A father of four died as his children tried to resuscitate him when his device suddenly stopped. A teenager died after vomiting blood in the middle of the night, while his mother struggled to restart a faulty pump.

“I am concerned by FDA’s slow action, over multiple administrations, to protect patients from this product despite early warning signs,” Rep. Raja Krishnamoorthi, D-Ill., said in a scathing letter sent March 22 to the agency’s commissioner, Robert Califf, MD.

Mr. Krishnamoorthi, the chairman of the U.S. House Committee on Oversight and Reform’s Subcommittee on Economic and Consumer Policy, requested information on how the FDA made regulatory decisions related to the HeartWare device and why it didn’t take further action.

The FDA did not provide comment to ProPublica on the subcommittee’s investigation and said it would respond directly to Mr. Krishnamoorthi. It also reiterated its response to ProPublica’s findings and said the agency had been closely overseeing the HeartWare device since 2012, with patient safety as its “highest priority.”

Medtronic, the company that acquired HeartWare in 2016, took the device off the market in June 2021. The company said that new data showed a competing heart pump had better outcomes. In response to the ProPublica investigation 2 months later, the company said it took the FDA’s inspections seriously and had worked closely with the agency to address issues with the device.

Medtronic declined to comment on the subcommittee’s investigation.

Mr. Krishnamoorthi asked in the letter if any steps were being taken to address how patients, doctors and other federal agencies are notified of problems that the FDA finds with medical devices.

Many patients told ProPublica they were never informed of issues with the HeartWare pump before or after their implants. Some people who still have the device said they weren’t told when it was taken off the market. Medtronic said in December it had confirmed 90% of U.S. patients had received notification of the HeartWare discontinuation, but that it was still working to reach the other 10%.

About 2,000 patients still had HeartWare pumps as of last year. The FDA and Medtronic recommended against removing those devices barring medical necessity because the surgery to do so carries a high risk.

In his letter, Mr. Krishnamoorthi gave the FDA a deadline of April 5 to respond.
 

This story was originally published on ProPublica. ProPublica is a nonprofit newsroom that investigates abuses of power. Sign up to receive their biggest stories as soon as they’re published.

The vast majority of U.S. physicians regularly treat patients with socioeconomic challenges – from financial instability and a lack of transportation to eviction threats and domestic problems – but are deeply frustrated by their inability to adequately address these issues, a new survey has found.

The survey, conducted in February by The Physicians Foundation, queried 1,502 doctors (500 primary care physicians and 1,002 specialists) about their experience with social drivers – also known as determinants – of health (SDOH). Among the key findings: More than 60% of respondents said they had little or no time to effectively address the SDOH needs of their patients, yet nearly 9 in 10 (87%) said they would like to be able to do so in the future.

Most (63%) said they feel burned out when they try to help patients with their SDOH needs; and nearly 7 in 10 (68%) said managing SDOH for their patients has a “major impact” on their mental health and well-being.

This news organization spoke with Gary Price, MD, president of The Physicians Foundation, about the findings.

Q: These issues aren’t new. Why did you undertake this survey now?

The Physicians Foundation has surveyed America’s physicians for a decade on their practice and the broader health care environment, which included questions on SDOH. However, this is the first one we’ve done that concentrated entirely on SDOH. We think it’s particularly timely now.

The COVID-19 pandemic focused a very harsh spotlight on the tremendous impact SDOH can have on patient health, care outcomes, costs, physician burden, and the physician-patient relationship. It’s become increasingly apparent that for our country to achieve health equity and improve our health care system, including physician satisfaction, we must address the impact of SDOH on patients and physicians.

Even before the pandemic, we had an epidemic of physician burnout. That was driven in large part by the huge amount of time being wasted on administrative tasks such as pre-approvals, insurance forms, and working with electronic medical records. Now we’re recognizing that the causes of physician burnout are much larger than that.

Q: The results of the survey show that physicians are seeing the effects of SDOH no matter where they practice – rural (81%), urban (81%), suburban (73%) – how old they are, or their own racial or ethnic heritage. Is that surprising?

I was, in fact, surprised by the pervasiveness. Every physician is seeing the impact of social drivers on their patients every day. For a long time, physicians tried to ignore these problems because they couldn’t deal with them at the practice level; it was too big a task. But if we’re going to decrease the cost of health care and increase the quality of outcomes and decrease the enormous disparities we see, we’re going to have to deal with these SDOH.

I think the problem is grim, but physicians recognize this issue. It’s not one that they traditionally are trained to deal with – and, more importantly, they are not reimbursed on these issues. But despite that, they all want to help.

Q: The survey found that 83% of physicians believed their inability to adequately deal with SDOH moderately (60%) or significantly (23%) contributed to their feelings of burnout. Why do you think physicians find these problems so frustrating and stressful?

The definition of burnout is feeling that you’re being held responsible for things you no longer have any control or authority over. A patient’s inability to find transportation to get to an appointment, or who has financial instability that can lead them to have to make a choice between buying medicine or buying food for their family, isn’t something a physician can change. The overwhelming majority of physicians in our survey not only recognize that their patients have needs in these areas, but they don’t have time to be able to deal with them the way that they’d like to – either the resources aren’t there, or they aren’t effective, or they simply don’t know where to turn.

This phenomenon has been quantified by research. A 2020 study in JAMA, by the Physicians Foundation Center for the Study of Physician Practice and Leadership at Weill Cornell Medicine, found that physicians who had a larger burden of patients with more social needs received lower quality scores from Medicare and were less likely to receive bonuses for the care they provided. But the lower scores were related to the patients’ socioeconomic environment and had nothing do with the quality of the care they received.

Q: Researchers have looked at the relationship between SDOH and burnout, and what happens when physicians incorporate resources to address social issues into their practice. And it seems that doing so can help ease burnout at least a little.

That makes perfect sense. You’re now giving them the ability to intervene and do something about a health-related issue that’s going to help their patients get better quicker. At the same time, addressing these social issues can reduce health care costs to the system while improving outcomes. For example, when a patient with diabetes who needs insulin has their electricity cut off, they can no longer refrigerate the insulin. So simply having their electricity restored could keep them from being hospitalized for a diabetic coma because they weren’t able to follow their treatment.

The Health Leads Grow and Catalyze project, which we helped fund in 2014-2018, trained college students to make lists of key resources patients might require – like food, electricity, or heat – and work with physicians in the emergency room to get a prescription for that need. We’ve seen a very excellent return on investment and it’s now in health systems all over the country.

Q: The survey does a good job of highlighting the nature and scope of the problem, but what about solutions? What, if anything, can physicians be doing now to reduce the burden of SDOH for their patients?

The most important thing we’re doing now is drawing attention to the problem, not only to the impact it’s having on patients’ health but the health and well-being of our physicians.

The greatest challenge physicians said they faced was not having enough time to address these issues in their practice, and that stems directly from a lot of time that gets wasted on other things – preapprovals, inefficient EHRs, checkboxes. Our doctors reported that even when they know where the resources exist, they are hard to access or unavailable when they want them.

Almost all these things are going to require innovative solutions, and in some cases might vary by the individual. With transportation, for example, maybe we need a system like Meals on Wheels, where part of the solution could be a system of volunteer drivers to take patients to appointments. Or we might need more funding for transportation directly aimed at people who don’t have access to a bus line. But when you think about how much a ride in an ambulance costs versus how much it would cost to get someone to the doctor before they got sick enough to require that ambulance, that kind of expenditure makes a lot of sense for driving down individual and system costs. 

Q: The problem of unconscious bias in medicine has been receiving increasing attention. Do you think this bias is related to the issues of SDOH the new survey reveals?

Discrimination and racism are examples of SDOH. Implicit bias can happen in any aspect of our lives and interactions with others – so for physicians this can happen with our patients. Our survey didn’t specifically dive into how bias plays a role in addressing the impact of SDOH, but as a society we can no longer ignore any factor that hinders a person from accessing high-quality, cost-effective health care, including our own unconscious bias.

