Neurology

Coffee’s ability to boost alertness is commonly attributed to caffeine, but new research suggests there may be other underlying mechanisms that explain this effect.

“There is a widespread anticipation that coffee boosts alertness and psychomotor performance. By gaining a deeper understanding of the mechanisms underlying this biological phenomenon, we pave the way for investigating the factors that can influence it and even exploring the potential advantages of those mechanisms,” study investigator Nuno Sousa, MD, PhD, with the University of Minho, Braga, Portugal, said in a statement.

The study was published online in Frontiers in Behavioral Neuroscience.
 

Caffeine can’t take all the credit

Certain compounds in coffee, including caffeine and chlorogenic acids, have well-documented psychoactive effects, but the psychological impact of coffee/caffeine consumption as a whole remains a matter of debate.

The researchers investigated the neurobiological impact of coffee drinking on brain connectivity using resting-state functional MRI (fMRI).

They recruited 47 generally healthy adults (mean age, 30 years; 31 women) who regularly drank a minimum of one cup of coffee per day. Participants refrained from eating or drinking caffeinated beverages for at least 3 hours prior to undergoing fMRI.

To tease out the specific impact of caffeinated coffee intake, 30 habitual coffee drinkers (mean age, 32 years; 27 women) were given hot water containing the same amount of caffeine, but they were not given coffee.

The investigators conducted two fMRI scans – one before, and one 30 minutes after drinking coffee or caffeine-infused water.

Both drinking coffee and drinking plain caffeine in water led to a decrease in functional connectivity of the brain’s default mode network, which is typically active during self-reflection in resting states.

This finding suggests that consuming either coffee or caffeine heightened individuals’ readiness to transition from a state of rest to engaging in task-related activities, the researchers noted.

However, drinking a cup of coffee also boosted connectivity in the higher visual network and the right executive control network, which are linked to working memory, cognitive control, and goal-directed behavior – something that did not occur from drinking caffeinated water.

“Put simply, individuals exhibited a heightened state of preparedness, being more responsive and attentive to external stimuli after drinking coffee,” said first author Maria Picó-Pérez, PhD, with the University of Minho.

Given that some of the effects of coffee also occurred with caffeine alone, it’s “plausible to assume that other caffeinated beverages may share similar effects,” she added.

Still, certain effects were specific to coffee drinking, “likely influenced by factors such as the distinct aroma and taste of coffee or the psychological expectations associated with consuming this particular beverage,” the researcher wrote.

The investigators report that the observations could provide a scientific foundation for the common belief that coffee increases alertness and cognitive functioning. Further research is needed to differentiate the effects of caffeine from the overall experience of drinking coffee.

A limitation of the study is the absence of a nondrinker control sample (to rule out the withdrawal effect) or an alternative group that consumed decaffeinated coffee (to rule out the placebo effect of coffee intake) – something that should be considered in future studies, the researchers noted.

The study was funded by the Institute for the Scientific Information on Coffee. The authors declared no relevant conflicts of interest.

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

Coffee’s ability to boost alertness is commonly attributed to caffeine, but new research suggests there may be other underlying mechanisms that explain this effect.

“There is a widespread anticipation that coffee boosts alertness and psychomotor performance. By gaining a deeper understanding of the mechanisms underlying this biological phenomenon, we pave the way for investigating the factors that can influence it and even exploring the potential advantages of those mechanisms,” study investigator Nuno Sousa, MD, PhD, with the University of Minho, Braga, Portugal, said in a statement.

The study was published online in Frontiers in Behavioral Neuroscience.
 

Caffeine can’t take all the credit

Certain compounds in coffee, including caffeine and chlorogenic acids, have well-documented psychoactive effects, but the psychological impact of coffee/caffeine consumption as a whole remains a matter of debate.

The researchers investigated the neurobiological impact of coffee drinking on brain connectivity using resting-state functional MRI (fMRI).

They recruited 47 generally healthy adults (mean age, 30 years; 31 women) who regularly drank a minimum of one cup of coffee per day. Participants refrained from eating or drinking caffeinated beverages for at least 3 hours prior to undergoing fMRI.

To tease out the specific impact of caffeinated coffee intake, 30 habitual coffee drinkers (mean age, 32 years; 27 women) were given hot water containing the same amount of caffeine, but they were not given coffee.

The investigators conducted two fMRI scans – one before, and one 30 minutes after drinking coffee or caffeine-infused water.

Both drinking coffee and drinking plain caffeine in water led to a decrease in functional connectivity of the brain’s default mode network, which is typically active during self-reflection in resting states.

This finding suggests that consuming either coffee or caffeine heightened individuals’ readiness to transition from a state of rest to engaging in task-related activities, the researchers noted.

However, drinking a cup of coffee also boosted connectivity in the higher visual network and the right executive control network, which are linked to working memory, cognitive control, and goal-directed behavior – something that did not occur from drinking caffeinated water.

“Put simply, individuals exhibited a heightened state of preparedness, being more responsive and attentive to external stimuli after drinking coffee,” said first author Maria Picó-Pérez, PhD, with the University of Minho.

Given that some of the effects of coffee also occurred with caffeine alone, it’s “plausible to assume that other caffeinated beverages may share similar effects,” she added.

Still, certain effects were specific to coffee drinking, “likely influenced by factors such as the distinct aroma and taste of coffee or the psychological expectations associated with consuming this particular beverage,” the researcher wrote.

The investigators report that the observations could provide a scientific foundation for the common belief that coffee increases alertness and cognitive functioning. Further research is needed to differentiate the effects of caffeine from the overall experience of drinking coffee.

A limitation of the study is the absence of a nondrinker control sample (to rule out the withdrawal effect) or an alternative group that consumed decaffeinated coffee (to rule out the placebo effect of coffee intake) – something that should be considered in future studies, the researchers noted.

The study was funded by the Institute for the Scientific Information on Coffee. The authors declared no relevant conflicts of interest.

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

Coffee’s ability to boost alertness is commonly attributed to caffeine, but new research suggests there may be other underlying mechanisms that explain this effect.

“There is a widespread anticipation that coffee boosts alertness and psychomotor performance. By gaining a deeper understanding of the mechanisms underlying this biological phenomenon, we pave the way for investigating the factors that can influence it and even exploring the potential advantages of those mechanisms,” study investigator Nuno Sousa, MD, PhD, with the University of Minho, Braga, Portugal, said in a statement.

The study was published online in Frontiers in Behavioral Neuroscience.
 

Caffeine can’t take all the credit

Certain compounds in coffee, including caffeine and chlorogenic acids, have well-documented psychoactive effects, but the psychological impact of coffee/caffeine consumption as a whole remains a matter of debate.

The researchers investigated the neurobiological impact of coffee drinking on brain connectivity using resting-state functional MRI (fMRI).

They recruited 47 generally healthy adults (mean age, 30 years; 31 women) who regularly drank a minimum of one cup of coffee per day. Participants refrained from eating or drinking caffeinated beverages for at least 3 hours prior to undergoing fMRI.

To tease out the specific impact of caffeinated coffee intake, 30 habitual coffee drinkers (mean age, 32 years; 27 women) were given hot water containing the same amount of caffeine, but they were not given coffee.

The investigators conducted two fMRI scans – one before, and one 30 minutes after drinking coffee or caffeine-infused water.

Both drinking coffee and drinking plain caffeine in water led to a decrease in functional connectivity of the brain’s default mode network, which is typically active during self-reflection in resting states.

This finding suggests that consuming either coffee or caffeine heightened individuals’ readiness to transition from a state of rest to engaging in task-related activities, the researchers noted.

However, drinking a cup of coffee also boosted connectivity in the higher visual network and the right executive control network, which are linked to working memory, cognitive control, and goal-directed behavior – something that did not occur from drinking caffeinated water.

“Put simply, individuals exhibited a heightened state of preparedness, being more responsive and attentive to external stimuli after drinking coffee,” said first author Maria Picó-Pérez, PhD, with the University of Minho.

Given that some of the effects of coffee also occurred with caffeine alone, it’s “plausible to assume that other caffeinated beverages may share similar effects,” she added.

Still, certain effects were specific to coffee drinking, “likely influenced by factors such as the distinct aroma and taste of coffee or the psychological expectations associated with consuming this particular beverage,” the researcher wrote.

The investigators report that the observations could provide a scientific foundation for the common belief that coffee increases alertness and cognitive functioning. Further research is needed to differentiate the effects of caffeine from the overall experience of drinking coffee.

A limitation of the study is the absence of a nondrinker control sample (to rule out the withdrawal effect) or an alternative group that consumed decaffeinated coffee (to rule out the placebo effect of coffee intake) – something that should be considered in future studies, the researchers noted.

The study was funded by the Institute for the Scientific Information on Coffee. The authors declared no relevant conflicts of interest.

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

A new European consensus statement offers expert guidance on which biomarkers to use for patients presenting with cognitive complaints.

Led by Giovanni B. Frisoni, MD, laboratory of neuroimaging of aging, University of Geneva, and director of the memory clinic at Geneva University Hospital, the multidisciplinary task force set out to define a patient-centered diagnostic workflow for the rational and cost-effective use of biomarkers in memory clinics.

The new algorithm is part of a consensus statement presented at the Congress of the European Academy of Neurology 2023. An interim update was published in June in Alzheimer’s and Dementia.
 

Which biomarker?

Many biomarkers can aid diagnosis, said Dr. Frisoni; the challenge is choosing which biomarker to use for an individual patient.

A literature-based search, he said, yields a number of recommendations, but the vast majority of these are either disease based or biomarker based. The task force notes that “in vivo biomarkers enable early etiological diagnosis of neurocognitive disorders. While they have good analytical validity, their clinical validity and utility are uncertain.”

“When you have a patient in front of you, you don’ t know whether they have Alzheimer’s disease,” Dr. Frisoni said.

“You have a differential diagnosis to make, and you have a number of biomarkers – a number of weapons in your armamentarium – you have to choose. You can’t use all of them – we would like to, but we cannot.”

He added that trying to determine from the literature which biomarker is most appropriate given individual clinical conditions and all of the potential combinations is impossible.

“You will not find evidence of the comparative diagnostic value and the added diagnostic value” of one test vs, another, he noted.

“Is CSF [cerebrospinal fluid] better than amyloid PET in a particular clinical situation? What do I gain in terms of positive and negative predictive value in all the possible clinical conditions that I encounter in my clinical practice?”

Dr. Frisoni said the reality is that clinicians in memory clinics end up using biomarkers that are “based on clinical opportunities.”

For instance, “if you have a proficient nuclear medic, you use PET a lot.” In contrast, “if you have a proficient laboratory medic,” CSF markers will be favored – a situation that he said is “not ideal” and has resulted in large discrepancies in diagnostic approaches across Europe.
 

Harmonizing clinical practice

In a bid to harmonize clinical practice, 22 European experts from 11 European scientific societies and the executive director of Alzheimer Europe set out to develop a multidisciplinary consensus algorithm for the biomarker-based diagnosis of neurocognitive disorders in general, rather than specific neurocognitive disorders.

They used the Delphi method, in which a systematic literature review of the literature was followed by the drafting of a series of clinical statements by an executive board. These were then presented to the expert panel. If a majority consensus was reached on a given statement, it was considered closed. Questions for which there was no consensus were revised and presented to the panel again. The process was repeated until a consensus was reached.

A total of 56 statements underwent six rounds of discussion. A final online meeting led to the development of a diagnostic algorithm for patients who attend memory clinics for cognitive complaints.

The algorithm features three potential assessment waves. Wave 1 defines 11 clinical profiles that are based on the results of clinical and neuropsychological assessments, blood exams, brain imaging, and, in specific cases, electroencephalography. Wave 2 defines first-line biomarkers based on Wave 1 clinical profiles, and Wave 3 defines the second-line biomarker based on Wave 2 biomarker results.

When a patient’s clinical profile suggests Alzheimer’s disease and, in undefined cases, cerebrospinal fluid biomarkers are used first line. When CSF is inconclusive, 18-fluorodeoxyglucose positron emission tomography (FDG-PET) is used second line.

When the clinical profile suggests frontotemporal lobar degeneration or motor tauopathies, FDG-PET is first line and CSF biomarkers second line in atypical metabolic patter cases. When the clinical profile suggests Lewy body disease, dopamine transporter SPECT is first line and cardia I23I-metaiodobenzylguanidine scintigraphy is second line.

Dr. Frisoni noted that the panel strongly recommends performing biomarker tests for patients younger than 70. For those aged 70-85 years, biomarker testing is only recommended for patients with specific clinical features. For patients older than 85, biomarker testing is recommended only in “exceptional circumstances.”

Dr. Frisoni noted that the consensus document has a number of limitations.

“First of all, we could not capture all the theoretical possible combinations” of potential diagnosis and relevant biomarker tests. “There are so many that it’s virtually impossible.”

He also noted that the agreement among the panel for the use of some markers was “relatively low” at “barely 50%,” while for others, the agreement was approximately 70%.

The consensus document also does not explicitly address patients with “mixed pathologies,” which are common. In addition, it does not include emerging biomarkers, such as neurofilament light polypeptide levels, an indicator of axonal compromise.

“Last, but not least,” Dr. Frisoni said, the consensus document requires validation.

“This is a paper and pencil exercise. We, as self-appointed experts, can recommend ... whatever we want, but we must check whether what we write is applicable, feasible.”

In other words, it must be determined whether the “real patient journey” fits with the “ideal patient journey” set out in the consensus document.

This kind of validation, Dr. Frisoni said, is “usually not done for this type of exercise,” but “we want to do it in this case.”
 

Pros and cons

Bogdan Draganski, MD, consultant in neurology at the department of clinical neurosciences and director of the neuroimaging research laboratory, University Hospital of Lausanne (Switzerland), who cochaired the session, told this news organization that he was “swaying between two extremes” when considering the usefulness of the consensus document.

On one hand, the “reductionist approach” of breaking down a “complex issue into an algorithm” via the Delphi method risks introducing subjective bias.

He said machine learning and artificial intelligence could answer some of the questions posed by clinicians and, by extension, the statements included in the Delphi process by assessing the available data in a more objective manner.

On the other hand, Dr. Draganski said that reducing the options available to clinicians when making a differential diagnosis into the current algorithm is, pragmatically speaking, a “good approach.”

From this standpoint, the danger of using machine learning to answer clinical questions is that it “doesn’t take the responsibility” for the final decision, which means “we’re closing the loop of subjective decision-making for an individual doctor.”

He also applauded the idea of trying to provide more uniform patient assessment across Europe, although he believes “we have a long way to go” before it can deliver on the promise of personalized medicine.

Like Dr. Frisoni, Dr. Draganski noted the fact that patients with potential neurocognitive disorders often have multiple pathologies, which can include cardiovascular problems, depression, and cancer and that that could affect the choice of diagnostic biomarkers.

The second issue, he said, concerns implementation of the consensus document, which is a political decision that centers around “how politicians will define ‘uniformity’ and equal access to technological or nontechnological platforms.”

Achieving uniformity will require a pan-regional collaboration, he noted.

The task force was supported by unrestricted grants from F. Hoffmann-La Roche, Biogen International GmbH, Eisai Europe Limited, Life Molecular Imaging GmbH, and OM Pharma Suisse SA. The authors have disclosed no relevant financial relationships.

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

A new European consensus statement offers expert guidance on which biomarkers to use for patients presenting with cognitive complaints.

Led by Giovanni B. Frisoni, MD, laboratory of neuroimaging of aging, University of Geneva, and director of the memory clinic at Geneva University Hospital, the multidisciplinary task force set out to define a patient-centered diagnostic workflow for the rational and cost-effective use of biomarkers in memory clinics.

The new algorithm is part of a consensus statement presented at the Congress of the European Academy of Neurology 2023. An interim update was published in June in Alzheimer’s and Dementia.
 

Which biomarker?

Many biomarkers can aid diagnosis, said Dr. Frisoni; the challenge is choosing which biomarker to use for an individual patient.

A literature-based search, he said, yields a number of recommendations, but the vast majority of these are either disease based or biomarker based. The task force notes that “in vivo biomarkers enable early etiological diagnosis of neurocognitive disorders. While they have good analytical validity, their clinical validity and utility are uncertain.”

“When you have a patient in front of you, you don’ t know whether they have Alzheimer’s disease,” Dr. Frisoni said.

“You have a differential diagnosis to make, and you have a number of biomarkers – a number of weapons in your armamentarium – you have to choose. You can’t use all of them – we would like to, but we cannot.”

He added that trying to determine from the literature which biomarker is most appropriate given individual clinical conditions and all of the potential combinations is impossible.

“You will not find evidence of the comparative diagnostic value and the added diagnostic value” of one test vs, another, he noted.

“Is CSF [cerebrospinal fluid] better than amyloid PET in a particular clinical situation? What do I gain in terms of positive and negative predictive value in all the possible clinical conditions that I encounter in my clinical practice?”

Dr. Frisoni said the reality is that clinicians in memory clinics end up using biomarkers that are “based on clinical opportunities.”

For instance, “if you have a proficient nuclear medic, you use PET a lot.” In contrast, “if you have a proficient laboratory medic,” CSF markers will be favored – a situation that he said is “not ideal” and has resulted in large discrepancies in diagnostic approaches across Europe.
 

Harmonizing clinical practice

In a bid to harmonize clinical practice, 22 European experts from 11 European scientific societies and the executive director of Alzheimer Europe set out to develop a multidisciplinary consensus algorithm for the biomarker-based diagnosis of neurocognitive disorders in general, rather than specific neurocognitive disorders.

They used the Delphi method, in which a systematic literature review of the literature was followed by the drafting of a series of clinical statements by an executive board. These were then presented to the expert panel. If a majority consensus was reached on a given statement, it was considered closed. Questions for which there was no consensus were revised and presented to the panel again. The process was repeated until a consensus was reached.

A total of 56 statements underwent six rounds of discussion. A final online meeting led to the development of a diagnostic algorithm for patients who attend memory clinics for cognitive complaints.

The algorithm features three potential assessment waves. Wave 1 defines 11 clinical profiles that are based on the results of clinical and neuropsychological assessments, blood exams, brain imaging, and, in specific cases, electroencephalography. Wave 2 defines first-line biomarkers based on Wave 1 clinical profiles, and Wave 3 defines the second-line biomarker based on Wave 2 biomarker results.

When a patient’s clinical profile suggests Alzheimer’s disease and, in undefined cases, cerebrospinal fluid biomarkers are used first line. When CSF is inconclusive, 18-fluorodeoxyglucose positron emission tomography (FDG-PET) is used second line.

When the clinical profile suggests frontotemporal lobar degeneration or motor tauopathies, FDG-PET is first line and CSF biomarkers second line in atypical metabolic patter cases. When the clinical profile suggests Lewy body disease, dopamine transporter SPECT is first line and cardia I23I-metaiodobenzylguanidine scintigraphy is second line.

Dr. Frisoni noted that the panel strongly recommends performing biomarker tests for patients younger than 70. For those aged 70-85 years, biomarker testing is only recommended for patients with specific clinical features. For patients older than 85, biomarker testing is recommended only in “exceptional circumstances.”

Dr. Frisoni noted that the consensus document has a number of limitations.

“First of all, we could not capture all the theoretical possible combinations” of potential diagnosis and relevant biomarker tests. “There are so many that it’s virtually impossible.”

He also noted that the agreement among the panel for the use of some markers was “relatively low” at “barely 50%,” while for others, the agreement was approximately 70%.

The consensus document also does not explicitly address patients with “mixed pathologies,” which are common. In addition, it does not include emerging biomarkers, such as neurofilament light polypeptide levels, an indicator of axonal compromise.

“Last, but not least,” Dr. Frisoni said, the consensus document requires validation.

“This is a paper and pencil exercise. We, as self-appointed experts, can recommend ... whatever we want, but we must check whether what we write is applicable, feasible.”

In other words, it must be determined whether the “real patient journey” fits with the “ideal patient journey” set out in the consensus document.

This kind of validation, Dr. Frisoni said, is “usually not done for this type of exercise,” but “we want to do it in this case.”
 

Pros and cons

Bogdan Draganski, MD, consultant in neurology at the department of clinical neurosciences and director of the neuroimaging research laboratory, University Hospital of Lausanne (Switzerland), who cochaired the session, told this news organization that he was “swaying between two extremes” when considering the usefulness of the consensus document.

On one hand, the “reductionist approach” of breaking down a “complex issue into an algorithm” via the Delphi method risks introducing subjective bias.

He said machine learning and artificial intelligence could answer some of the questions posed by clinicians and, by extension, the statements included in the Delphi process by assessing the available data in a more objective manner.

On the other hand, Dr. Draganski said that reducing the options available to clinicians when making a differential diagnosis into the current algorithm is, pragmatically speaking, a “good approach.”

From this standpoint, the danger of using machine learning to answer clinical questions is that it “doesn’t take the responsibility” for the final decision, which means “we’re closing the loop of subjective decision-making for an individual doctor.”

He also applauded the idea of trying to provide more uniform patient assessment across Europe, although he believes “we have a long way to go” before it can deliver on the promise of personalized medicine.

Like Dr. Frisoni, Dr. Draganski noted the fact that patients with potential neurocognitive disorders often have multiple pathologies, which can include cardiovascular problems, depression, and cancer and that that could affect the choice of diagnostic biomarkers.

The second issue, he said, concerns implementation of the consensus document, which is a political decision that centers around “how politicians will define ‘uniformity’ and equal access to technological or nontechnological platforms.”

Achieving uniformity will require a pan-regional collaboration, he noted.

The task force was supported by unrestricted grants from F. Hoffmann-La Roche, Biogen International GmbH, Eisai Europe Limited, Life Molecular Imaging GmbH, and OM Pharma Suisse SA. The authors have disclosed no relevant financial relationships.

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

A new European consensus statement offers expert guidance on which biomarkers to use for patients presenting with cognitive complaints.

Led by Giovanni B. Frisoni, MD, laboratory of neuroimaging of aging, University of Geneva, and director of the memory clinic at Geneva University Hospital, the multidisciplinary task force set out to define a patient-centered diagnostic workflow for the rational and cost-effective use of biomarkers in memory clinics.

The new algorithm is part of a consensus statement presented at the Congress of the European Academy of Neurology 2023. An interim update was published in June in Alzheimer’s and Dementia.
 

Which biomarker?

Many biomarkers can aid diagnosis, said Dr. Frisoni; the challenge is choosing which biomarker to use for an individual patient.

A literature-based search, he said, yields a number of recommendations, but the vast majority of these are either disease based or biomarker based. The task force notes that “in vivo biomarkers enable early etiological diagnosis of neurocognitive disorders. While they have good analytical validity, their clinical validity and utility are uncertain.”

“When you have a patient in front of you, you don’ t know whether they have Alzheimer’s disease,” Dr. Frisoni said.

“You have a differential diagnosis to make, and you have a number of biomarkers – a number of weapons in your armamentarium – you have to choose. You can’t use all of them – we would like to, but we cannot.”

He added that trying to determine from the literature which biomarker is most appropriate given individual clinical conditions and all of the potential combinations is impossible.

“You will not find evidence of the comparative diagnostic value and the added diagnostic value” of one test vs, another, he noted.

“Is CSF [cerebrospinal fluid] better than amyloid PET in a particular clinical situation? What do I gain in terms of positive and negative predictive value in all the possible clinical conditions that I encounter in my clinical practice?”

Dr. Frisoni said the reality is that clinicians in memory clinics end up using biomarkers that are “based on clinical opportunities.”

For instance, “if you have a proficient nuclear medic, you use PET a lot.” In contrast, “if you have a proficient laboratory medic,” CSF markers will be favored – a situation that he said is “not ideal” and has resulted in large discrepancies in diagnostic approaches across Europe.
 

Harmonizing clinical practice

In a bid to harmonize clinical practice, 22 European experts from 11 European scientific societies and the executive director of Alzheimer Europe set out to develop a multidisciplinary consensus algorithm for the biomarker-based diagnosis of neurocognitive disorders in general, rather than specific neurocognitive disorders.

They used the Delphi method, in which a systematic literature review of the literature was followed by the drafting of a series of clinical statements by an executive board. These were then presented to the expert panel. If a majority consensus was reached on a given statement, it was considered closed. Questions for which there was no consensus were revised and presented to the panel again. The process was repeated until a consensus was reached.

A total of 56 statements underwent six rounds of discussion. A final online meeting led to the development of a diagnostic algorithm for patients who attend memory clinics for cognitive complaints.

The algorithm features three potential assessment waves. Wave 1 defines 11 clinical profiles that are based on the results of clinical and neuropsychological assessments, blood exams, brain imaging, and, in specific cases, electroencephalography. Wave 2 defines first-line biomarkers based on Wave 1 clinical profiles, and Wave 3 defines the second-line biomarker based on Wave 2 biomarker results.

When a patient’s clinical profile suggests Alzheimer’s disease and, in undefined cases, cerebrospinal fluid biomarkers are used first line. When CSF is inconclusive, 18-fluorodeoxyglucose positron emission tomography (FDG-PET) is used second line.

When the clinical profile suggests frontotemporal lobar degeneration or motor tauopathies, FDG-PET is first line and CSF biomarkers second line in atypical metabolic patter cases. When the clinical profile suggests Lewy body disease, dopamine transporter SPECT is first line and cardia I23I-metaiodobenzylguanidine scintigraphy is second line.

Dr. Frisoni noted that the panel strongly recommends performing biomarker tests for patients younger than 70. For those aged 70-85 years, biomarker testing is only recommended for patients with specific clinical features. For patients older than 85, biomarker testing is recommended only in “exceptional circumstances.”

Dr. Frisoni noted that the consensus document has a number of limitations.

“First of all, we could not capture all the theoretical possible combinations” of potential diagnosis and relevant biomarker tests. “There are so many that it’s virtually impossible.”

He also noted that the agreement among the panel for the use of some markers was “relatively low” at “barely 50%,” while for others, the agreement was approximately 70%.

The consensus document also does not explicitly address patients with “mixed pathologies,” which are common. In addition, it does not include emerging biomarkers, such as neurofilament light polypeptide levels, an indicator of axonal compromise.

“Last, but not least,” Dr. Frisoni said, the consensus document requires validation.

“This is a paper and pencil exercise. We, as self-appointed experts, can recommend ... whatever we want, but we must check whether what we write is applicable, feasible.”

In other words, it must be determined whether the “real patient journey” fits with the “ideal patient journey” set out in the consensus document.

This kind of validation, Dr. Frisoni said, is “usually not done for this type of exercise,” but “we want to do it in this case.”
 

Pros and cons

Bogdan Draganski, MD, consultant in neurology at the department of clinical neurosciences and director of the neuroimaging research laboratory, University Hospital of Lausanne (Switzerland), who cochaired the session, told this news organization that he was “swaying between two extremes” when considering the usefulness of the consensus document.

On one hand, the “reductionist approach” of breaking down a “complex issue into an algorithm” via the Delphi method risks introducing subjective bias.

He said machine learning and artificial intelligence could answer some of the questions posed by clinicians and, by extension, the statements included in the Delphi process by assessing the available data in a more objective manner.

On the other hand, Dr. Draganski said that reducing the options available to clinicians when making a differential diagnosis into the current algorithm is, pragmatically speaking, a “good approach.”

From this standpoint, the danger of using machine learning to answer clinical questions is that it “doesn’t take the responsibility” for the final decision, which means “we’re closing the loop of subjective decision-making for an individual doctor.”

He also applauded the idea of trying to provide more uniform patient assessment across Europe, although he believes “we have a long way to go” before it can deliver on the promise of personalized medicine.

Like Dr. Frisoni, Dr. Draganski noted the fact that patients with potential neurocognitive disorders often have multiple pathologies, which can include cardiovascular problems, depression, and cancer and that that could affect the choice of diagnostic biomarkers.

The second issue, he said, concerns implementation of the consensus document, which is a political decision that centers around “how politicians will define ‘uniformity’ and equal access to technological or nontechnological platforms.”