A version of this article first appeared on Medscape.com.

The vast majority of U.S. physicians regularly treat patients with socioeconomic challenges – from financial instability and a lack of transportation to eviction threats and domestic problems – but are deeply frustrated by their inability to adequately address these issues, a new survey has found.

The survey, conducted in February by The Physicians Foundation, queried 1,502 doctors (500 primary care physicians and 1,002 specialists) about their experience with social drivers – also known as determinants – of health (SDOH). Among the key findings: More than 60% of respondents said they had little or no time to effectively address the SDOH needs of their patients, yet nearly 9 in 10 (87%) said they would like to be able to do so in the future.

Most (63%) said they feel burned out when they try to help patients with their SDOH needs; and nearly 7 in 10 (68%) said managing SDOH for their patients has a “major impact” on their mental health and well-being.

This news organization spoke with Gary Price, MD, president of The Physicians Foundation, about the findings.

Q: These issues aren’t new. Why did you undertake this survey now?

The Physicians Foundation has surveyed America’s physicians for a decade on their practice and the broader health care environment, which included questions on SDOH. However, this is the first one we’ve done that concentrated entirely on SDOH. We think it’s particularly timely now.

The COVID-19 pandemic focused a very harsh spotlight on the tremendous impact SDOH can have on patient health, care outcomes, costs, physician burden, and the physician-patient relationship. It’s become increasingly apparent that for our country to achieve health equity and improve our health care system, including physician satisfaction, we must address the impact of SDOH on patients and physicians.

Even before the pandemic, we had an epidemic of physician burnout. That was driven in large part by the huge amount of time being wasted on administrative tasks such as pre-approvals, insurance forms, and working with electronic medical records. Now we’re recognizing that the causes of physician burnout are much larger than that.

Q: The results of the survey show that physicians are seeing the effects of SDOH no matter where they practice – rural (81%), urban (81%), suburban (73%) – how old they are, or their own racial or ethnic heritage. Is that surprising?

I was, in fact, surprised by the pervasiveness. Every physician is seeing the impact of social drivers on their patients every day. For a long time, physicians tried to ignore these problems because they couldn’t deal with them at the practice level; it was too big a task. But if we’re going to decrease the cost of health care and increase the quality of outcomes and decrease the enormous disparities we see, we’re going to have to deal with these SDOH.

I think the problem is grim, but physicians recognize this issue. It’s not one that they traditionally are trained to deal with – and, more importantly, they are not reimbursed on these issues. But despite that, they all want to help.

Q: The survey found that 83% of physicians believed their inability to adequately deal with SDOH moderately (60%) or significantly (23%) contributed to their feelings of burnout. Why do you think physicians find these problems so frustrating and stressful?

The definition of burnout is feeling that you’re being held responsible for things you no longer have any control or authority over. A patient’s inability to find transportation to get to an appointment, or who has financial instability that can lead them to have to make a choice between buying medicine or buying food for their family, isn’t something a physician can change. The overwhelming majority of physicians in our survey not only recognize that their patients have needs in these areas, but they don’t have time to be able to deal with them the way that they’d like to – either the resources aren’t there, or they aren’t effective, or they simply don’t know where to turn.

This phenomenon has been quantified by research. A 2020 study in JAMA, by the Physicians Foundation Center for the Study of Physician Practice and Leadership at Weill Cornell Medicine, found that physicians who had a larger burden of patients with more social needs received lower quality scores from Medicare and were less likely to receive bonuses for the care they provided. But the lower scores were related to the patients’ socioeconomic environment and had nothing do with the quality of the care they received.

Q: Researchers have looked at the relationship between SDOH and burnout, and what happens when physicians incorporate resources to address social issues into their practice. And it seems that doing so can help ease burnout at least a little.

That makes perfect sense. You’re now giving them the ability to intervene and do something about a health-related issue that’s going to help their patients get better quicker. At the same time, addressing these social issues can reduce health care costs to the system while improving outcomes. For example, when a patient with diabetes who needs insulin has their electricity cut off, they can no longer refrigerate the insulin. So simply having their electricity restored could keep them from being hospitalized for a diabetic coma because they weren’t able to follow their treatment.

The Health Leads Grow and Catalyze project, which we helped fund in 2014-2018, trained college students to make lists of key resources patients might require – like food, electricity, or heat – and work with physicians in the emergency room to get a prescription for that need. We’ve seen a very excellent return on investment and it’s now in health systems all over the country.

Q: The survey does a good job of highlighting the nature and scope of the problem, but what about solutions? What, if anything, can physicians be doing now to reduce the burden of SDOH for their patients?

The most important thing we’re doing now is drawing attention to the problem, not only to the impact it’s having on patients’ health but the health and well-being of our physicians.

The greatest challenge physicians said they faced was not having enough time to address these issues in their practice, and that stems directly from a lot of time that gets wasted on other things – preapprovals, inefficient EHRs, checkboxes. Our doctors reported that even when they know where the resources exist, they are hard to access or unavailable when they want them.

Almost all these things are going to require innovative solutions, and in some cases might vary by the individual. With transportation, for example, maybe we need a system like Meals on Wheels, where part of the solution could be a system of volunteer drivers to take patients to appointments. Or we might need more funding for transportation directly aimed at people who don’t have access to a bus line. But when you think about how much a ride in an ambulance costs versus how much it would cost to get someone to the doctor before they got sick enough to require that ambulance, that kind of expenditure makes a lot of sense for driving down individual and system costs. 

Q: The problem of unconscious bias in medicine has been receiving increasing attention. Do you think this bias is related to the issues of SDOH the new survey reveals?

Discrimination and racism are examples of SDOH. Implicit bias can happen in any aspect of our lives and interactions with others – so for physicians this can happen with our patients. Our survey didn’t specifically dive into how bias plays a role in addressing the impact of SDOH, but as a society we can no longer ignore any factor that hinders a person from accessing high-quality, cost-effective health care, including our own unconscious bias.

A version of this article first appeared on Medscape.com.

The vast majority of U.S. physicians regularly treat patients with socioeconomic challenges – from financial instability and a lack of transportation to eviction threats and domestic problems – but are deeply frustrated by their inability to adequately address these issues, a new survey has found.

The survey, conducted in February by The Physicians Foundation, queried 1,502 doctors (500 primary care physicians and 1,002 specialists) about their experience with social drivers – also known as determinants – of health (SDOH). Among the key findings: More than 60% of respondents said they had little or no time to effectively address the SDOH needs of their patients, yet nearly 9 in 10 (87%) said they would like to be able to do so in the future.

Most (63%) said they feel burned out when they try to help patients with their SDOH needs; and nearly 7 in 10 (68%) said managing SDOH for their patients has a “major impact” on their mental health and well-being.

This news organization spoke with Gary Price, MD, president of The Physicians Foundation, about the findings.

Q: These issues aren’t new. Why did you undertake this survey now?

The Physicians Foundation has surveyed America’s physicians for a decade on their practice and the broader health care environment, which included questions on SDOH. However, this is the first one we’ve done that concentrated entirely on SDOH. We think it’s particularly timely now.

The COVID-19 pandemic focused a very harsh spotlight on the tremendous impact SDOH can have on patient health, care outcomes, costs, physician burden, and the physician-patient relationship. It’s become increasingly apparent that for our country to achieve health equity and improve our health care system, including physician satisfaction, we must address the impact of SDOH on patients and physicians.

Even before the pandemic, we had an epidemic of physician burnout. That was driven in large part by the huge amount of time being wasted on administrative tasks such as pre-approvals, insurance forms, and working with electronic medical records. Now we’re recognizing that the causes of physician burnout are much larger than that.

Q: The results of the survey show that physicians are seeing the effects of SDOH no matter where they practice – rural (81%), urban (81%), suburban (73%) – how old they are, or their own racial or ethnic heritage. Is that surprising?