Achieving uniformity will require a pan-regional collaboration, he noted.

The task force was supported by unrestricted grants from F. Hoffmann-La Roche, Biogen International GmbH, Eisai Europe Limited, Life Molecular Imaging GmbH, and OM Pharma Suisse SA. The authors have disclosed no relevant financial relationships.

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

Kate Whitley was petrified of COVID-19 from the beginning of the pandemic because she has Hashimoto disease, an autoimmune disorder that she knew put her at high risk for complications.

She was right to be worried. Two months after contracting the infection in September 2022, the 42-year-old Nashville resident was diagnosed with long COVID. For Ms. Whitley, the resulting brain fog has been the most challenging factor. She is the owner of a successful paper goods store, and she can’t remember basic aspects of her job. She can’t tolerate loud noises and gets so distracted that she has trouble remembering what she was doing.

Ms. Whitley doesn’t like the term “brain fog” because it doesn’t begin to describe the dramatic disruption to her life over the past 7 months.

“I just can’t think anymore,” she said. “It makes you realize that you’re nothing without your brain. Sometimes I feel like a shell of my former self.”

Brain fog is among the most common symptoms of long COVID, and also one of the most poorly understood. A reported 46% of those diagnosed with long COVID complain of brain fog or a loss of memory. Many clinicians agree that the term is vague and often doesn’t truly represent the condition. That, in turn, makes it harder for doctors to diagnose and treat it. There are no standard tests for it, nor are there guidelines for symptom management or treatment.

“There’s a lot of imprecision in the term because it might mean different things to different patients,” said James C. Jackson, PsyD, a neuropsychiatrist at Vanderbilt University, Nashville, Tenn., and author of a new book, “Clearing the Fog: From Surviving to Thriving With Long COVID – A Practical Guide.”

Dr. Jackson, who began treating Ms. Whitley in February 2023, said that it makes more sense to call brain fog a brain impairment or an acquired brain injury (ABI) because it doesn’t occur gradually. COVID damages the brain and causes injury. For those with long COVID who were previously in the intensive care unit and may have undergone ventilation, hypoxic brain injury may result from the lack of oxygen to the brain.

Even among those with milder cases of acute COVID, there’s some evidence that persistent neuroinflammation in the brain caused by an activated immune system may also cause damage.

In both cases, the results can be debilitating. Ms. Whitley also has dysautonomia – a disorder of the autonomic nervous system that can cause dizziness, sweating, and headaches along with fatigue and heart palpitations.

She said that she’s so forgetful that when she sees people socially, she’s nervous of what she’ll say. “I feel like I’m constantly sticking my foot in my mouth because I can’t remember details of other people’s lives,” she said.

Although brain disorders such as Alzheimer’s disease and other forms of dementia are marked by a slow decline, ABI occurs more suddenly and may include a loss of executive function and attention.

“With a brain injury, you’re doing fine, and then some event happens (in this case COVID), and immediately after that, your cognitive function is different,” said Dr. Jackson.

Additionally, ABI is an actual diagnosis, whereas brain fog is not.

“With a brain injury, there’s a treatment pathway for cognitive rehabilitation,” said Dr. Jackson.

Treatments may include speech, cognitive, and occupational therapy as well as meeting with a neuropsychiatrist for treatment of the mental and behavioral disorders that may result. Dr. Jackson said that while many patients aren’t functioning cognitively or physically at 100%, they can make enough strides that they don’t have to give up things such as driving and, in some cases, their jobs.

Other experts agree that long COVID may damage the brain. An April 2022 study published in the journal Nature found strong evidence that SARS-CoV-2 infection may cause brain-related abnormalities, for example, a reduction in gray matter in certain parts of the brain, including the prefrontal cortex, hypothalamus, and amygdala.

Additionally, white matter, which is found deeper in the brain and is responsible for the exchange of information between different parts of the brain, may also be at risk of damage as a result of the virus, according to a November 2022 study published in the journal SN Comprehensive Clinical Medicine.

Calling it a “fog” makes it easier for clinicians and the general public to dismiss its severity, said Tyler Reed Bell, PhD, a researcher who specializes in viruses that cause brain injury. He is a fellow in the department of psychiatry at the University of California, San Diego. Brain fog can make driving and returning to work especially dangerous. Because of difficulty focusing, patients are much more likely to make mistakes that cause accidents.

“The COVID virus is very invasive to the brain,” Dr. Bell said.

Others contend this may be a rush to judgment. Karla L. Thompson, PhD, lead neuropsychologist at the University of North Carolina at Chapel Hill’s COVID Recovery Clinic, agrees that in more serious cases of COVID that cause a lack of oxygen to the brain, it’s reasonable to call it a brain injury. But brain fog can also be associated with other long COVID symptoms, not just damage to the brain.

Chronic fatigue and poor sleep are both commonly reported symptoms of long COVID that negatively affect brain function, she said. Sleep disturbances, cardiac problems, dysautonomia, and emotional distress could also affect the way the brain functions post COVID. Finding the right treatment requires identifying all the factors contributing to cognitive impairment.

Part of the problem in treating long COVID brain fog is that diagnostic technology is not sensitive enough to detect inflammation that could be causing damage.

Grace McComsey, MD, who leads the long COVID RECOVER study at University Hospitals Health System in Cleveland, said her team is working on identifying biomarkers that could detect brain inflammation in a way similar to the manner researchers have identified biomarkers to help diagnose chronic fatigue syndrome. Additionally, a new study published last month in JAMA for the first time clearly defined 12 symptoms of long COVID, and brain fog was listed among them. All of this contributes to the development of clear diagnostic criteria.

“It will make a big difference once we have some consistency among clinicians in diagnosing the condition,” said Dr. McComsey.

Ms. Whitley is thankful for the treatment that she’s received thus far. She’s seeing a cognitive rehabilitation therapist, who assesses her memory, cognition, and attention span and gives her tools to break up simple tasks, such as driving, so that they don’t feel overwhelming. She’s back behind the wheel and back to work.

But perhaps most importantly, Ms. Whitley joined a support group, led by Dr. Jackson, that includes other people experiencing the same symptoms she is. When she was at her darkest, they understood.

“Talking to other survivors has been the only solace in all this,” Ms. Whitley said. “Together, we grieve all that’s been lost.”

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

Kate Whitley was petrified of COVID-19 from the beginning of the pandemic because she has Hashimoto disease, an autoimmune disorder that she knew put her at high risk for complications.

She was right to be worried. Two months after contracting the infection in September 2022, the 42-year-old Nashville resident was diagnosed with long COVID. For Ms. Whitley, the resulting brain fog has been the most challenging factor. She is the owner of a successful paper goods store, and she can’t remember basic aspects of her job. She can’t tolerate loud noises and gets so distracted that she has trouble remembering what she was doing.

Ms. Whitley doesn’t like the term “brain fog” because it doesn’t begin to describe the dramatic disruption to her life over the past 7 months.

“I just can’t think anymore,” she said. “It makes you realize that you’re nothing without your brain. Sometimes I feel like a shell of my former self.”

Brain fog is among the most common symptoms of long COVID, and also one of the most poorly understood. A reported 46% of those diagnosed with long COVID complain of brain fog or a loss of memory. Many clinicians agree that the term is vague and often doesn’t truly represent the condition. That, in turn, makes it harder for doctors to diagnose and treat it. There are no standard tests for it, nor are there guidelines for symptom management or treatment.

“There’s a lot of imprecision in the term because it might mean different things to different patients,” said James C. Jackson, PsyD, a neuropsychiatrist at Vanderbilt University, Nashville, Tenn., and author of a new book, “Clearing the Fog: From Surviving to Thriving With Long COVID – A Practical Guide.”

Dr. Jackson, who began treating Ms. Whitley in February 2023, said that it makes more sense to call brain fog a brain impairment or an acquired brain injury (ABI) because it doesn’t occur gradually. COVID damages the brain and causes injury. For those with long COVID who were previously in the intensive care unit and may have undergone ventilation, hypoxic brain injury may result from the lack of oxygen to the brain.

Even among those with milder cases of acute COVID, there’s some evidence that persistent neuroinflammation in the brain caused by an activated immune system may also cause damage.

In both cases, the results can be debilitating. Ms. Whitley also has dysautonomia – a disorder of the autonomic nervous system that can cause dizziness, sweating, and headaches along with fatigue and heart palpitations.

She said that she’s so forgetful that when she sees people socially, she’s nervous of what she’ll say. “I feel like I’m constantly sticking my foot in my mouth because I can’t remember details of other people’s lives,” she said.

Although brain disorders such as Alzheimer’s disease and other forms of dementia are marked by a slow decline, ABI occurs more suddenly and may include a loss of executive function and attention.

“With a brain injury, you’re doing fine, and then some event happens (in this case COVID), and immediately after that, your cognitive function is different,” said Dr. Jackson.

Additionally, ABI is an actual diagnosis, whereas brain fog is not.

“With a brain injury, there’s a treatment pathway for cognitive rehabilitation,” said Dr. Jackson.

Treatments may include speech, cognitive, and occupational therapy as well as meeting with a neuropsychiatrist for treatment of the mental and behavioral disorders that may result. Dr. Jackson said that while many patients aren’t functioning cognitively or physically at 100%, they can make enough strides that they don’t have to give up things such as driving and, in some cases, their jobs.

Other experts agree that long COVID may damage the brain. An April 2022 study published in the journal Nature found strong evidence that SARS-CoV-2 infection may cause brain-related abnormalities, for example, a reduction in gray matter in certain parts of the brain, including the prefrontal cortex, hypothalamus, and amygdala.

Additionally, white matter, which is found deeper in the brain and is responsible for the exchange of information between different parts of the brain, may also be at risk of damage as a result of the virus, according to a November 2022 study published in the journal SN Comprehensive Clinical Medicine.

Calling it a “fog” makes it easier for clinicians and the general public to dismiss its severity, said Tyler Reed Bell, PhD, a researcher who specializes in viruses that cause brain injury. He is a fellow in the department of psychiatry at the University of California, San Diego. Brain fog can make driving and returning to work especially dangerous. Because of difficulty focusing, patients are much more likely to make mistakes that cause accidents.

“The COVID virus is very invasive to the brain,” Dr. Bell said.

Others contend this may be a rush to judgment. Karla L. Thompson, PhD, lead neuropsychologist at the University of North Carolina at Chapel Hill’s COVID Recovery Clinic, agrees that in more serious cases of COVID that cause a lack of oxygen to the brain, it’s reasonable to call it a brain injury. But brain fog can also be associated with other long COVID symptoms, not just damage to the brain.

Chronic fatigue and poor sleep are both commonly reported symptoms of long COVID that negatively affect brain function, she said. Sleep disturbances, cardiac problems, dysautonomia, and emotional distress could also affect the way the brain functions post COVID. Finding the right treatment requires identifying all the factors contributing to cognitive impairment.

Part of the problem in treating long COVID brain fog is that diagnostic technology is not sensitive enough to detect inflammation that could be causing damage.

Grace McComsey, MD, who leads the long COVID RECOVER study at University Hospitals Health System in Cleveland, said her team is working on identifying biomarkers that could detect brain inflammation in a way similar to the manner researchers have identified biomarkers to help diagnose chronic fatigue syndrome. Additionally, a new study published last month in JAMA for the first time clearly defined 12 symptoms of long COVID, and brain fog was listed among them. All of this contributes to the development of clear diagnostic criteria.

“It will make a big difference once we have some consistency among clinicians in diagnosing the condition,” said Dr. McComsey.

Ms. Whitley is thankful for the treatment that she’s received thus far. She’s seeing a cognitive rehabilitation therapist, who assesses her memory, cognition, and attention span and gives her tools to break up simple tasks, such as driving, so that they don’t feel overwhelming. She’s back behind the wheel and back to work.

But perhaps most importantly, Ms. Whitley joined a support group, led by Dr. Jackson, that includes other people experiencing the same symptoms she is. When she was at her darkest, they understood.

“Talking to other survivors has been the only solace in all this,” Ms. Whitley said. “Together, we grieve all that’s been lost.”

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

Kate Whitley was petrified of COVID-19 from the beginning of the pandemic because she has Hashimoto disease, an autoimmune disorder that she knew put her at high risk for complications.

She was right to be worried. Two months after contracting the infection in September 2022, the 42-year-old Nashville resident was diagnosed with long COVID. For Ms. Whitley, the resulting brain fog has been the most challenging factor. She is the owner of a successful paper goods store, and she can’t remember basic aspects of her job. She can’t tolerate loud noises and gets so distracted that she has trouble remembering what she was doing.

Ms. Whitley doesn’t like the term “brain fog” because it doesn’t begin to describe the dramatic disruption to her life over the past 7 months.

“I just can’t think anymore,” she said. “It makes you realize that you’re nothing without your brain. Sometimes I feel like a shell of my former self.”

Brain fog is among the most common symptoms of long COVID, and also one of the most poorly understood. A reported 46% of those diagnosed with long COVID complain of brain fog or a loss of memory. Many clinicians agree that the term is vague and often doesn’t truly represent the condition. That, in turn, makes it harder for doctors to diagnose and treat it. There are no standard tests for it, nor are there guidelines for symptom management or treatment.

“There’s a lot of imprecision in the term because it might mean different things to different patients,” said James C. Jackson, PsyD, a neuropsychiatrist at Vanderbilt University, Nashville, Tenn., and author of a new book, “Clearing the Fog: From Surviving to Thriving With Long COVID – A Practical Guide.”

Dr. Jackson, who began treating Ms. Whitley in February 2023, said that it makes more sense to call brain fog a brain impairment or an acquired brain injury (ABI) because it doesn’t occur gradually. COVID damages the brain and causes injury. For those with long COVID who were previously in the intensive care unit and may have undergone ventilation, hypoxic brain injury may result from the lack of oxygen to the brain.

Even among those with milder cases of acute COVID, there’s some evidence that persistent neuroinflammation in the brain caused by an activated immune system may also cause damage.

In both cases, the results can be debilitating. Ms. Whitley also has dysautonomia – a disorder of the autonomic nervous system that can cause dizziness, sweating, and headaches along with fatigue and heart palpitations.