I was, in fact, surprised by the pervasiveness. Every physician is seeing the impact of social drivers on their patients every day. For a long time, physicians tried to ignore these problems because they couldn’t deal with them at the practice level; it was too big a task. But if we’re going to decrease the cost of health care and increase the quality of outcomes and decrease the enormous disparities we see, we’re going to have to deal with these SDOH.

I think the problem is grim, but physicians recognize this issue. It’s not one that they traditionally are trained to deal with – and, more importantly, they are not reimbursed on these issues. But despite that, they all want to help.

Q: The survey found that 83% of physicians believed their inability to adequately deal with SDOH moderately (60%) or significantly (23%) contributed to their feelings of burnout. Why do you think physicians find these problems so frustrating and stressful?

The definition of burnout is feeling that you’re being held responsible for things you no longer have any control or authority over. A patient’s inability to find transportation to get to an appointment, or who has financial instability that can lead them to have to make a choice between buying medicine or buying food for their family, isn’t something a physician can change. The overwhelming majority of physicians in our survey not only recognize that their patients have needs in these areas, but they don’t have time to be able to deal with them the way that they’d like to – either the resources aren’t there, or they aren’t effective, or they simply don’t know where to turn.

This phenomenon has been quantified by research. A 2020 study in JAMA, by the Physicians Foundation Center for the Study of Physician Practice and Leadership at Weill Cornell Medicine, found that physicians who had a larger burden of patients with more social needs received lower quality scores from Medicare and were less likely to receive bonuses for the care they provided. But the lower scores were related to the patients’ socioeconomic environment and had nothing do with the quality of the care they received.

Q: Researchers have looked at the relationship between SDOH and burnout, and what happens when physicians incorporate resources to address social issues into their practice. And it seems that doing so can help ease burnout at least a little.

That makes perfect sense. You’re now giving them the ability to intervene and do something about a health-related issue that’s going to help their patients get better quicker. At the same time, addressing these social issues can reduce health care costs to the system while improving outcomes. For example, when a patient with diabetes who needs insulin has their electricity cut off, they can no longer refrigerate the insulin. So simply having their electricity restored could keep them from being hospitalized for a diabetic coma because they weren’t able to follow their treatment.

The Health Leads Grow and Catalyze project, which we helped fund in 2014-2018, trained college students to make lists of key resources patients might require – like food, electricity, or heat – and work with physicians in the emergency room to get a prescription for that need. We’ve seen a very excellent return on investment and it’s now in health systems all over the country.

Q: The survey does a good job of highlighting the nature and scope of the problem, but what about solutions? What, if anything, can physicians be doing now to reduce the burden of SDOH for their patients?

The most important thing we’re doing now is drawing attention to the problem, not only to the impact it’s having on patients’ health but the health and well-being of our physicians.

The greatest challenge physicians said they faced was not having enough time to address these issues in their practice, and that stems directly from a lot of time that gets wasted on other things – preapprovals, inefficient EHRs, checkboxes. Our doctors reported that even when they know where the resources exist, they are hard to access or unavailable when they want them.

Almost all these things are going to require innovative solutions, and in some cases might vary by the individual. With transportation, for example, maybe we need a system like Meals on Wheels, where part of the solution could be a system of volunteer drivers to take patients to appointments. Or we might need more funding for transportation directly aimed at people who don’t have access to a bus line. But when you think about how much a ride in an ambulance costs versus how much it would cost to get someone to the doctor before they got sick enough to require that ambulance, that kind of expenditure makes a lot of sense for driving down individual and system costs. 

Q: The problem of unconscious bias in medicine has been receiving increasing attention. Do you think this bias is related to the issues of SDOH the new survey reveals?

Discrimination and racism are examples of SDOH. Implicit bias can happen in any aspect of our lives and interactions with others – so for physicians this can happen with our patients. Our survey didn’t specifically dive into how bias plays a role in addressing the impact of SDOH, but as a society we can no longer ignore any factor that hinders a person from accessing high-quality, cost-effective health care, including our own unconscious bias.

A version of this article first appeared on Medscape.com.

Seroprevalence surveys suggest that, from the beginning of the pandemic to 2022, more than a third of the global population had been infected with SARS-CoV-2. As large numbers of people continue to be infected, the efficacy and duration of natural immunity, in terms of protection against SARS-CoV-2 reinfections and severe disease, are of crucial significance. The virus’s epidemiologic trajectory will be influenced by the trends in vaccine-induced and hybrid immunity.

Omicron’s immune evasion

Cases of SARS-CoV-2 reinfection are increasing around the world. According to data from the U.K. Health Security Agency, 650,000 people in England have been infected twice, and most of them were reinfected in the past 2 months. Before mid-November 2021, reinfections accounted for about 1% of reported cases, but the rate has now increased to around 10%. The reinfection risk was 16 times higher between mid-December 2021 and early January 2022. Experts believe that this spike in reinfections is related to the spread of Omicron, which overtook Delta as the dominant variant. Nonetheless, other aspects should also be considered.

Omicron’s greater propensity to spread is not unrelated to its ability to evade the body’s immune defenses. This aspect was raised in a letter recently published in the New England Journal of Medicine. The authors reported that the effectiveness of previous infection in preventing reinfection against the Alpha, Beta, and Delta variants was around 90%, but it was only 56% against Omicron.
 

Natural immunity

Natural immunity showed roughly similar effectiveness regarding protection against reinfection across different SARS-CoV-2 variants, with the exception of the Omicron variant. The risk of hospitalization and death was also reduced in SARS-CoV-2 reinfections versus primary infections. Observational studies indicate that natural immunity may offer equal or greater protection against SARS-CoV-2 infections, compared with immunization with two doses of an mRNA vaccine, but the data are not fully consistent.

Natural immunity seems to be relatively long-lasting. Data from Denmark and Austria show no evidence that protection against reinfections wanes after 6 months. Some investigations indicate that protection against reinfection is lowest 4-5 months after initial infection and increases thereafter, a finding that might hypothetically be explained by persistent viral shedding; that is, misclassification of prolonged SARS-CoV-2 infections as reinfections. While no comparison was made against information pertaining to unvaccinated, not previously-infected individuals, preliminary data from Israel suggest that protection from reinfection can decrease from 6 to more than 12 months after the first SARS-CoV-2 infection. Taken together, epidemiologic studies indicate that protection against reinfections by natural immunity lasts over 1 year with only moderate, if any, decline over this period. Among older individuals, immunocompromised patients, and those with certain comorbidities or exposure risk (for example, health care workers), rates of reinfection may be higher. It is plausible that reinfection risk may be a function of exposure risk.

There is accumulating evidence that reinfections may be significantly less severe than primary infections with SARS-CoV-2. Reduced clinical severity of SARS-CoV-2 reinfections naturally also makes sense from a biologic point of view, inasmuch as a previously primed immune system should be better prepared for a rechallenge with this virus.
 

Vaccine-induced immunity

The short-term (<4 months) efficacy of mRNA vaccines against SARS-CoV-2 is high and varies from 94.1% (Moderna) to 95% (BioNTech/Pfizer). This has been confirmed by randomized controlled trials and was subsequently confirmed in effectiveness studies in real-world settings. Waning efficacy was observed with respect to protection against SARS-CoV-2 infections (for example, only approximately 20% after about half a year in Qatar), whereas protection against severe disease was either sustained or showed only a moderate decline.

In individuals who received two doses of the BioNTech/Pfizer vaccine at least 5 months earlier, an additional vaccine dose, a so-called booster, significantly lowered mortality and severe illness. These findings suggest that the booster restored and probably exceeded the initial short-term efficacy of the initial vaccination.

Data are still emerging regarding the efficacy of boosters against the Omicron variants. Preliminary data suggest a far lower ability to restore protection from infection and vaccination. However, fatalities and hospitalizations remain low.
 

Natural immunity vs. vaccine-induced immunity

Comparisons of natural immunity with vaccine-induced immunity are complicated by a series of biases and by combinations of biases – for example, the biases of comparisons between infected and uninfected, plus the biases of comparisons between vaccinated and nonvaccinated, with strong potential selection biases and confounding. Of particular note, the proportion of people previously infected and/or vaccinated may influence estimates of effectiveness. Regarding this point, one study compared unvaccinated patients with a prior SARS-CoV-2 infection and vaccinated individuals followed up from a week after the second vaccine dose onward versus a group of unvaccinated, not previously infected individuals. The findings showed that, compared with unvaccinated, not previously infected individuals, the natural immunity group and the vaccinated group had similar protection of 94.8% and 92.8% against infection, of 94.1% and 94.2% against hospitalization, and of 96.4% and 94.4% against severe illness, respectively.

Hybrid immunity

The combination of a previous SARS-CoV-2 infection and a respective vaccination is called hybrid immunity. This combination seems to confer the greatest protection against SARS-CoV-2 infections, but several knowledge gaps remain regarding this issue.

Data from Israel showed that, when the time since the last immunity-conferring event (either primary infection or vaccination) was the same, the rates of SARS-CoV-2 infections were similar in the following groups: individuals who had a previous infection and no vaccination, individuals who had an infection and were then vaccinated with a single dose after at least 3 months, and individuals who were vaccinated (two doses) and then infected. Severe disease was relatively rare overall.

Data on the efficacy of hybrid immunity point in the direction of hybrid immunity being superior, as compared with either vaccine-induced (without a booster) immunity or natural immunity alone. Timing and mode of vaccination of previously infected individuals to achieve optimal hybrid immunity are central questions that remain to be addressed in future studies.

Given that vaccination rates are continuously increasing and that, by the beginning of 2022, perhaps half or more of the global population had already been infected with SARS-CoV-2, with the vast majority of this group not being officially detected, it would appear logical that future infection waves, even with highly transmissible variants of SARS-CoV-2, may be limited with respect to their maximum potential health burden. The advent of Omicron suggests that massive surges can occur even in populations with extremely high rates of previous vaccination and variable rates of prior infections. However, even then, the accompanying burden of hospitalizations and deaths is far less than what was seen in 2020 and 2021. One may argue that the pandemic has already transitioned to the endemic phase and that Omicron is an endemic wave occurring in the setting of already widespread population immunity.

A version of this article first appeared on Medscape.com.

Seroprevalence surveys suggest that, from the beginning of the pandemic to 2022, more than a third of the global population had been infected with SARS-CoV-2. As large numbers of people continue to be infected, the efficacy and duration of natural immunity, in terms of protection against SARS-CoV-2 reinfections and severe disease, are of crucial significance. The virus’s epidemiologic trajectory will be influenced by the trends in vaccine-induced and hybrid immunity.

Omicron’s immune evasion

Cases of SARS-CoV-2 reinfection are increasing around the world. According to data from the U.K. Health Security Agency, 650,000 people in England have been infected twice, and most of them were reinfected in the past 2 months. Before mid-November 2021, reinfections accounted for about 1% of reported cases, but the rate has now increased to around 10%. The reinfection risk was 16 times higher between mid-December 2021 and early January 2022. Experts believe that this spike in reinfections is related to the spread of Omicron, which overtook Delta as the dominant variant. Nonetheless, other aspects should also be considered.

Omicron’s greater propensity to spread is not unrelated to its ability to evade the body’s immune defenses. This aspect was raised in a letter recently published in the New England Journal of Medicine. The authors reported that the effectiveness of previous infection in preventing reinfection against the Alpha, Beta, and Delta variants was around 90%, but it was only 56% against Omicron.
 

Natural immunity

Natural immunity showed roughly similar effectiveness regarding protection against reinfection across different SARS-CoV-2 variants, with the exception of the Omicron variant. The risk of hospitalization and death was also reduced in SARS-CoV-2 reinfections versus primary infections. Observational studies indicate that natural immunity may offer equal or greater protection against SARS-CoV-2 infections, compared with immunization with two doses of an mRNA vaccine, but the data are not fully consistent.

Natural immunity seems to be relatively long-lasting. Data from Denmark and Austria show no evidence that protection against reinfections wanes after 6 months. Some investigations indicate that protection against reinfection is lowest 4-5 months after initial infection and increases thereafter, a finding that might hypothetically be explained by persistent viral shedding; that is, misclassification of prolonged SARS-CoV-2 infections as reinfections. While no comparison was made against information pertaining to unvaccinated, not previously-infected individuals, preliminary data from Israel suggest that protection from reinfection can decrease from 6 to more than 12 months after the first SARS-CoV-2 infection. Taken together, epidemiologic studies indicate that protection against reinfections by natural immunity lasts over 1 year with only moderate, if any, decline over this period. Among older individuals, immunocompromised patients, and those with certain comorbidities or exposure risk (for example, health care workers), rates of reinfection may be higher. It is plausible that reinfection risk may be a function of exposure risk.

There is accumulating evidence that reinfections may be significantly less severe than primary infections with SARS-CoV-2. Reduced clinical severity of SARS-CoV-2 reinfections naturally also makes sense from a biologic point of view, inasmuch as a previously primed immune system should be better prepared for a rechallenge with this virus.
 

Vaccine-induced immunity

The short-term (<4 months) efficacy of mRNA vaccines against SARS-CoV-2 is high and varies from 94.1% (Moderna) to 95% (BioNTech/Pfizer). This has been confirmed by randomized controlled trials and was subsequently confirmed in effectiveness studies in real-world settings. Waning efficacy was observed with respect to protection against SARS-CoV-2 infections (for example, only approximately 20% after about half a year in Qatar), whereas protection against severe disease was either sustained or showed only a moderate decline.

In individuals who received two doses of the BioNTech/Pfizer vaccine at least 5 months earlier, an additional vaccine dose, a so-called booster, significantly lowered mortality and severe illness. These findings suggest that the booster restored and probably exceeded the initial short-term efficacy of the initial vaccination.

Data are still emerging regarding the efficacy of boosters against the Omicron variants. Preliminary data suggest a far lower ability to restore protection from infection and vaccination. However, fatalities and hospitalizations remain low.
 

Natural immunity vs. vaccine-induced immunity

Comparisons of natural immunity with vaccine-induced immunity are complicated by a series of biases and by combinations of biases – for example, the biases of comparisons between infected and uninfected, plus the biases of comparisons between vaccinated and nonvaccinated, with strong potential selection biases and confounding. Of particular note, the proportion of people previously infected and/or vaccinated may influence estimates of effectiveness. Regarding this point, one study compared unvaccinated patients with a prior SARS-CoV-2 infection and vaccinated individuals followed up from a week after the second vaccine dose onward versus a group of unvaccinated, not previously infected individuals. The findings showed that, compared with unvaccinated, not previously infected individuals, the natural immunity group and the vaccinated group had similar protection of 94.8% and 92.8% against infection, of 94.1% and 94.2% against hospitalization, and of 96.4% and 94.4% against severe illness, respectively.

Hybrid immunity

The combination of a previous SARS-CoV-2 infection and a respective vaccination is called hybrid immunity. This combination seems to confer the greatest protection against SARS-CoV-2 infections, but several knowledge gaps remain regarding this issue.

Data from Israel showed that, when the time since the last immunity-conferring event (either primary infection or vaccination) was the same, the rates of SARS-CoV-2 infections were similar in the following groups: individuals who had a previous infection and no vaccination, individuals who had an infection and were then vaccinated with a single dose after at least 3 months, and individuals who were vaccinated (two doses) and then infected. Severe disease was relatively rare overall.

Data on the efficacy of hybrid immunity point in the direction of hybrid immunity being superior, as compared with either vaccine-induced (without a booster) immunity or natural immunity alone. Timing and mode of vaccination of previously infected individuals to achieve optimal hybrid immunity are central questions that remain to be addressed in future studies.