She said that she’s so forgetful that when she sees people socially, she’s nervous of what she’ll say. “I feel like I’m constantly sticking my foot in my mouth because I can’t remember details of other people’s lives,” she said.

Although brain disorders such as Alzheimer’s disease and other forms of dementia are marked by a slow decline, ABI occurs more suddenly and may include a loss of executive function and attention.

“With a brain injury, you’re doing fine, and then some event happens (in this case COVID), and immediately after that, your cognitive function is different,” said Dr. Jackson.

Additionally, ABI is an actual diagnosis, whereas brain fog is not.

“With a brain injury, there’s a treatment pathway for cognitive rehabilitation,” said Dr. Jackson.

Treatments may include speech, cognitive, and occupational therapy as well as meeting with a neuropsychiatrist for treatment of the mental and behavioral disorders that may result. Dr. Jackson said that while many patients aren’t functioning cognitively or physically at 100%, they can make enough strides that they don’t have to give up things such as driving and, in some cases, their jobs.

Other experts agree that long COVID may damage the brain. An April 2022 study published in the journal Nature found strong evidence that SARS-CoV-2 infection may cause brain-related abnormalities, for example, a reduction in gray matter in certain parts of the brain, including the prefrontal cortex, hypothalamus, and amygdala.

Additionally, white matter, which is found deeper in the brain and is responsible for the exchange of information between different parts of the brain, may also be at risk of damage as a result of the virus, according to a November 2022 study published in the journal SN Comprehensive Clinical Medicine.

Calling it a “fog” makes it easier for clinicians and the general public to dismiss its severity, said Tyler Reed Bell, PhD, a researcher who specializes in viruses that cause brain injury. He is a fellow in the department of psychiatry at the University of California, San Diego. Brain fog can make driving and returning to work especially dangerous. Because of difficulty focusing, patients are much more likely to make mistakes that cause accidents.

“The COVID virus is very invasive to the brain,” Dr. Bell said.

Others contend this may be a rush to judgment. Karla L. Thompson, PhD, lead neuropsychologist at the University of North Carolina at Chapel Hill’s COVID Recovery Clinic, agrees that in more serious cases of COVID that cause a lack of oxygen to the brain, it’s reasonable to call it a brain injury. But brain fog can also be associated with other long COVID symptoms, not just damage to the brain.

Chronic fatigue and poor sleep are both commonly reported symptoms of long COVID that negatively affect brain function, she said. Sleep disturbances, cardiac problems, dysautonomia, and emotional distress could also affect the way the brain functions post COVID. Finding the right treatment requires identifying all the factors contributing to cognitive impairment.

Part of the problem in treating long COVID brain fog is that diagnostic technology is not sensitive enough to detect inflammation that could be causing damage.

Grace McComsey, MD, who leads the long COVID RECOVER study at University Hospitals Health System in Cleveland, said her team is working on identifying biomarkers that could detect brain inflammation in a way similar to the manner researchers have identified biomarkers to help diagnose chronic fatigue syndrome. Additionally, a new study published last month in JAMA for the first time clearly defined 12 symptoms of long COVID, and brain fog was listed among them. All of this contributes to the development of clear diagnostic criteria.

“It will make a big difference once we have some consistency among clinicians in diagnosing the condition,” said Dr. McComsey.

Ms. Whitley is thankful for the treatment that she’s received thus far. She’s seeing a cognitive rehabilitation therapist, who assesses her memory, cognition, and attention span and gives her tools to break up simple tasks, such as driving, so that they don’t feel overwhelming. She’s back behind the wheel and back to work.

But perhaps most importantly, Ms. Whitley joined a support group, led by Dr. Jackson, that includes other people experiencing the same symptoms she is. When she was at her darkest, they understood.

“Talking to other survivors has been the only solace in all this,” Ms. Whitley said. “Together, we grieve all that’s been lost.”

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

Federal Practitioner and the Association of VA Hematology/Oncology (AVAHO) present the 2023 edition of Cancer Data Trends (click to view the digital edition). This special issue provides updates on some of the top cancers and related concerns affecting veterans through original infographics and visual storytelling.

In this issue:

• COVID-19 Outcomes in Veterans With Hematologic Cancers
• Promising New Approaches for Testicular and Prostate Cancer
• Screening Guideline Updates and New Treatments in Colon Cancer
• Exposure-Related Cancers: A Look at the PACT Act
• New Classifications and Emerging Treatments in Brain Cancer
• Gender Disparity in Breast Cancer Among US Veterans
• Lung Cancer Screening in Veterans
• Necessary Updates to Skin Cancer Risk Stratification
• Innovation in Cancer Treatment

Federal Practitioner and the Association of VA Hematology/Oncology (AVAHO) present the 2023 edition of Cancer Data Trends (click to view the digital edition). This special issue provides updates on some of the top cancers and related concerns affecting veterans through original infographics and visual storytelling.

In this issue:

• COVID-19 Outcomes in Veterans With Hematologic Cancers
• Promising New Approaches for Testicular and Prostate Cancer
• Screening Guideline Updates and New Treatments in Colon Cancer
• Exposure-Related Cancers: A Look at the PACT Act
• New Classifications and Emerging Treatments in Brain Cancer
• Gender Disparity in Breast Cancer Among US Veterans
• Lung Cancer Screening in Veterans
• Necessary Updates to Skin Cancer Risk Stratification
• Innovation in Cancer Treatment

Federal Practitioner and the Association of VA Hematology/Oncology (AVAHO) present the 2023 edition of Cancer Data Trends (click to view the digital edition). This special issue provides updates on some of the top cancers and related concerns affecting veterans through original infographics and visual storytelling.

In this issue:

• COVID-19 Outcomes in Veterans With Hematologic Cancers
• Promising New Approaches for Testicular and Prostate Cancer
• Screening Guideline Updates and New Treatments in Colon Cancer
• Exposure-Related Cancers: A Look at the PACT Act
• New Classifications and Emerging Treatments in Brain Cancer
• Gender Disparity in Breast Cancer Among US Veterans
• Lung Cancer Screening in Veterans
• Necessary Updates to Skin Cancer Risk Stratification
• Innovation in Cancer Treatment

Click to view more from Cancer Data Trends 2023.

Click to view more from Cancer Data Trends 2023.

Click to view more from Cancer Data Trends 2023.

TOPLINE:

The availability of medical cannabis has little effect on prescription rates of opioids, nonopioid pain medicines, and other pain interventions, according to a new study published in Annals of Internal Medicine.

METHODOLOGY:

• Cannabis advocates suggest that legal medical cannabis can be a partial solution to the opioid overdose crisis in the United States, which claimed more than 80,000 lives in 2021.
• Current research on how legalized cannabis reduces dependence on prescription pain medication is inconclusive.
• Researchers examined insurance data for the period 2010-2022 from 583,820 adults with chronic noncancer pain.
• They drew from 12 states in which medical cannabis is legal and from 17 in which it is not legal to create a hypothetical randomized trial. The control group simulated prescription rates where medical cannabis was not available.
• Authors evaluated prescription rates for opioids, nonopioid painkillers, and pain interventions, such as physical therapy.

TAKEAWAY:

In a given month during the first 3 years after legalization, for states with medical cannabis, the investigators found the following:

• There was an average decrease of 1.07 percentage points in the proportion of patients who received any opioid prescription, compared to a 1.12 percentage point decrease in the control group.
• There was an average increase of 1.14 percentage points in the proportion of patients who received any nonopioid prescription painkiller, compared to a 1.19 percentage point increase in the control group.
• There was a 0.17 percentage point decrease in the proportion of patients who received any pain procedure, compared to a 0.001 percentage point decrease in the control group.

IN PRACTICE:

“This study did not identify important effects of medical cannabis laws on receipt of opioid or nonopioid pain treatment among patients with chronic noncancer pain,” according to the researchers.

SOURCE:

The study was led by Emma E. McGinty, PhD, of Weill Cornell Medicine, New York, and was funded by the National Institute on Drug Abuse.

LIMITATIONS:

The investigators used a simulated, hypothetical control group that was based on untestable assumptions. They also drew data solely from insured individuals, so the study does not necessarily represent uninsured populations.

DISCLOSURES:

Dr. McGinty reports receiving a grant from NIDA. Her coauthors reported receiving support from NIDA and the National Institutes of Health.

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

TOPLINE:

The availability of medical cannabis has little effect on prescription rates of opioids, nonopioid pain medicines, and other pain interventions, according to a new study published in Annals of Internal Medicine.

METHODOLOGY:

• Cannabis advocates suggest that legal medical cannabis can be a partial solution to the opioid overdose crisis in the United States, which claimed more than 80,000 lives in 2021.
• Current research on how legalized cannabis reduces dependence on prescription pain medication is inconclusive.
• Researchers examined insurance data for the period 2010-2022 from 583,820 adults with chronic noncancer pain.
• They drew from 12 states in which medical cannabis is legal and from 17 in which it is not legal to create a hypothetical randomized trial. The control group simulated prescription rates where medical cannabis was not available.
• Authors evaluated prescription rates for opioids, nonopioid painkillers, and pain interventions, such as physical therapy.

TAKEAWAY:

In a given month during the first 3 years after legalization, for states with medical cannabis, the investigators found the following:

• There was an average decrease of 1.07 percentage points in the proportion of patients who received any opioid prescription, compared to a 1.12 percentage point decrease in the control group.
• There was an average increase of 1.14 percentage points in the proportion of patients who received any nonopioid prescription painkiller, compared to a 1.19 percentage point increase in the control group.
• There was a 0.17 percentage point decrease in the proportion of patients who received any pain procedure, compared to a 0.001 percentage point decrease in the control group.

IN PRACTICE:

“This study did not identify important effects of medical cannabis laws on receipt of opioid or nonopioid pain treatment among patients with chronic noncancer pain,” according to the researchers.

SOURCE:

The study was led by Emma E. McGinty, PhD, of Weill Cornell Medicine, New York, and was funded by the National Institute on Drug Abuse.

LIMITATIONS:

The investigators used a simulated, hypothetical control group that was based on untestable assumptions. They also drew data solely from insured individuals, so the study does not necessarily represent uninsured populations.

DISCLOSURES:

Dr. McGinty reports receiving a grant from NIDA. Her coauthors reported receiving support from NIDA and the National Institutes of Health.

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

TOPLINE:

The availability of medical cannabis has little effect on prescription rates of opioids, nonopioid pain medicines, and other pain interventions, according to a new study published in Annals of Internal Medicine.

METHODOLOGY:

• Cannabis advocates suggest that legal medical cannabis can be a partial solution to the opioid overdose crisis in the United States, which claimed more than 80,000 lives in 2021.
• Current research on how legalized cannabis reduces dependence on prescription pain medication is inconclusive.
• Researchers examined insurance data for the period 2010-2022 from 583,820 adults with chronic noncancer pain.
• They drew from 12 states in which medical cannabis is legal and from 17 in which it is not legal to create a hypothetical randomized trial. The control group simulated prescription rates where medical cannabis was not available.
• Authors evaluated prescription rates for opioids, nonopioid painkillers, and pain interventions, such as physical therapy.

TAKEAWAY:

In a given month during the first 3 years after legalization, for states with medical cannabis, the investigators found the following:

• There was an average decrease of 1.07 percentage points in the proportion of patients who received any opioid prescription, compared to a 1.12 percentage point decrease in the control group.
• There was an average increase of 1.14 percentage points in the proportion of patients who received any nonopioid prescription painkiller, compared to a 1.19 percentage point increase in the control group.
• There was a 0.17 percentage point decrease in the proportion of patients who received any pain procedure, compared to a 0.001 percentage point decrease in the control group.

IN PRACTICE:

“This study did not identify important effects of medical cannabis laws on receipt of opioid or nonopioid pain treatment among patients with chronic noncancer pain,” according to the researchers.

SOURCE:

The study was led by Emma E. McGinty, PhD, of Weill Cornell Medicine, New York, and was funded by the National Institute on Drug Abuse.

LIMITATIONS:

The investigators used a simulated, hypothetical control group that was based on untestable assumptions. They also drew data solely from insured individuals, so the study does not necessarily represent uninsured populations.

DISCLOSURES:

Dr. McGinty reports receiving a grant from NIDA. Her coauthors reported receiving support from NIDA and the National Institutes of Health.

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

Lean muscle mass may offer protection against the development of Alzheimer’s disease (AD), new research suggests.

Investigators analyzed data on more than 450,000 participants in the UK Biobank as well as two independent samples of more than 320,000 individuals with and without AD, and more than 260,000 individuals participating in a separate genes and intelligence study.

They estimated lean muscle and fat tissue in the arms and legs and found, in adjusted analyses, over 500 genetic variants associated with lean mass.

On average, higher genetically lean mass was associated with a “modest but statistically robust” reduction in AD risk and with superior performance on cognitive tasks.

“Using human genetic data, we found evidence for a protective effect of lean mass on risk of Alzheimer’s disease,” study investigators Iyas Daghlas, MD, a resident in the department of neurology, University of California, San Francisco, said in an interview.

Although “clinical intervention studies are needed to confirm this effect, this study supports current recommendations to maintain a healthy lifestyle to prevent dementia,” he said.

The study was published online in BMJ Medicine.
 

Naturally randomized research

Several measures of body composition have been investigated for their potential association with AD. Lean mass – a “proxy for muscle mass, defined as the difference between total mass and fat mass” – has been shown to be reduced in patients with AD compared with controls, the researchers noted.

“Previous research studies have tested the relationship of body mass index with Alzheimer’s disease and did not find evidence for a causal effect,” Dr. Daghlas said. “We wondered whether BMI was an insufficiently granular measure and hypothesized that disaggregating body mass into lean mass and fat mass could reveal novel associations with disease.”

Most studies have used case-control designs, which might be biased by “residual confounding or reverse causality.” Naturally randomized data “may be used as an alternative to conventional observational studies to investigate causal relations between risk factors and diseases,” the researchers wrote.

In particular, the Mendelian randomization (MR) paradigm randomly allocates germline genetic variants and uses them as proxies for a specific risk factor.

MR “is a technique that permits researchers to investigate cause-and-effect relationships using human genetic data,” Dr. Daghlas explained. “In effect, we’re studying the results of a naturally randomized experiment whereby some individuals are genetically allocated to carry more lean mass.” 

The current study used MR to investigate the effect of genetically proxied lean mass on the risk of AD and the “related phenotype” of cognitive performance.
 

Genetic proxy

As genetic proxies for lean mass, the researchers chose single nucleotide polymorphisms (genetic variants) that were associated, in a genome-wide association study (GWAS), with appendicular lean mass.

Appendicular lean mass “more accurately reflects the effects of lean mass than whole body lean mass, which includes smooth and cardiac muscle,” the authors explained.

This GWAS used phenotypic and genetic data from 450,243 participants in the UK Biobank cohort (mean age 57 years). All participants were of European ancestry.

The researchers adjusted for age, sex, and genetic ancestry. They measured appendicular lean mass using bioimpedance – an electric current that flows at different rates through the body, depending on its composition.

In addition to the UK Biobank participants, the researchers drew on an independent sample of 21,982 people with AD; a control group of 41,944 people without AD; a replication sample of 7,329 people with and 252,879 people without AD to validate the findings; and 269,867 people taking part in a genome-wide study of cognitive performance.

The researchers identified 584 variants that met criteria for use as genetic proxies for lean mass. None were located within the APOE gene region. In the aggregate, these variants explained 10.3% of the variance in appendicular lean mass.

Each standard deviation increase in genetically proxied lean mass was associated with a 12% reduction in AD risk (odds ratio [OR], 0.88; 95% confidence interval [CI], 0.82-0.95; P < .001). This finding was replicated in the independent consortium (OR, 0.91; 95% CI, 0.83-0.99; P = .02).

The findings remained “consistent” in sensitivity analyses.
 

A modifiable risk factor?

Higher appendicular lean mass was associated with higher levels of cognitive performance, with each SD increase in lean mass associated with an SD increase in cognitive performance (OR, 0.09; 95% CI, 0.06-0.11; P = .001).

“Adjusting for potential mediation through performance did not reduce the association between appendicular lean mass and risk of AD,” the authors wrote.

They obtained similar results using genetically proxied trunk and whole-body lean mass, after adjusting for fat mass.

The authors noted several limitations. The bioimpedance measures “only predict, but do not directly measure, lean mass.” Moreover, the approach didn’t examine whether a “critical window of risk factor timing” exists, during which lean mass might play a role in influencing AD risk and after which “interventions would no longer be effective.” Nor could the study determine whether increasing lean mass could reverse AD pathology in patients with preclinical disease or mild cognitive impairment.

Nevertheless, the findings suggest “that lean mass might be a possible modifiable protective factor for Alzheimer’s disease,” the authors wrote. “The mechanisms underlying this finding, as well as the clinical and public health implications, warrant further investigation.”
 

Novel strategies

In a comment, Iva Miljkovic, MD, PhD, associate professor, department of epidemiology, University of Pittsburgh, said the investigators used “very rigorous methodology.”

The finding suggesting that lean mass is associated with better cognitive function is “important, as cognitive impairment can become stable rather than progress to a pathological state; and, in some cases, can even be reversed.”

In those cases, “identifying the underlying cause – e.g., low lean mass – can significantly improve cognitive function,” said Dr. Miljkovic, senior author of a study showing muscle fat as a risk factor for cognitive decline.

More research will enable us to “expand our understanding” of the mechanisms involved and determine whether interventions aimed at preventing muscle loss and/or increasing muscle fat may have a beneficial effect on cognitive function,” she said. “This might lead to novel strategies to prevent AD.”

Dr. Daghlas is supported by the British Heart Foundation Centre of Research Excellence at Imperial College, London, and is employed part-time by Novo Nordisk. Dr. Miljkovic reports no relevant financial relationships.

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

Lean muscle mass may offer protection against the development of Alzheimer’s disease (AD), new research suggests.

Investigators analyzed data on more than 450,000 participants in the UK Biobank as well as two independent samples of more than 320,000 individuals with and without AD, and more than 260,000 individuals participating in a separate genes and intelligence study.

They estimated lean muscle and fat tissue in the arms and legs and found, in adjusted analyses, over 500 genetic variants associated with lean mass.

On average, higher genetically lean mass was associated with a “modest but statistically robust” reduction in AD risk and with superior performance on cognitive tasks.

“Using human genetic data, we found evidence for a protective effect of lean mass on risk of Alzheimer’s disease,” study investigators Iyas Daghlas, MD, a resident in the department of neurology, University of California, San Francisco, said in an interview.

Although “clinical intervention studies are needed to confirm this effect, this study supports current recommendations to maintain a healthy lifestyle to prevent dementia,” he said.

The study was published online in BMJ Medicine.
 

Naturally randomized research

Several measures of body composition have been investigated for their potential association with AD. Lean mass – a “proxy for muscle mass, defined as the difference between total mass and fat mass” – has been shown to be reduced in patients with AD compared with controls, the researchers noted.

“Previous research studies have tested the relationship of body mass index with Alzheimer’s disease and did not find evidence for a causal effect,” Dr. Daghlas said. “We wondered whether BMI was an insufficiently granular measure and hypothesized that disaggregating body mass into lean mass and fat mass could reveal novel associations with disease.”

Most studies have used case-control designs, which might be biased by “residual confounding or reverse causality.” Naturally randomized data “may be used as an alternative to conventional observational studies to investigate causal relations between risk factors and diseases,” the researchers wrote.

In particular, the Mendelian randomization (MR) paradigm randomly allocates germline genetic variants and uses them as proxies for a specific risk factor.

MR “is a technique that permits researchers to investigate cause-and-effect relationships using human genetic data,” Dr. Daghlas explained. “In effect, we’re studying the results of a naturally randomized experiment whereby some individuals are genetically allocated to carry more lean mass.” 

The current study used MR to investigate the effect of genetically proxied lean mass on the risk of AD and the “related phenotype” of cognitive performance.
 

Genetic proxy

As genetic proxies for lean mass, the researchers chose single nucleotide polymorphisms (genetic variants) that were associated, in a genome-wide association study (GWAS), with appendicular lean mass.

Appendicular lean mass “more accurately reflects the effects of lean mass than whole body lean mass, which includes smooth and cardiac muscle,” the authors explained.

This GWAS used phenotypic and genetic data from 450,243 participants in the UK Biobank cohort (mean age 57 years). All participants were of European ancestry.

The researchers adjusted for age, sex, and genetic ancestry. They measured appendicular lean mass using bioimpedance – an electric current that flows at different rates through the body, depending on its composition.

In addition to the UK Biobank participants, the researchers drew on an independent sample of 21,982 people with AD; a control group of 41,944 people without AD; a replication sample of 7,329 people with and 252,879 people without AD to validate the findings; and 269,867 people taking part in a genome-wide study of cognitive performance.

The researchers identified 584 variants that met criteria for use as genetic proxies for lean mass. None were located within the APOE gene region. In the aggregate, these variants explained 10.3% of the variance in appendicular lean mass.

Each standard deviation increase in genetically proxied lean mass was associated with a 12% reduction in AD risk (odds ratio [OR], 0.88; 95% confidence interval [CI], 0.82-0.95; P < .001). This finding was replicated in the independent consortium (OR, 0.91; 95% CI, 0.83-0.99; P = .02).

The findings remained “consistent” in sensitivity analyses.
 

A modifiable risk factor?

Higher appendicular lean mass was associated with higher levels of cognitive performance, with each SD increase in lean mass associated with an SD increase in cognitive performance (OR, 0.09; 95% CI, 0.06-0.11; P = .001).

“Adjusting for potential mediation through performance did not reduce the association between appendicular lean mass and risk of AD,” the authors wrote.

They obtained similar results using genetically proxied trunk and whole-body lean mass, after adjusting for fat mass.

The authors noted several limitations. The bioimpedance measures “only predict, but do not directly measure, lean mass.” Moreover, the approach didn’t examine whether a “critical window of risk factor timing” exists, during which lean mass might play a role in influencing AD risk and after which “interventions would no longer be effective.” Nor could the study determine whether increasing lean mass could reverse AD pathology in patients with preclinical disease or mild cognitive impairment.

Nevertheless, the findings suggest “that lean mass might be a possible modifiable protective factor for Alzheimer’s disease,” the authors wrote. “The mechanisms underlying this finding, as well as the clinical and public health implications, warrant further investigation.”
 

Novel strategies

In a comment, Iva Miljkovic, MD, PhD, associate professor, department of epidemiology, University of Pittsburgh, said the investigators used “very rigorous methodology.”

The finding suggesting that lean mass is associated with better cognitive function is “important, as cognitive impairment can become stable rather than progress to a pathological state; and, in some cases, can even be reversed.”

In those cases, “identifying the underlying cause – e.g., low lean mass – can significantly improve cognitive function,” said Dr. Miljkovic, senior author of a study showing muscle fat as a risk factor for cognitive decline.

More research will enable us to “expand our understanding” of the mechanisms involved and determine whether interventions aimed at preventing muscle loss and/or increasing muscle fat may have a beneficial effect on cognitive function,” she said. “This might lead to novel strategies to prevent AD.”

Dr. Daghlas is supported by the British Heart Foundation Centre of Research Excellence at Imperial College, London, and is employed part-time by Novo Nordisk. Dr. Miljkovic reports no relevant financial relationships.

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

Lean muscle mass may offer protection against the development of Alzheimer’s disease (AD), new research suggests.

Investigators analyzed data on more than 450,000 participants in the UK Biobank as well as two independent samples of more than 320,000 individuals with and without AD, and more than 260,000 individuals participating in a separate genes and intelligence study.

They estimated lean muscle and fat tissue in the arms and legs and found, in adjusted analyses, over 500 genetic variants associated with lean mass.

On average, higher genetically lean mass was associated with a “modest but statistically robust” reduction in AD risk and with superior performance on cognitive tasks.

“Using human genetic data, we found evidence for a protective effect of lean mass on risk of Alzheimer’s disease,” study investigators Iyas Daghlas, MD, a resident in the department of neurology, University of California, San Francisco, said in an interview.

Although “clinical intervention studies are needed to confirm this effect, this study supports current recommendations to maintain a healthy lifestyle to prevent dementia,” he said.

The study was published online in BMJ Medicine.
 

Naturally randomized research

Several measures of body composition have been investigated for their potential association with AD. Lean mass – a “proxy for muscle mass, defined as the difference between total mass and fat mass” – has been shown to be reduced in patients with AD compared with controls, the researchers noted.

“Previous research studies have tested the relationship of body mass index with Alzheimer’s disease and did not find evidence for a causal effect,” Dr. Daghlas said. “We wondered whether BMI was an insufficiently granular measure and hypothesized that disaggregating body mass into lean mass and fat mass could reveal novel associations with disease.”

Most studies have used case-control designs, which might be biased by “residual confounding or reverse causality.” Naturally randomized data “may be used as an alternative to conventional observational studies to investigate causal relations between risk factors and diseases,” the researchers wrote.

In particular, the Mendelian randomization (MR) paradigm randomly allocates germline genetic variants and uses them as proxies for a specific risk factor.

MR “is a technique that permits researchers to investigate cause-and-effect relationships using human genetic data,” Dr. Daghlas explained. “In effect, we’re studying the results of a naturally randomized experiment whereby some individuals are genetically allocated to carry more lean mass.” 

The current study used MR to investigate the effect of genetically proxied lean mass on the risk of AD and the “related phenotype” of cognitive performance.
 

Genetic proxy

As genetic proxies for lean mass, the researchers chose single nucleotide polymorphisms (genetic variants) that were associated, in a genome-wide association study (GWAS), with appendicular lean mass.

Appendicular lean mass “more accurately reflects the effects of lean mass than whole body lean mass, which includes smooth and cardiac muscle,” the authors explained.

This GWAS used phenotypic and genetic data from 450,243 participants in the UK Biobank cohort (mean age 57 years). All participants were of European ancestry.

The researchers adjusted for age, sex, and genetic ancestry. They measured appendicular lean mass using bioimpedance – an electric current that flows at different rates through the body, depending on its composition.

In addition to the UK Biobank participants, the researchers drew on an independent sample of 21,982 people with AD; a control group of 41,944 people without AD; a replication sample of 7,329 people with and 252,879 people without AD to validate the findings; and 269,867 people taking part in a genome-wide study of cognitive performance.

The researchers identified 584 variants that met criteria for use as genetic proxies for lean mass. None were located within the APOE gene region. In the aggregate, these variants explained 10.3% of the variance in appendicular lean mass.

Each standard deviation increase in genetically proxied lean mass was associated with a 12% reduction in AD risk (odds ratio [OR], 0.88; 95% confidence interval [CI], 0.82-0.95; P < .001). This finding was replicated in the independent consortium (OR, 0.91; 95% CI, 0.83-0.99; P = .02).

The findings remained “consistent” in sensitivity analyses.
 

A modifiable risk factor?

Higher appendicular lean mass was associated with higher levels of cognitive performance, with each SD increase in lean mass associated with an SD increase in cognitive performance (OR, 0.09; 95% CI, 0.06-0.11; P = .001).

“Adjusting for potential mediation through performance did not reduce the association between appendicular lean mass and risk of AD,” the authors wrote.

They obtained similar results using genetically proxied trunk and whole-body lean mass, after adjusting for fat mass.

The authors noted several limitations. The bioimpedance measures “only predict, but do not directly measure, lean mass.” Moreover, the approach didn’t examine whether a “critical window of risk factor timing” exists, during which lean mass might play a role in influencing AD risk and after which “interventions would no longer be effective.” Nor could the study determine whether increasing lean mass could reverse AD pathology in patients with preclinical disease or mild cognitive impairment.

Nevertheless, the findings suggest “that lean mass might be a possible modifiable protective factor for Alzheimer’s disease,” the authors wrote. “The mechanisms underlying this finding, as well as the clinical and public health implications, warrant further investigation.”
 

Novel strategies

In a comment, Iva Miljkovic, MD, PhD, associate professor, department of epidemiology, University of Pittsburgh, said the investigators used “very rigorous methodology.”