Given that vaccination rates are continuously increasing and that, by the beginning of 2022, perhaps half or more of the global population had already been infected with SARS-CoV-2, with the vast majority of this group not being officially detected, it would appear logical that future infection waves, even with highly transmissible variants of SARS-CoV-2, may be limited with respect to their maximum potential health burden. The advent of Omicron suggests that massive surges can occur even in populations with extremely high rates of previous vaccination and variable rates of prior infections. However, even then, the accompanying burden of hospitalizations and deaths is far less than what was seen in 2020 and 2021. One may argue that the pandemic has already transitioned to the endemic phase and that Omicron is an endemic wave occurring in the setting of already widespread population immunity.

A version of this article first appeared on Medscape.com.

Seroprevalence surveys suggest that, from the beginning of the pandemic to 2022, more than a third of the global population had been infected with SARS-CoV-2. As large numbers of people continue to be infected, the efficacy and duration of natural immunity, in terms of protection against SARS-CoV-2 reinfections and severe disease, are of crucial significance. The virus’s epidemiologic trajectory will be influenced by the trends in vaccine-induced and hybrid immunity.

Omicron’s immune evasion

Cases of SARS-CoV-2 reinfection are increasing around the world. According to data from the U.K. Health Security Agency, 650,000 people in England have been infected twice, and most of them were reinfected in the past 2 months. Before mid-November 2021, reinfections accounted for about 1% of reported cases, but the rate has now increased to around 10%. The reinfection risk was 16 times higher between mid-December 2021 and early January 2022. Experts believe that this spike in reinfections is related to the spread of Omicron, which overtook Delta as the dominant variant. Nonetheless, other aspects should also be considered.

Omicron’s greater propensity to spread is not unrelated to its ability to evade the body’s immune defenses. This aspect was raised in a letter recently published in the New England Journal of Medicine. The authors reported that the effectiveness of previous infection in preventing reinfection against the Alpha, Beta, and Delta variants was around 90%, but it was only 56% against Omicron.
 

Natural immunity

Natural immunity showed roughly similar effectiveness regarding protection against reinfection across different SARS-CoV-2 variants, with the exception of the Omicron variant. The risk of hospitalization and death was also reduced in SARS-CoV-2 reinfections versus primary infections. Observational studies indicate that natural immunity may offer equal or greater protection against SARS-CoV-2 infections, compared with immunization with two doses of an mRNA vaccine, but the data are not fully consistent.

Natural immunity seems to be relatively long-lasting. Data from Denmark and Austria show no evidence that protection against reinfections wanes after 6 months. Some investigations indicate that protection against reinfection is lowest 4-5 months after initial infection and increases thereafter, a finding that might hypothetically be explained by persistent viral shedding; that is, misclassification of prolonged SARS-CoV-2 infections as reinfections. While no comparison was made against information pertaining to unvaccinated, not previously-infected individuals, preliminary data from Israel suggest that protection from reinfection can decrease from 6 to more than 12 months after the first SARS-CoV-2 infection. Taken together, epidemiologic studies indicate that protection against reinfections by natural immunity lasts over 1 year with only moderate, if any, decline over this period. Among older individuals, immunocompromised patients, and those with certain comorbidities or exposure risk (for example, health care workers), rates of reinfection may be higher. It is plausible that reinfection risk may be a function of exposure risk.

There is accumulating evidence that reinfections may be significantly less severe than primary infections with SARS-CoV-2. Reduced clinical severity of SARS-CoV-2 reinfections naturally also makes sense from a biologic point of view, inasmuch as a previously primed immune system should be better prepared for a rechallenge with this virus.
 

Vaccine-induced immunity

The short-term (<4 months) efficacy of mRNA vaccines against SARS-CoV-2 is high and varies from 94.1% (Moderna) to 95% (BioNTech/Pfizer). This has been confirmed by randomized controlled trials and was subsequently confirmed in effectiveness studies in real-world settings. Waning efficacy was observed with respect to protection against SARS-CoV-2 infections (for example, only approximately 20% after about half a year in Qatar), whereas protection against severe disease was either sustained or showed only a moderate decline.

In individuals who received two doses of the BioNTech/Pfizer vaccine at least 5 months earlier, an additional vaccine dose, a so-called booster, significantly lowered mortality and severe illness. These findings suggest that the booster restored and probably exceeded the initial short-term efficacy of the initial vaccination.

Data are still emerging regarding the efficacy of boosters against the Omicron variants. Preliminary data suggest a far lower ability to restore protection from infection and vaccination. However, fatalities and hospitalizations remain low.
 

Natural immunity vs. vaccine-induced immunity

Comparisons of natural immunity with vaccine-induced immunity are complicated by a series of biases and by combinations of biases – for example, the biases of comparisons between infected and uninfected, plus the biases of comparisons between vaccinated and nonvaccinated, with strong potential selection biases and confounding. Of particular note, the proportion of people previously infected and/or vaccinated may influence estimates of effectiveness. Regarding this point, one study compared unvaccinated patients with a prior SARS-CoV-2 infection and vaccinated individuals followed up from a week after the second vaccine dose onward versus a group of unvaccinated, not previously infected individuals. The findings showed that, compared with unvaccinated, not previously infected individuals, the natural immunity group and the vaccinated group had similar protection of 94.8% and 92.8% against infection, of 94.1% and 94.2% against hospitalization, and of 96.4% and 94.4% against severe illness, respectively.

Hybrid immunity

The combination of a previous SARS-CoV-2 infection and a respective vaccination is called hybrid immunity. This combination seems to confer the greatest protection against SARS-CoV-2 infections, but several knowledge gaps remain regarding this issue.

Data from Israel showed that, when the time since the last immunity-conferring event (either primary infection or vaccination) was the same, the rates of SARS-CoV-2 infections were similar in the following groups: individuals who had a previous infection and no vaccination, individuals who had an infection and were then vaccinated with a single dose after at least 3 months, and individuals who were vaccinated (two doses) and then infected. Severe disease was relatively rare overall.

Data on the efficacy of hybrid immunity point in the direction of hybrid immunity being superior, as compared with either vaccine-induced (without a booster) immunity or natural immunity alone. Timing and mode of vaccination of previously infected individuals to achieve optimal hybrid immunity are central questions that remain to be addressed in future studies.

Given that vaccination rates are continuously increasing and that, by the beginning of 2022, perhaps half or more of the global population had already been infected with SARS-CoV-2, with the vast majority of this group not being officially detected, it would appear logical that future infection waves, even with highly transmissible variants of SARS-CoV-2, may be limited with respect to their maximum potential health burden. The advent of Omicron suggests that massive surges can occur even in populations with extremely high rates of previous vaccination and variable rates of prior infections. However, even then, the accompanying burden of hospitalizations and deaths is far less than what was seen in 2020 and 2021. One may argue that the pandemic has already transitioned to the endemic phase and that Omicron is an endemic wave occurring in the setting of already widespread population immunity.

A version of this article first appeared on Medscape.com.

Moderna on March 23 released interim results indicating that its mRNA-1273 COVID vaccine produced “robust” neutralizing antibody titers in children aged 6 months to 6 years – levels similar to those seen in adults.

Vaccine efficacy against infection was 43.7% in children aged 6 months to 2 years and 37.5% among children aged 2-6 years, the new data from its phase 2/3 KidCOVE study show.

The company explained the lower efficacy numbers by noting that its study involving these younger children was conducted during the Omicron wave. The same decrease in efficacy against infection was reported in adults during the Omicron surge.

A majority of COVID-19 cases were mild in the approximately 6,900 children aged 6 months to 6 years in the study. No severe COVID-19 cases, hospitalizations, or deaths were reported.

The primary series of two 25-mcg doses of the vaccine given 28 days apart was generally well tolerated. Most adverse events were mild to moderate. For example, temperature greater than 38° C (>100.4° F) was reported for 17.0% of the 6-month-old to 2-year-old group and for 14.6% of the 2- to 6-year-old group. A few children, 0.2% of each group, experienced a temperature greater than 40° C (>104° F).