The finding suggesting that lean mass is associated with better cognitive function is “important, as cognitive impairment can become stable rather than progress to a pathological state; and, in some cases, can even be reversed.”

In those cases, “identifying the underlying cause – e.g., low lean mass – can significantly improve cognitive function,” said Dr. Miljkovic, senior author of a study showing muscle fat as a risk factor for cognitive decline.

More research will enable us to “expand our understanding” of the mechanisms involved and determine whether interventions aimed at preventing muscle loss and/or increasing muscle fat may have a beneficial effect on cognitive function,” she said. “This might lead to novel strategies to prevent AD.”

Dr. Daghlas is supported by the British Heart Foundation Centre of Research Excellence at Imperial College, London, and is employed part-time by Novo Nordisk. Dr. Miljkovic reports no relevant financial relationships.

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

Multiple sclerosis (MS) is an immune-mediated demyelinating disorder. There are 2 broad categories of MS: relapsing, also called active MS; and progressive MS. Unfortunately, there is no cure for MS, but disease-modifying therapies (DMTs) can help prevent relapses and new central nervous system lesions in people living with active MS. For patients with the most common type of MS, relapsing-remitting MS (RRMS), DMTs are typically continued for decades while the patient has active disease. RRMS will usually transition to secondary progressive MS (SPMS), which can present as active SPMS or nonactive SPMS. The latter is the type of MS most people with RRMS eventually experience.

A 2019 study estimated that nearly 1 million people in the United States were living with MS.¹ This population estimate indicated the peak age-specific prevalence of MS was 55 to 64 years. Population data demonstrate improved mortality rates for people diagnosed with MS from 1997 to 2012 compared with prior years.² Therefore, the management of nonactive SPMS is an increasingly significant area of need. There are currently no DMTs on the market approved for nonactive SPMS, and lifelong DMTs in these patients are neither indicated nor supported by evidence. Nevertheless, the discontinuation of DMTs in nonactive SPMS has been a long-debated topic with varied opinions on how and when to discontinue.

The 2018 American Academy of Neurology (AAN) guideline recommends that clinicians advise patients with SPMS to discontinue DMT use if they do not have ongoing relapses (or gadolinium-enhanced lesions on magnetic resonance imaging activity) or have not been ambulatory (Expanded Disability Status Scale [EDSS] ≥ 7) for ≥ 2 years.³ In recent years, there has been increased research on nonactive SPMS, specifically on discontinuation of DMTs. This clinical review assesses the recent evidence from a variety of standpoints, including the effect of discontinuing DMTs on the MS disease course and quality of life (QOL) and the perspectives of patients living with MS. Based on this evidence, a conversation guide will be presented as a framework to aid with the clinician-patient discussion on discontinuing MS DMTs.

Disease Modifying Therapies

Roos and colleagues used data from 2 large MS cohorts: MSBase and Observatoire Français de la Sclérose en Plaques (OFSEP) to compare high-efficacy vs low-efficacy DMT in both active and nonactive SPMS.⁴ In the active SPMS group, the strength of DMTs did not change disability progression, but high-efficacy DMTs reduced relapses better than the low-efficacy DMTs. On the other hand, the nonactive SPMS group saw no difference between DMTs in both relapse risk and disability progression. Another observational study of 221 patients with RRMS who discontinued DMTs noted that there were 2 independent predictors for the absence of relapse following DMT discontinuation: being aged > 45 years and the lack of relapse for ≥ 4 years prior to DMT discontinuation.⁵ Though these patients still may have been classified as RRMS, both these independent predictors for stability postdiscontinuation of DMTs are the typical characteristics of a nonactive SPMS patient.

Pathophysiology may help explain why DMT discontinuation seems to produce no adverse clinical outcomes in people with nonactive SPMS. Nonactive SPMS, which follows after RRMS, is largely correlated with age. In nonactive SPMS, there is less B and T lymphocyte migration across the blood-brain barrier. Furthermore, a lifetime of low-grade inflammation during the RRMS phase results in axonal damage and declined repair capacity, which produces the predominance of neurodegeneration in the nonactive SPMS disease process.⁶ This pathophysiologic difference between active and nonactive disease not only explains the different symptomatology of these MS subtypes, but also could explain why drugs that target the inflammatory processes more characteristic of active disease are not effective in nonactive SPMS.

Other recent studies explored the impact of age on DMT efficacy for patients with nonactive SPMS. A meta-analysis by Weidman and colleagues pooled trial data across multiple DMT classes in > 28,000 patients.⁷ The resulting regression model predicted zero efficacy of any DMT in patients who are aged > 53 years. High-efficacy DMTs only outperformed low-efficacy DMTs in people aged < 40.5 years. Another observational study by Hua and colleagues saw a similar result.⁸ This study included patients who discontinued DMT who were aged ≥ 60 years. The median follow-up time was 5.3 years. Of the 178 patients who discontinued DMTs, only 1 patient had a relapse. In this study, the age for participation provided a higher likelihood that patients included were in nonactive SPMS. Furthermore, the outcome reflects the typical presentation of nonactive SPMS where, despite the continuation or discontinuation of DMT, there was a lack of relapses. When comparing patients who discontinued DMTs with those who continued use, there was no significant difference in their 25-foot walk times, which is an objective marker for a more progressive symptom seen in nonactive MS.

The DISCOMS trial (NCT03073603) has been completed, but full results are not yet published. In this noninferiority trial, > 250 patients aged ≥ 55 years were assessed on a variety of outcomes, including relapses, EDSS score, and QOL. MS subtypes were considered at baseline, and subgroup analysis looking particularly at the SPMS population could provide further insight into its effect on MS course.

Quality of Life

Whether discontinuation of DMTs is worth considering in nonactive SPMS, it is also important to consider the risks and burdens associated with continuation. Medication administration burdens come with all MS DMTs whether there is the need to inject oneself, increased pill burden, or travel to an infusion clinic. The ever-rising costs of DMTs also can be a financial burden to the patient.⁹ All MS DMTs carry risks of adverse effects (AEs). These can range from a mild injection site reaction to severe infection, depending on the DMT used. Many of these severe AEs, such as opportunistic infections and cancer, have been associated with either an increased risk of occurrence and/or worsened outcomes in older adults who remain on DMTs, particularly moderate- to high-efficacy DMTs, such as sphingosine-1- phosphate receptor modulators, fumarates, natalizumab, alemtuzumab, cladribine, and anti-CD20 antibodies.¹⁰ In a 2019 survey of 377 patients with MS, 63.8% of respondents ranked safety as the most important reason they would consider discontinuing their DMTs.¹¹ In addition, a real-world study comparing people with nonactive SPMS who continued DMTs vs those who discontinued found that discontinuers reported better QOL.⁸

Conversation Guide for Discontinuing Therapies

The 2019 survey that assessed reasons for discontinuation also asked people with nonactive SPMS whether they thought they were in a nonactive disease stage, and what was their likelihood they would stop DMTs.¹¹ Interestingly, only 59.4% of respondents self-assessed their MS as nonactive, and just 11.9% of respondents were willing to discontinue DMTs.¹¹ These results suggest that there may be a need for patient education about nonactive SPMS and the rationale to continue or discontinue DMTs. Thus, before broaching the topic of discontinuation, explaining the nonactive SPMS subtype is important.

Even with a good understanding of nonactive SPMS, patients may be hesitant to stop using DMTs that they previously relied on to keep their MS stable. The 2019 survey ranked physician recommendation as the third highest reason to discontinue DMTs.¹¹ Taking the time to explain the clinical evidence for DMT discontinuation may help patients better understand a clinician’s recommendation and inspire more confidence.

Another important aspect of DMT discontinuation decision making is creating a plan for how the patient will be monitored to provide assurance if they experience a relapse. The 2019 survey asked patients what would be most important to them for their management plan after discontinuing DMT; magnetic resonance imaging and neurologic examination monitoring ranked the highest.¹¹ The plan should include timing for follow-up appointments and imaging, providing the patient comfort in knowing their MS will be monitored and verified for the relapse stability that is expected from nonactive SPMS. In the rare case a relapse does occur, having a contingency plan and noting the possibility of restarting DMTs is an integral part of reassuring the patient that their decision to discontinue DMTs will be treated with the utmost caution and individualized to their needs.

Lastly, highlighting which aspects of MS treatment will continue to be a priority in nonactive SPMS, such as symptomatic medication management and nonpharmacologic therapy, is important for the patient to recognize that there are still opportunities to manage this phase of MS. There are many lifestyle modifications that can be considered complementary to medical management of MS at any stage of the disease. Vascular comorbidities, such as hypertension, hyperlipidemia, and diabetes, have been associated with more rapid disability progression in MS.¹² Optimized management of these diseases may slow disability progression, in addition to the benefit of improved outcomes of the vascular comorbidity. Various formats of exercise have been studied in the MS population. A meta-analysis of aerobic, resistance, and combined exercise found benefits in these formats on health-related QOL.¹³

Many dietary strategies have been studied in MS. A recent network meta-analysis reviewed some of the more commonly studied diets, including low-fat, modified Mediterranean, ketogenic, anti-inflammatory, Paleolithic, intermittent fasting, and calorie restriction vs a usual diet.¹⁴ Although the overall quality of evidence was low, the Paleolithic and modified Mediterranean showed greater reductions in fatigue, as well as increased physical and mental QOL compared with a usual diet. The low-fat diet was associated with a reduction in fatigue. Many of these lifestyle modifications may complement optimized vascular comorbidity treatment; however, any exercise regimen or dietary change should be considered with the whole health of the patient in mind.

As with any health care decision, it is important to involve the patient in a joint decision regarding their care. This may mean giving the patient time to think about the information presented, do their own research, talk to family members or other clinicians, etc. The decision to discontinue DMT may not happen at the same appointment it is initially brought up at. It may even be reasonable to revisit the conversation later if discontinuation is not something the patient is amenable to at the time.

Conclusions

There is high-quality evidence that discontinuing DMTs in nonactive SPMS is not a major detriment to the MS disease course. Current literature also suggests that there may be benefits to discontinuation in this MS subtype in terms of QOL and meeting patient values. Additional research particularly in the nonactive SPMS population will continue to improve the knowledge and awareness of this aspect of MS DMT management. The growing evidence in this area may make discontinuation of DMT in nonactive SPMS a less-debatable topic, but it is still a major treatment decision that clinicians must thoroughly discuss with the patient to provide high-quality, patient-centered care.

Multiple sclerosis (MS) is an immune-mediated demyelinating disorder. There are 2 broad categories of MS: relapsing, also called active MS; and progressive MS. Unfortunately, there is no cure for MS, but disease-modifying therapies (DMTs) can help prevent relapses and new central nervous system lesions in people living with active MS. For patients with the most common type of MS, relapsing-remitting MS (RRMS), DMTs are typically continued for decades while the patient has active disease. RRMS will usually transition to secondary progressive MS (SPMS), which can present as active SPMS or nonactive SPMS. The latter is the type of MS most people with RRMS eventually experience.

A 2019 study estimated that nearly 1 million people in the United States were living with MS.¹ This population estimate indicated the peak age-specific prevalence of MS was 55 to 64 years. Population data demonstrate improved mortality rates for people diagnosed with MS from 1997 to 2012 compared with prior years.² Therefore, the management of nonactive SPMS is an increasingly significant area of need. There are currently no DMTs on the market approved for nonactive SPMS, and lifelong DMTs in these patients are neither indicated nor supported by evidence. Nevertheless, the discontinuation of DMTs in nonactive SPMS has been a long-debated topic with varied opinions on how and when to discontinue.

The 2018 American Academy of Neurology (AAN) guideline recommends that clinicians advise patients with SPMS to discontinue DMT use if they do not have ongoing relapses (or gadolinium-enhanced lesions on magnetic resonance imaging activity) or have not been ambulatory (Expanded Disability Status Scale [EDSS] ≥ 7) for ≥ 2 years.³ In recent years, there has been increased research on nonactive SPMS, specifically on discontinuation of DMTs. This clinical review assesses the recent evidence from a variety of standpoints, including the effect of discontinuing DMTs on the MS disease course and quality of life (QOL) and the perspectives of patients living with MS. Based on this evidence, a conversation guide will be presented as a framework to aid with the clinician-patient discussion on discontinuing MS DMTs.

Disease Modifying Therapies

Roos and colleagues used data from 2 large MS cohorts: MSBase and Observatoire Français de la Sclérose en Plaques (OFSEP) to compare high-efficacy vs low-efficacy DMT in both active and nonactive SPMS.⁴ In the active SPMS group, the strength of DMTs did not change disability progression, but high-efficacy DMTs reduced relapses better than the low-efficacy DMTs. On the other hand, the nonactive SPMS group saw no difference between DMTs in both relapse risk and disability progression. Another observational study of 221 patients with RRMS who discontinued DMTs noted that there were 2 independent predictors for the absence of relapse following DMT discontinuation: being aged > 45 years and the lack of relapse for ≥ 4 years prior to DMT discontinuation.⁵ Though these patients still may have been classified as RRMS, both these independent predictors for stability postdiscontinuation of DMTs are the typical characteristics of a nonactive SPMS patient.

Pathophysiology may help explain why DMT discontinuation seems to produce no adverse clinical outcomes in people with nonactive SPMS. Nonactive SPMS, which follows after RRMS, is largely correlated with age. In nonactive SPMS, there is less B and T lymphocyte migration across the blood-brain barrier. Furthermore, a lifetime of low-grade inflammation during the RRMS phase results in axonal damage and declined repair capacity, which produces the predominance of neurodegeneration in the nonactive SPMS disease process.⁶ This pathophysiologic difference between active and nonactive disease not only explains the different symptomatology of these MS subtypes, but also could explain why drugs that target the inflammatory processes more characteristic of active disease are not effective in nonactive SPMS.

Other recent studies explored the impact of age on DMT efficacy for patients with nonactive SPMS. A meta-analysis by Weidman and colleagues pooled trial data across multiple DMT classes in > 28,000 patients.⁷ The resulting regression model predicted zero efficacy of any DMT in patients who are aged > 53 years. High-efficacy DMTs only outperformed low-efficacy DMTs in people aged < 40.5 years. Another observational study by Hua and colleagues saw a similar result.⁸ This study included patients who discontinued DMT who were aged ≥ 60 years. The median follow-up time was 5.3 years. Of the 178 patients who discontinued DMTs, only 1 patient had a relapse. In this study, the age for participation provided a higher likelihood that patients included were in nonactive SPMS. Furthermore, the outcome reflects the typical presentation of nonactive SPMS where, despite the continuation or discontinuation of DMT, there was a lack of relapses. When comparing patients who discontinued DMTs with those who continued use, there was no significant difference in their 25-foot walk times, which is an objective marker for a more progressive symptom seen in nonactive MS.

The DISCOMS trial (NCT03073603) has been completed, but full results are not yet published. In this noninferiority trial, > 250 patients aged ≥ 55 years were assessed on a variety of outcomes, including relapses, EDSS score, and QOL. MS subtypes were considered at baseline, and subgroup analysis looking particularly at the SPMS population could provide further insight into its effect on MS course.

Quality of Life

Whether discontinuation of DMTs is worth considering in nonactive SPMS, it is also important to consider the risks and burdens associated with continuation. Medication administration burdens come with all MS DMTs whether there is the need to inject oneself, increased pill burden, or travel to an infusion clinic. The ever-rising costs of DMTs also can be a financial burden to the patient.⁹ All MS DMTs carry risks of adverse effects (AEs). These can range from a mild injection site reaction to severe infection, depending on the DMT used. Many of these severe AEs, such as opportunistic infections and cancer, have been associated with either an increased risk of occurrence and/or worsened outcomes in older adults who remain on DMTs, particularly moderate- to high-efficacy DMTs, such as sphingosine-1- phosphate receptor modulators, fumarates, natalizumab, alemtuzumab, cladribine, and anti-CD20 antibodies.¹⁰ In a 2019 survey of 377 patients with MS, 63.8% of respondents ranked safety as the most important reason they would consider discontinuing their DMTs.¹¹ In addition, a real-world study comparing people with nonactive SPMS who continued DMTs vs those who discontinued found that discontinuers reported better QOL.⁸

Conversation Guide for Discontinuing Therapies

The 2019 survey that assessed reasons for discontinuation also asked people with nonactive SPMS whether they thought they were in a nonactive disease stage, and what was their likelihood they would stop DMTs.¹¹ Interestingly, only 59.4% of respondents self-assessed their MS as nonactive, and just 11.9% of respondents were willing to discontinue DMTs.¹¹ These results suggest that there may be a need for patient education about nonactive SPMS and the rationale to continue or discontinue DMTs. Thus, before broaching the topic of discontinuation, explaining the nonactive SPMS subtype is important.

Even with a good understanding of nonactive SPMS, patients may be hesitant to stop using DMTs that they previously relied on to keep their MS stable. The 2019 survey ranked physician recommendation as the third highest reason to discontinue DMTs.¹¹ Taking the time to explain the clinical evidence for DMT discontinuation may help patients better understand a clinician’s recommendation and inspire more confidence.

Another important aspect of DMT discontinuation decision making is creating a plan for how the patient will be monitored to provide assurance if they experience a relapse. The 2019 survey asked patients what would be most important to them for their management plan after discontinuing DMT; magnetic resonance imaging and neurologic examination monitoring ranked the highest.¹¹ The plan should include timing for follow-up appointments and imaging, providing the patient comfort in knowing their MS will be monitored and verified for the relapse stability that is expected from nonactive SPMS. In the rare case a relapse does occur, having a contingency plan and noting the possibility of restarting DMTs is an integral part of reassuring the patient that their decision to discontinue DMTs will be treated with the utmost caution and individualized to their needs.

Lastly, highlighting which aspects of MS treatment will continue to be a priority in nonactive SPMS, such as symptomatic medication management and nonpharmacologic therapy, is important for the patient to recognize that there are still opportunities to manage this phase of MS. There are many lifestyle modifications that can be considered complementary to medical management of MS at any stage of the disease. Vascular comorbidities, such as hypertension, hyperlipidemia, and diabetes, have been associated with more rapid disability progression in MS.¹² Optimized management of these diseases may slow disability progression, in addition to the benefit of improved outcomes of the vascular comorbidity. Various formats of exercise have been studied in the MS population. A meta-analysis of aerobic, resistance, and combined exercise found benefits in these formats on health-related QOL.¹³

Many dietary strategies have been studied in MS. A recent network meta-analysis reviewed some of the more commonly studied diets, including low-fat, modified Mediterranean, ketogenic, anti-inflammatory, Paleolithic, intermittent fasting, and calorie restriction vs a usual diet.¹⁴ Although the overall quality of evidence was low, the Paleolithic and modified Mediterranean showed greater reductions in fatigue, as well as increased physical and mental QOL compared with a usual diet. The low-fat diet was associated with a reduction in fatigue. Many of these lifestyle modifications may complement optimized vascular comorbidity treatment; however, any exercise regimen or dietary change should be considered with the whole health of the patient in mind.

As with any health care decision, it is important to involve the patient in a joint decision regarding their care. This may mean giving the patient time to think about the information presented, do their own research, talk to family members or other clinicians, etc. The decision to discontinue DMT may not happen at the same appointment it is initially brought up at. It may even be reasonable to revisit the conversation later if discontinuation is not something the patient is amenable to at the time.

Conclusions

There is high-quality evidence that discontinuing DMTs in nonactive SPMS is not a major detriment to the MS disease course. Current literature also suggests that there may be benefits to discontinuation in this MS subtype in terms of QOL and meeting patient values. Additional research particularly in the nonactive SPMS population will continue to improve the knowledge and awareness of this aspect of MS DMT management. The growing evidence in this area may make discontinuation of DMT in nonactive SPMS a less-debatable topic, but it is still a major treatment decision that clinicians must thoroughly discuss with the patient to provide high-quality, patient-centered care.

Multiple sclerosis (MS) is an immune-mediated demyelinating disorder. There are 2 broad categories of MS: relapsing, also called active MS; and progressive MS. Unfortunately, there is no cure for MS, but disease-modifying therapies (DMTs) can help prevent relapses and new central nervous system lesions in people living with active MS. For patients with the most common type of MS, relapsing-remitting MS (RRMS), DMTs are typically continued for decades while the patient has active disease. RRMS will usually transition to secondary progressive MS (SPMS), which can present as active SPMS or nonactive SPMS. The latter is the type of MS most people with RRMS eventually experience.

A 2019 study estimated that nearly 1 million people in the United States were living with MS.¹ This population estimate indicated the peak age-specific prevalence of MS was 55 to 64 years. Population data demonstrate improved mortality rates for people diagnosed with MS from 1997 to 2012 compared with prior years.² Therefore, the management of nonactive SPMS is an increasingly significant area of need. There are currently no DMTs on the market approved for nonactive SPMS, and lifelong DMTs in these patients are neither indicated nor supported by evidence. Nevertheless, the discontinuation of DMTs in nonactive SPMS has been a long-debated topic with varied opinions on how and when to discontinue.

The 2018 American Academy of Neurology (AAN) guideline recommends that clinicians advise patients with SPMS to discontinue DMT use if they do not have ongoing relapses (or gadolinium-enhanced lesions on magnetic resonance imaging activity) or have not been ambulatory (Expanded Disability Status Scale [EDSS] ≥ 7) for ≥ 2 years.³ In recent years, there has been increased research on nonactive SPMS, specifically on discontinuation of DMTs. This clinical review assesses the recent evidence from a variety of standpoints, including the effect of discontinuing DMTs on the MS disease course and quality of life (QOL) and the perspectives of patients living with MS. Based on this evidence, a conversation guide will be presented as a framework to aid with the clinician-patient discussion on discontinuing MS DMTs.

Disease Modifying Therapies

Roos and colleagues used data from 2 large MS cohorts: MSBase and Observatoire Français de la Sclérose en Plaques (OFSEP) to compare high-efficacy vs low-efficacy DMT in both active and nonactive SPMS.⁴ In the active SPMS group, the strength of DMTs did not change disability progression, but high-efficacy DMTs reduced relapses better than the low-efficacy DMTs. On the other hand, the nonactive SPMS group saw no difference between DMTs in both relapse risk and disability progression. Another observational study of 221 patients with RRMS who discontinued DMTs noted that there were 2 independent predictors for the absence of relapse following DMT discontinuation: being aged > 45 years and the lack of relapse for ≥ 4 years prior to DMT discontinuation.⁵ Though these patients still may have been classified as RRMS, both these independent predictors for stability postdiscontinuation of DMTs are the typical characteristics of a nonactive SPMS patient.

Pathophysiology may help explain why DMT discontinuation seems to produce no adverse clinical outcomes in people with nonactive SPMS. Nonactive SPMS, which follows after RRMS, is largely correlated with age. In nonactive SPMS, there is less B and T lymphocyte migration across the blood-brain barrier. Furthermore, a lifetime of low-grade inflammation during the RRMS phase results in axonal damage and declined repair capacity, which produces the predominance of neurodegeneration in the nonactive SPMS disease process.⁶ This pathophysiologic difference between active and nonactive disease not only explains the different symptomatology of these MS subtypes, but also could explain why drugs that target the inflammatory processes more characteristic of active disease are not effective in nonactive SPMS.

Other recent studies explored the impact of age on DMT efficacy for patients with nonactive SPMS. A meta-analysis by Weidman and colleagues pooled trial data across multiple DMT classes in > 28,000 patients.⁷ The resulting regression model predicted zero efficacy of any DMT in patients who are aged > 53 years. High-efficacy DMTs only outperformed low-efficacy DMTs in people aged < 40.5 years. Another observational study by Hua and colleagues saw a similar result.⁸ This study included patients who discontinued DMT who were aged ≥ 60 years. The median follow-up time was 5.3 years. Of the 178 patients who discontinued DMTs, only 1 patient had a relapse. In this study, the age for participation provided a higher likelihood that patients included were in nonactive SPMS. Furthermore, the outcome reflects the typical presentation of nonactive SPMS where, despite the continuation or discontinuation of DMT, there was a lack of relapses. When comparing patients who discontinued DMTs with those who continued use, there was no significant difference in their 25-foot walk times, which is an objective marker for a more progressive symptom seen in nonactive MS.

The DISCOMS trial (NCT03073603) has been completed, but full results are not yet published. In this noninferiority trial, > 250 patients aged ≥ 55 years were assessed on a variety of outcomes, including relapses, EDSS score, and QOL. MS subtypes were considered at baseline, and subgroup analysis looking particularly at the SPMS population could provide further insight into its effect on MS course.

Quality of Life

Whether discontinuation of DMTs is worth considering in nonactive SPMS, it is also important to consider the risks and burdens associated with continuation. Medication administration burdens come with all MS DMTs whether there is the need to inject oneself, increased pill burden, or travel to an infusion clinic. The ever-rising costs of DMTs also can be a financial burden to the patient.⁹ All MS DMTs carry risks of adverse effects (AEs). These can range from a mild injection site reaction to severe infection, depending on the DMT used. Many of these severe AEs, such as opportunistic infections and cancer, have been associated with either an increased risk of occurrence and/or worsened outcomes in older adults who remain on DMTs, particularly moderate- to high-efficacy DMTs, such as sphingosine-1- phosphate receptor modulators, fumarates, natalizumab, alemtuzumab, cladribine, and anti-CD20 antibodies.¹⁰ In a 2019 survey of 377 patients with MS, 63.8% of respondents ranked safety as the most important reason they would consider discontinuing their DMTs.¹¹ In addition, a real-world study comparing people with nonactive SPMS who continued DMTs vs those who discontinued found that discontinuers reported better QOL.⁸

Conversation Guide for Discontinuing Therapies

The 2019 survey that assessed reasons for discontinuation also asked people with nonactive SPMS whether they thought they were in a nonactive disease stage, and what was their likelihood they would stop DMTs.¹¹ Interestingly, only 59.4% of respondents self-assessed their MS as nonactive, and just 11.9% of respondents were willing to discontinue DMTs.¹¹ These results suggest that there may be a need for patient education about nonactive SPMS and the rationale to continue or discontinue DMTs. Thus, before broaching the topic of discontinuation, explaining the nonactive SPMS subtype is important.

Even with a good understanding of nonactive SPMS, patients may be hesitant to stop using DMTs that they previously relied on to keep their MS stable. The 2019 survey ranked physician recommendation as the third highest reason to discontinue DMTs.¹¹ Taking the time to explain the clinical evidence for DMT discontinuation may help patients better understand a clinician’s recommendation and inspire more confidence.

Another important aspect of DMT discontinuation decision making is creating a plan for how the patient will be monitored to provide assurance if they experience a relapse. The 2019 survey asked patients what would be most important to them for their management plan after discontinuing DMT; magnetic resonance imaging and neurologic examination monitoring ranked the highest.¹¹ The plan should include timing for follow-up appointments and imaging, providing the patient comfort in knowing their MS will be monitored and verified for the relapse stability that is expected from nonactive SPMS. In the rare case a relapse does occur, having a contingency plan and noting the possibility of restarting DMTs is an integral part of reassuring the patient that their decision to discontinue DMTs will be treated with the utmost caution and individualized to their needs.

Lastly, highlighting which aspects of MS treatment will continue to be a priority in nonactive SPMS, such as symptomatic medication management and nonpharmacologic therapy, is important for the patient to recognize that there are still opportunities to manage this phase of MS. There are many lifestyle modifications that can be considered complementary to medical management of MS at any stage of the disease. Vascular comorbidities, such as hypertension, hyperlipidemia, and diabetes, have been associated with more rapid disability progression in MS.¹² Optimized management of these diseases may slow disability progression, in addition to the benefit of improved outcomes of the vascular comorbidity. Various formats of exercise have been studied in the MS population. A meta-analysis of aerobic, resistance, and combined exercise found benefits in these formats on health-related QOL.¹³

Many dietary strategies have been studied in MS. A recent network meta-analysis reviewed some of the more commonly studied diets, including low-fat, modified Mediterranean, ketogenic, anti-inflammatory, Paleolithic, intermittent fasting, and calorie restriction vs a usual diet.¹⁴ Although the overall quality of evidence was low, the Paleolithic and modified Mediterranean showed greater reductions in fatigue, as well as increased physical and mental QOL compared with a usual diet. The low-fat diet was associated with a reduction in fatigue. Many of these lifestyle modifications may complement optimized vascular comorbidity treatment; however, any exercise regimen or dietary change should be considered with the whole health of the patient in mind.