Moderna plans to include these response, efficacy, and safety data in an application to the Food and Drug Administration for emergency use authorization (EUA) of the vaccine in these younger children in the coming weeks.

“We now have clinical data on the performance of our vaccine from infants 6 months of age through older adults,” Moderna CEO Stephane Bancel said in a news release. He described the interim results as “good news for parents of children under 6 years of age.”
 

In other news

Moderna also announced that it began the FDA EUA submission process for a 50-μg two-dose primary series for children aged 6-12 years.

The company is also updating its EUA submission for a 100-mcg two-dose primary series for children and adolescents aged 12-18 years.

Similar to its booster research in adults, Moderna plans to evaluate the potential of a booster dose for all pediatric populations, including those aged 6 months to 6 years, 6-12 years, and adolescents. The company is evaluating both a booster dose of mRNA-1273 and its bivalent booster candidate (mRNA1273.214), which includes an Omicron variant booster and mRNA-1273.

A version of this article first appeared on Medscape.com.

Moderna on March 23 released interim results indicating that its mRNA-1273 COVID vaccine produced “robust” neutralizing antibody titers in children aged 6 months to 6 years – levels similar to those seen in adults.

Vaccine efficacy against infection was 43.7% in children aged 6 months to 2 years and 37.5% among children aged 2-6 years, the new data from its phase 2/3 KidCOVE study show.

The company explained the lower efficacy numbers by noting that its study involving these younger children was conducted during the Omicron wave. The same decrease in efficacy against infection was reported in adults during the Omicron surge.

A majority of COVID-19 cases were mild in the approximately 6,900 children aged 6 months to 6 years in the study. No severe COVID-19 cases, hospitalizations, or deaths were reported.

The primary series of two 25-mcg doses of the vaccine given 28 days apart was generally well tolerated. Most adverse events were mild to moderate. For example, temperature greater than 38° C (>100.4° F) was reported for 17.0% of the 6-month-old to 2-year-old group and for 14.6% of the 2- to 6-year-old group. A few children, 0.2% of each group, experienced a temperature greater than 40° C (>104° F).

Moderna plans to include these response, efficacy, and safety data in an application to the Food and Drug Administration for emergency use authorization (EUA) of the vaccine in these younger children in the coming weeks.

“We now have clinical data on the performance of our vaccine from infants 6 months of age through older adults,” Moderna CEO Stephane Bancel said in a news release. He described the interim results as “good news for parents of children under 6 years of age.”
 

In other news

Moderna also announced that it began the FDA EUA submission process for a 50-μg two-dose primary series for children aged 6-12 years.

The company is also updating its EUA submission for a 100-mcg two-dose primary series for children and adolescents aged 12-18 years.

Similar to its booster research in adults, Moderna plans to evaluate the potential of a booster dose for all pediatric populations, including those aged 6 months to 6 years, 6-12 years, and adolescents. The company is evaluating both a booster dose of mRNA-1273 and its bivalent booster candidate (mRNA1273.214), which includes an Omicron variant booster and mRNA-1273.

A version of this article first appeared on Medscape.com.

Moderna on March 23 released interim results indicating that its mRNA-1273 COVID vaccine produced “robust” neutralizing antibody titers in children aged 6 months to 6 years – levels similar to those seen in adults.

Vaccine efficacy against infection was 43.7% in children aged 6 months to 2 years and 37.5% among children aged 2-6 years, the new data from its phase 2/3 KidCOVE study show.

The company explained the lower efficacy numbers by noting that its study involving these younger children was conducted during the Omicron wave. The same decrease in efficacy against infection was reported in adults during the Omicron surge.

A majority of COVID-19 cases were mild in the approximately 6,900 children aged 6 months to 6 years in the study. No severe COVID-19 cases, hospitalizations, or deaths were reported.

The primary series of two 25-mcg doses of the vaccine given 28 days apart was generally well tolerated. Most adverse events were mild to moderate. For example, temperature greater than 38° C (>100.4° F) was reported for 17.0% of the 6-month-old to 2-year-old group and for 14.6% of the 2- to 6-year-old group. A few children, 0.2% of each group, experienced a temperature greater than 40° C (>104° F).

Moderna plans to include these response, efficacy, and safety data in an application to the Food and Drug Administration for emergency use authorization (EUA) of the vaccine in these younger children in the coming weeks.

“We now have clinical data on the performance of our vaccine from infants 6 months of age through older adults,” Moderna CEO Stephane Bancel said in a news release. He described the interim results as “good news for parents of children under 6 years of age.”
 

In other news

Moderna also announced that it began the FDA EUA submission process for a 50-μg two-dose primary series for children aged 6-12 years.

The company is also updating its EUA submission for a 100-mcg two-dose primary series for children and adolescents aged 12-18 years.

Similar to its booster research in adults, Moderna plans to evaluate the potential of a booster dose for all pediatric populations, including those aged 6 months to 6 years, 6-12 years, and adolescents. The company is evaluating both a booster dose of mRNA-1273 and its bivalent booster candidate (mRNA1273.214), which includes an Omicron variant booster and mRNA-1273.

A version of this article first appeared on Medscape.com.

The use of an electronic clinical decision support tool called “ePNa” reduced severity-adjusted, 30-day, all-cause mortality by 38% across 16 community hospitals in Utah, compared with predeployment levels, a 3-year, pragmatic, cluster-controlled study shows.

“We designed the ePNa specifically to require minimal input from the clinician so everything it does is already in the electronic medical record,” Nathan Dean, MD, University of Utah, Salt Lake City, told this news organization.

“So it’s actually putting the guideline recommendations into effect for physicians so that they can make better decisions by having all this information – it’s a comprehensive best practice kind of tool where best practices are likely to make the biggest difference for patients with a high severity of illness,” he added.

The study was published online in the American Journal of Respiratory and Critical Care Medicine.


 

Guideline-based tool

The ePNa makes use of pneumonia guidelines of 2007 and 2019 from the American Thoracic Society/Infectious Disease Society of America. The system was deployed into six geographic clusters of 16 Intermountain hospital EDs at 2-month intervals between December 2017 and November 2018. Simultaneous deployment was impractical, as implementation of the tool takes education, monitoring, and feedback that can be facilitated by focusing on only a few hospitals at a time.

The decision support tool gathers key patient indicators including age, fever, oxygen saturation, vital signs, and laboratory and chest imaging results to offer recommendations on care, including appropriate antibiotic therapy, microbiology studies, and whether a given patient should be sent to the intensive care unit, admitted to hospital, or may safely be discharged home.

Investigators analyzed a total of 6,848 patients, of whom 4,536 were managed for pneumonia before the ePNa was deployed and 2,312 after deployment.

The median age of patients was 67 years (interquartile range, 50-79 years). Roughly half were female and almost all were White. “Observed 30-day all-cause mortality including both outpatients and inpatients was 8.6% before deployment versus 4.8% after deployment of ePNa,” Dr. Dean and colleagues reported.

Adjusted for severity of illness, the odds ratio for lower mortality post-ePNa launch was 0.62 (95% confidence interval, 0.49-0.79; P < .0010) “and lower morality was consistent across hospital clusters.”

Compared with patients who were discharged home, reductions in mortality were greatest in patients who were directly admitted to ICUs from the ED (OR, 0.32; 95% CI, 0.14-0.77; P = .01). The OR for patients admitted to the medical floor was 0.53 (95% CI, 0.25-1.1; P = .09), which did not reach statistical significance.

Dr. Dean explained that the reductions in mortality were seen among those with the most severe illness, in whom best practices would benefit the most. In contrast, patients who are sent home on an antibiotic are at low risk for mortality while patients admitted to the medical floor may well have another, more lethal illness from which they end up dying, rather than simple pneumonia.

“For me, this was a clear demonstration that these best practices made the biggest difference in patients who were sick and who did not have any underlying disease that was going to kill them anyway,” he emphasized. On the other hand, both 30-day mortality and 7-day secondary hospital admission were higher among patients the tool recommended for hospital ward admission but who were discharged home from the ED.

“This was an unexpected finding,” Dr. Dean observed. However, as he explained, the authors reviewed 25% of randomly selected patients who fell into this subgroup and discovered that the ePNa tool was used in only about 20% of patients – “so doctors did not use the tool in the majority of this group.”

In addition, some of these patients declined hospital admission, so the doctors may have recommended that they be admitted but the patients said no. “The hypothesis here is that if they had been admitted to the hospital, they may have had a lower mortality risk,” Dr. Dean said.
 

Noticeable changes

Another noticeable change following the introduction of the ePNa tool was that guideline-concordant antibiotic prescribing increased in the 8 hours after patients presented to the ED, from 79.5% prior to the tool’s launch to 87.9%, again after adjusting for pneumonia severity (P < .001). Use of broad-spectrum antibiotics was not significantly different between the two treatment intervals, but administration of antibiotics active against methicillin-resistant Staphylococcus aureus dropped significantly between the two treatment intervals (P < .001). And the mean time from admission to the ED to the first antibiotic taken was slightly faster, improving from 159.4 minutes (95% CI, 156.9-161.9 minutes) prior to the ePNa launch to 150.9 minutes (95% CI, 144.1-157.8) post deployment (P < .001).

“Overall outpatient disposition for treatment of pneumonia from the emergency department increased from 29.2% before ePNa to 46.9% [post ePNA],” the authors noted, while a similar increase was observed in patients for whom ePNA recommended outpatient care – from 49.2% pre-ePNA to 66.6% after ePNA.

Both hospital ward admission and admission to the ICU decreased after ePNa had been introduced. Despite a significant increase in the percentage of patients being discharged home, neither 7-day secondary hospital admission nor severity-adjusted, 30-day mortality were significantly different before versus after the introduction of ePNa, the authors stressed.

A limitation of the study was that the trial was confined to a single health care system in one region of the United States with a patient population that may differ from that in other regions.
 

Reason for its success

Asked to comment on the findings, Adam Balls, MD, emergency department chair, Intermountain Medical Center, Murray, Utah, suggested that the reason the ePNa tool has been so successful at improving care for pneumonia patients is that it puts the guidelines directly into the hands of individual providers and tells them what’s going on. (Dr. Balls was not involved in the study.) “The tool allows us to take into consideration various clinical features – a patient’s oxygen requirements and whether or not they had prior complicated pneumonias that required additional antibiotics, for example – and then it makes the best determination for not only the disposition for that patient but antibiotic treatment as well,” he said in an interview.

This then allows physicians to either appropriately discharge less severely ill patients and admit those who are more ill – “and in general, just do a better job of treating pneumonia with this tool,” Dr. Balls said. He himself uses the decision support tool when attending to his own patients with pneumonia, as he feels that the tool really does make his care of these patients better. “There is a disparity around how we treat pneumonia in the U.S.

“Clinicians sometimes have a bias or a preference for certain antibiotics and we may not be appropriately treating these patients with broad-spectrum antibiotics or are perhaps using antibiotics that are not as effective based on an individual patient scenario so this is definitely a user-friendly tool that hopefully can be deployed throughout other health care systems to improve the treatment of pneumonia overall,” Dr. Balls emphasized.

A version of this article first appeared on Medscape.com.

The use of an electronic clinical decision support tool called “ePNa” reduced severity-adjusted, 30-day, all-cause mortality by 38% across 16 community hospitals in Utah, compared with predeployment levels, a 3-year, pragmatic, cluster-controlled study shows.

“We designed the ePNa specifically to require minimal input from the clinician so everything it does is already in the electronic medical record,” Nathan Dean, MD, University of Utah, Salt Lake City, told this news organization.

“So it’s actually putting the guideline recommendations into effect for physicians so that they can make better decisions by having all this information – it’s a comprehensive best practice kind of tool where best practices are likely to make the biggest difference for patients with a high severity of illness,” he added.

The study was published online in the American Journal of Respiratory and Critical Care Medicine.


 

Guideline-based tool

The ePNa makes use of pneumonia guidelines of 2007 and 2019 from the American Thoracic Society/Infectious Disease Society of America. The system was deployed into six geographic clusters of 16 Intermountain hospital EDs at 2-month intervals between December 2017 and November 2018. Simultaneous deployment was impractical, as implementation of the tool takes education, monitoring, and feedback that can be facilitated by focusing on only a few hospitals at a time.

The decision support tool gathers key patient indicators including age, fever, oxygen saturation, vital signs, and laboratory and chest imaging results to offer recommendations on care, including appropriate antibiotic therapy, microbiology studies, and whether a given patient should be sent to the intensive care unit, admitted to hospital, or may safely be discharged home.

Investigators analyzed a total of 6,848 patients, of whom 4,536 were managed for pneumonia before the ePNa was deployed and 2,312 after deployment.

The median age of patients was 67 years (interquartile range, 50-79 years). Roughly half were female and almost all were White. “Observed 30-day all-cause mortality including both outpatients and inpatients was 8.6% before deployment versus 4.8% after deployment of ePNa,” Dr. Dean and colleagues reported.

Adjusted for severity of illness, the odds ratio for lower mortality post-ePNa launch was 0.62 (95% confidence interval, 0.49-0.79; P < .0010) “and lower morality was consistent across hospital clusters.”

Compared with patients who were discharged home, reductions in mortality were greatest in patients who were directly admitted to ICUs from the ED (OR, 0.32; 95% CI, 0.14-0.77; P = .01). The OR for patients admitted to the medical floor was 0.53 (95% CI, 0.25-1.1; P = .09), which did not reach statistical significance.

Dr. Dean explained that the reductions in mortality were seen among those with the most severe illness, in whom best practices would benefit the most. In contrast, patients who are sent home on an antibiotic are at low risk for mortality while patients admitted to the medical floor may well have another, more lethal illness from which they end up dying, rather than simple pneumonia.

“For me, this was a clear demonstration that these best practices made the biggest difference in patients who were sick and who did not have any underlying disease that was going to kill them anyway,” he emphasized. On the other hand, both 30-day mortality and 7-day secondary hospital admission were higher among patients the tool recommended for hospital ward admission but who were discharged home from the ED.

“This was an unexpected finding,” Dr. Dean observed. However, as he explained, the authors reviewed 25% of randomly selected patients who fell into this subgroup and discovered that the ePNa tool was used in only about 20% of patients – “so doctors did not use the tool in the majority of this group.”

In addition, some of these patients declined hospital admission, so the doctors may have recommended that they be admitted but the patients said no. “The hypothesis here is that if they had been admitted to the hospital, they may have had a lower mortality risk,” Dr. Dean said.
 

Noticeable changes

Another noticeable change following the introduction of the ePNa tool was that guideline-concordant antibiotic prescribing increased in the 8 hours after patients presented to the ED, from 79.5% prior to the tool’s launch to 87.9%, again after adjusting for pneumonia severity (P < .001). Use of broad-spectrum antibiotics was not significantly different between the two treatment intervals, but administration of antibiotics active against methicillin-resistant Staphylococcus aureus dropped significantly between the two treatment intervals (P < .001). And the mean time from admission to the ED to the first antibiotic taken was slightly faster, improving from 159.4 minutes (95% CI, 156.9-161.9 minutes) prior to the ePNa launch to 150.9 minutes (95% CI, 144.1-157.8) post deployment (P < .001).

“Overall outpatient disposition for treatment of pneumonia from the emergency department increased from 29.2% before ePNa to 46.9% [post ePNA],” the authors noted, while a similar increase was observed in patients for whom ePNA recommended outpatient care – from 49.2% pre-ePNA to 66.6% after ePNA.

Both hospital ward admission and admission to the ICU decreased after ePNa had been introduced. Despite a significant increase in the percentage of patients being discharged home, neither 7-day secondary hospital admission nor severity-adjusted, 30-day mortality were significantly different before versus after the introduction of ePNa, the authors stressed.

A limitation of the study was that the trial was confined to a single health care system in one region of the United States with a patient population that may differ from that in other regions.
 

Reason for its success

Asked to comment on the findings, Adam Balls, MD, emergency department chair, Intermountain Medical Center, Murray, Utah, suggested that the reason the ePNa tool has been so successful at improving care for pneumonia patients is that it puts the guidelines directly into the hands of individual providers and tells them what’s going on. (Dr. Balls was not involved in the study.) “The tool allows us to take into consideration various clinical features – a patient’s oxygen requirements and whether or not they had prior complicated pneumonias that required additional antibiotics, for example – and then it makes the best determination for not only the disposition for that patient but antibiotic treatment as well,” he said in an interview.

This then allows physicians to either appropriately discharge less severely ill patients and admit those who are more ill – “and in general, just do a better job of treating pneumonia with this tool,” Dr. Balls said. He himself uses the decision support tool when attending to his own patients with pneumonia, as he feels that the tool really does make his care of these patients better. “There is a disparity around how we treat pneumonia in the U.S.

“Clinicians sometimes have a bias or a preference for certain antibiotics and we may not be appropriately treating these patients with broad-spectrum antibiotics or are perhaps using antibiotics that are not as effective based on an individual patient scenario so this is definitely a user-friendly tool that hopefully can be deployed throughout other health care systems to improve the treatment of pneumonia overall,” Dr. Balls emphasized.

A version of this article first appeared on Medscape.com.

The use of an electronic clinical decision support tool called “ePNa” reduced severity-adjusted, 30-day, all-cause mortality by 38% across 16 community hospitals in Utah, compared with predeployment levels, a 3-year, pragmatic, cluster-controlled study shows.

“We designed the ePNa specifically to require minimal input from the clinician so everything it does is already in the electronic medical record,” Nathan Dean, MD, University of Utah, Salt Lake City, told this news organization.

“So it’s actually putting the guideline recommendations into effect for physicians so that they can make better decisions by having all this information – it’s a comprehensive best practice kind of tool where best practices are likely to make the biggest difference for patients with a high severity of illness,” he added.

The study was published online in the American Journal of Respiratory and Critical Care Medicine.


 

Guideline-based tool

The ePNa makes use of pneumonia guidelines of 2007 and 2019 from the American Thoracic Society/Infectious Disease Society of America. The system was deployed into six geographic clusters of 16 Intermountain hospital EDs at 2-month intervals between December 2017 and November 2018. Simultaneous deployment was impractical, as implementation of the tool takes education, monitoring, and feedback that can be facilitated by focusing on only a few hospitals at a time.

The decision support tool gathers key patient indicators including age, fever, oxygen saturation, vital signs, and laboratory and chest imaging results to offer recommendations on care, including appropriate antibiotic therapy, microbiology studies, and whether a given patient should be sent to the intensive care unit, admitted to hospital, or may safely be discharged home.

Investigators analyzed a total of 6,848 patients, of whom 4,536 were managed for pneumonia before the ePNa was deployed and 2,312 after deployment.

The median age of patients was 67 years (interquartile range, 50-79 years). Roughly half were female and almost all were White. “Observed 30-day all-cause mortality including both outpatients and inpatients was 8.6% before deployment versus 4.8% after deployment of ePNa,” Dr. Dean and colleagues reported.

Adjusted for severity of illness, the odds ratio for lower mortality post-ePNa launch was 0.62 (95% confidence interval, 0.49-0.79; P < .0010) “and lower morality was consistent across hospital clusters.”

Compared with patients who were discharged home, reductions in mortality were greatest in patients who were directly admitted to ICUs from the ED (OR, 0.32; 95% CI, 0.14-0.77; P = .01). The OR for patients admitted to the medical floor was 0.53 (95% CI, 0.25-1.1; P = .09), which did not reach statistical significance.

Dr. Dean explained that the reductions in mortality were seen among those with the most severe illness, in whom best practices would benefit the most. In contrast, patients who are sent home on an antibiotic are at low risk for mortality while patients admitted to the medical floor may well have another, more lethal illness from which they end up dying, rather than simple pneumonia.

“For me, this was a clear demonstration that these best practices made the biggest difference in patients who were sick and who did not have any underlying disease that was going to kill them anyway,” he emphasized. On the other hand, both 30-day mortality and 7-day secondary hospital admission were higher among patients the tool recommended for hospital ward admission but who were discharged home from the ED.

“This was an unexpected finding,” Dr. Dean observed. However, as he explained, the authors reviewed 25% of randomly selected patients who fell into this subgroup and discovered that the ePNa tool was used in only about 20% of patients – “so doctors did not use the tool in the majority of this group.”

In addition, some of these patients declined hospital admission, so the doctors may have recommended that they be admitted but the patients said no. “The hypothesis here is that if they had been admitted to the hospital, they may have had a lower mortality risk,” Dr. Dean said.
 

Noticeable changes

Another noticeable change following the introduction of the ePNa tool was that guideline-concordant antibiotic prescribing increased in the 8 hours after patients presented to the ED, from 79.5% prior to the tool’s launch to 87.9%, again after adjusting for pneumonia severity (P < .001). Use of broad-spectrum antibiotics was not significantly different between the two treatment intervals, but administration of antibiotics active against methicillin-resistant Staphylococcus aureus dropped significantly between the two treatment intervals (P < .001). And the mean time from admission to the ED to the first antibiotic taken was slightly faster, improving from 159.4 minutes (95% CI, 156.9-161.9 minutes) prior to the ePNa launch to 150.9 minutes (95% CI, 144.1-157.8) post deployment (P < .001).

“Overall outpatient disposition for treatment of pneumonia from the emergency department increased from 29.2% before ePNa to 46.9% [post ePNA],” the authors noted, while a similar increase was observed in patients for whom ePNA recommended outpatient care – from 49.2% pre-ePNA to 66.6% after ePNA.

Both hospital ward admission and admission to the ICU decreased after ePNa had been introduced. Despite a significant increase in the percentage of patients being discharged home, neither 7-day secondary hospital admission nor severity-adjusted, 30-day mortality were significantly different before versus after the introduction of ePNa, the authors stressed.

A limitation of the study was that the trial was confined to a single health care system in one region of the United States with a patient population that may differ from that in other regions.
 

Reason for its success

Asked to comment on the findings, Adam Balls, MD, emergency department chair, Intermountain Medical Center, Murray, Utah, suggested that the reason the ePNa tool has been so successful at improving care for pneumonia patients is that it puts the guidelines directly into the hands of individual providers and tells them what’s going on. (Dr. Balls was not involved in the study.) “The tool allows us to take into consideration various clinical features – a patient’s oxygen requirements and whether or not they had prior complicated pneumonias that required additional antibiotics, for example – and then it makes the best determination for not only the disposition for that patient but antibiotic treatment as well,” he said in an interview.

This then allows physicians to either appropriately discharge less severely ill patients and admit those who are more ill – “and in general, just do a better job of treating pneumonia with this tool,” Dr. Balls said. He himself uses the decision support tool when attending to his own patients with pneumonia, as he feels that the tool really does make his care of these patients better. “There is a disparity around how we treat pneumonia in the U.S.

“Clinicians sometimes have a bias or a preference for certain antibiotics and we may not be appropriately treating these patients with broad-spectrum antibiotics or are perhaps using antibiotics that are not as effective based on an individual patient scenario so this is definitely a user-friendly tool that hopefully can be deployed throughout other health care systems to improve the treatment of pneumonia overall,” Dr. Balls emphasized.

A version of this article first appeared on Medscape.com.