As with any health care decision, it is important to involve the patient in a joint decision regarding their care. This may mean giving the patient time to think about the information presented, do their own research, talk to family members or other clinicians, etc. The decision to discontinue DMT may not happen at the same appointment it is initially brought up at. It may even be reasonable to revisit the conversation later if discontinuation is not something the patient is amenable to at the time.

Conclusions

There is high-quality evidence that discontinuing DMTs in nonactive SPMS is not a major detriment to the MS disease course. Current literature also suggests that there may be benefits to discontinuation in this MS subtype in terms of QOL and meeting patient values. Additional research particularly in the nonactive SPMS population will continue to improve the knowledge and awareness of this aspect of MS DMT management. The growing evidence in this area may make discontinuation of DMT in nonactive SPMS a less-debatable topic, but it is still a major treatment decision that clinicians must thoroughly discuss with the patient to provide high-quality, patient-centered care.

An automated artificial intelligence (AI) model trained to read electroencephalograms (EEGs) in patients with suspected epilepsy is just as accurate as trained neurologists, new data suggest.

Known as SCORE-AI, the technology distinguishes between abnormal and normal EEG recordings and classifies irregular recordings into specific categories crucial for patient decision-making.

“SCORE-AI can be used in place of experts in underprivileged areas, where expertise is missing, or to help physicians to preselect or prescore recordings in areas where the workload is high – we can all benefit from AI,” study investigator Sándor Beniczky, MD, PhD, said in a JAMA Neurology podcast.

Dr. Beniczky is professor of clinical neurophysiology at Aarhus University in Denmark.

The findings were published online in JAMA Neurology.
 

Gaining a foothold

Increasingly, AI is gaining a foothold in medicine by credibly addressing patient queries and aiding radiologists.

To bring AI to EEG interpretation, the researchers developed and validated an AI model that was able to assess routine, clinical EEGs in patients with suspected epilepsy.

Beyond using AI to distinguish abnormal from normal EEG recordings, the researchers wanted to train the new system to classify abnormal recordings into the major categories that are most relevant for clinical decision-making in patients who may have epilepsy. The categories included epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse abnormalities.

The researchers trained the learning model using Standardized Computer-based Organized Reporting of EEG (SCORE) software.

In the development phase, the model was trained using more than 30,490 anonymized and highly annotated EEG recordings from 14,100 men (median age, 25 years) from a single center. The recordings had an average duration of 31 minutes and were interpreted by 17 neurologists using standardized criteria. If an EEG recording was abnormal, the physicians had to specify which abnormal features were present.

SCORE-AI then performed an analysis of the recordings based on input from the experts.

To validate the findings, investigators used two independent test datasets. The first dataset consisted of 100 representative routine EEGs from 61 men (median age, 26 years), evaluated by 11 neurologists from different centers.

The consensus of these evaluations served as the reference standard. The second dataset comprised nearly 10,000 EEGs from a single center (5,170 men; median age, 35 years), independently assessed by 14 neurologists.
 

Near-perfect accuracy

When compared with the experts, SCORE-AI had near-perfect accuracy with an area under the receiver operating characteristic (AUROC) curve for differentiating normal from abnormal EEG recordings of 0.95.

SCORE-AI also performed well at identifying generalized epileptiform abnormalities (AUROC, 0.96), focal epileptiform abnormalities (AUROC, 0.91), focal nonepileptiform abnormalities (AUROC, 0.89), and diffuse nonepileptiform abnormalities (AUROC, 0.93).

In addition, SCORE-AI had excellent agreement with clinicians – and sometimes agreed with individual experts more than the experts agreed with one another.

When Dr. Beniczky and team tested SCORE-AI against three previously published AI models, SCORE-AI demonstrated greater specificity than those models (90% vs. 3%-63%) but was not as sensitive (86.7%) as two of the models (96.7% and 100%).

One of the study’s limitations was the fact that SCORE-AI was developed and validated on routine EEGs that excluded neonates and critically ill patients.

In the future, Dr. Beniczky said on the podcast, the team would like to train SCORE-AI to read EEGs with more granularity, and eventually use only one single channel to record EEGs. At present, SCORE-AI is being integrated with Natus Neuro, a widely used EEG equipment system, the investigators note.

In an accompanying editorial, Jonathan Kleen, MD, PhD, and Elan Guterman, MD, said, “The overall approach taken ... in developing and validating SCORE-AI sets a standard for this work going forward.”

Dr. Kleen and Dr. Guterman note that the technological gains brought about by SCORE-AI technology “could offer an exciting prospect to improve EEG availability and clinical care for the 50 million people with epilepsy worldwide.”
 

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

An automated artificial intelligence (AI) model trained to read electroencephalograms (EEGs) in patients with suspected epilepsy is just as accurate as trained neurologists, new data suggest.

Known as SCORE-AI, the technology distinguishes between abnormal and normal EEG recordings and classifies irregular recordings into specific categories crucial for patient decision-making.

“SCORE-AI can be used in place of experts in underprivileged areas, where expertise is missing, or to help physicians to preselect or prescore recordings in areas where the workload is high – we can all benefit from AI,” study investigator Sándor Beniczky, MD, PhD, said in a JAMA Neurology podcast.

Dr. Beniczky is professor of clinical neurophysiology at Aarhus University in Denmark.

The findings were published online in JAMA Neurology.
 

Gaining a foothold

Increasingly, AI is gaining a foothold in medicine by credibly addressing patient queries and aiding radiologists.

To bring AI to EEG interpretation, the researchers developed and validated an AI model that was able to assess routine, clinical EEGs in patients with suspected epilepsy.

Beyond using AI to distinguish abnormal from normal EEG recordings, the researchers wanted to train the new system to classify abnormal recordings into the major categories that are most relevant for clinical decision-making in patients who may have epilepsy. The categories included epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse abnormalities.

The researchers trained the learning model using Standardized Computer-based Organized Reporting of EEG (SCORE) software.

In the development phase, the model was trained using more than 30,490 anonymized and highly annotated EEG recordings from 14,100 men (median age, 25 years) from a single center. The recordings had an average duration of 31 minutes and were interpreted by 17 neurologists using standardized criteria. If an EEG recording was abnormal, the physicians had to specify which abnormal features were present.

SCORE-AI then performed an analysis of the recordings based on input from the experts.

To validate the findings, investigators used two independent test datasets. The first dataset consisted of 100 representative routine EEGs from 61 men (median age, 26 years), evaluated by 11 neurologists from different centers.

The consensus of these evaluations served as the reference standard. The second dataset comprised nearly 10,000 EEGs from a single center (5,170 men; median age, 35 years), independently assessed by 14 neurologists.
 

Near-perfect accuracy

When compared with the experts, SCORE-AI had near-perfect accuracy with an area under the receiver operating characteristic (AUROC) curve for differentiating normal from abnormal EEG recordings of 0.95.

SCORE-AI also performed well at identifying generalized epileptiform abnormalities (AUROC, 0.96), focal epileptiform abnormalities (AUROC, 0.91), focal nonepileptiform abnormalities (AUROC, 0.89), and diffuse nonepileptiform abnormalities (AUROC, 0.93).

In addition, SCORE-AI had excellent agreement with clinicians – and sometimes agreed with individual experts more than the experts agreed with one another.

When Dr. Beniczky and team tested SCORE-AI against three previously published AI models, SCORE-AI demonstrated greater specificity than those models (90% vs. 3%-63%) but was not as sensitive (86.7%) as two of the models (96.7% and 100%).

One of the study’s limitations was the fact that SCORE-AI was developed and validated on routine EEGs that excluded neonates and critically ill patients.

In the future, Dr. Beniczky said on the podcast, the team would like to train SCORE-AI to read EEGs with more granularity, and eventually use only one single channel to record EEGs. At present, SCORE-AI is being integrated with Natus Neuro, a widely used EEG equipment system, the investigators note.

In an accompanying editorial, Jonathan Kleen, MD, PhD, and Elan Guterman, MD, said, “The overall approach taken ... in developing and validating SCORE-AI sets a standard for this work going forward.”

Dr. Kleen and Dr. Guterman note that the technological gains brought about by SCORE-AI technology “could offer an exciting prospect to improve EEG availability and clinical care for the 50 million people with epilepsy worldwide.”
 

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

An automated artificial intelligence (AI) model trained to read electroencephalograms (EEGs) in patients with suspected epilepsy is just as accurate as trained neurologists, new data suggest.

Known as SCORE-AI, the technology distinguishes between abnormal and normal EEG recordings and classifies irregular recordings into specific categories crucial for patient decision-making.

“SCORE-AI can be used in place of experts in underprivileged areas, where expertise is missing, or to help physicians to preselect or prescore recordings in areas where the workload is high – we can all benefit from AI,” study investigator Sándor Beniczky, MD, PhD, said in a JAMA Neurology podcast.

Dr. Beniczky is professor of clinical neurophysiology at Aarhus University in Denmark.

The findings were published online in JAMA Neurology.
 

Gaining a foothold

Increasingly, AI is gaining a foothold in medicine by credibly addressing patient queries and aiding radiologists.

To bring AI to EEG interpretation, the researchers developed and validated an AI model that was able to assess routine, clinical EEGs in patients with suspected epilepsy.

Beyond using AI to distinguish abnormal from normal EEG recordings, the researchers wanted to train the new system to classify abnormal recordings into the major categories that are most relevant for clinical decision-making in patients who may have epilepsy. The categories included epileptiform-focal, epileptiform-generalized, nonepileptiform-focal, and nonepileptiform-diffuse abnormalities.

The researchers trained the learning model using Standardized Computer-based Organized Reporting of EEG (SCORE) software.

In the development phase, the model was trained using more than 30,490 anonymized and highly annotated EEG recordings from 14,100 men (median age, 25 years) from a single center. The recordings had an average duration of 31 minutes and were interpreted by 17 neurologists using standardized criteria. If an EEG recording was abnormal, the physicians had to specify which abnormal features were present.

SCORE-AI then performed an analysis of the recordings based on input from the experts.

To validate the findings, investigators used two independent test datasets. The first dataset consisted of 100 representative routine EEGs from 61 men (median age, 26 years), evaluated by 11 neurologists from different centers.

The consensus of these evaluations served as the reference standard. The second dataset comprised nearly 10,000 EEGs from a single center (5,170 men; median age, 35 years), independently assessed by 14 neurologists.
 

Near-perfect accuracy

When compared with the experts, SCORE-AI had near-perfect accuracy with an area under the receiver operating characteristic (AUROC) curve for differentiating normal from abnormal EEG recordings of 0.95.

SCORE-AI also performed well at identifying generalized epileptiform abnormalities (AUROC, 0.96), focal epileptiform abnormalities (AUROC, 0.91), focal nonepileptiform abnormalities (AUROC, 0.89), and diffuse nonepileptiform abnormalities (AUROC, 0.93).

In addition, SCORE-AI had excellent agreement with clinicians – and sometimes agreed with individual experts more than the experts agreed with one another.

When Dr. Beniczky and team tested SCORE-AI against three previously published AI models, SCORE-AI demonstrated greater specificity than those models (90% vs. 3%-63%) but was not as sensitive (86.7%) as two of the models (96.7% and 100%).

One of the study’s limitations was the fact that SCORE-AI was developed and validated on routine EEGs that excluded neonates and critically ill patients.

In the future, Dr. Beniczky said on the podcast, the team would like to train SCORE-AI to read EEGs with more granularity, and eventually use only one single channel to record EEGs. At present, SCORE-AI is being integrated with Natus Neuro, a widely used EEG equipment system, the investigators note.

In an accompanying editorial, Jonathan Kleen, MD, PhD, and Elan Guterman, MD, said, “The overall approach taken ... in developing and validating SCORE-AI sets a standard for this work going forward.”

Dr. Kleen and Dr. Guterman note that the technological gains brought about by SCORE-AI technology “could offer an exciting prospect to improve EEG availability and clinical care for the 50 million people with epilepsy worldwide.”
 

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

New longitudinal data from the TRACK TBI investigators show that recovery from traumatic brain injury (TBI) is a dynamic process that continues to evolve well beyond the initial 12 months after injury.

The data show that patients with TBI may continue to improve or decline during a period of up to 7 years after injury, making it more of a chronic condition, the investigators report.

“Our results dispute the notion that TBI is a discrete, isolated medical event with a finite, static functional outcome following a relatively short period of upward recovery (typically up to 1 year),” Benjamin Brett, PhD, assistant professor, departments of neurosurgery and neurology, Medical College of Wisconsin, Milwaukee, told this news organization.

“Rather, individuals continue to exhibit improvement and decline across a range of domains, including psychiatric, cognitive, and functional outcomes, even 2-7 years after their injury,” Dr. Brett said.

“Ultimately, our findings support conceptualizing TBI as a chronic condition for many patients, which requires routine follow-up, medical monitoring, responsive care, and support, adapting to their evolving needs many years following injury,” he said.

Results of the TRACK TBI LONG (Transforming Research and Clinical Knowledge in TBI Longitudinal study) were published online in Neurology.
 

Chronic and evolving

The results are based on 1,264 adults (mean age at injury, 41 years) from the initial TRACK TBI study, including 917 with mild TBI (mTBI) and 193 with moderate/severe TBI (msTBI), who were matched to 154 control patients who had experienced orthopedic trauma without evidence of head injury (OTC).

The participants were followed annually for up to 7 years after injury using the Glasgow Outcome Scale–Extended (GOSE), Brief Symptom Inventory–18 (BSI), and the Brief Test of Adult Cognition by Telephone (BTACT), as well as a self-reported perception of function. The researchers calculated rates of change (classified as stable, improved, or declined) for individual outcomes at each long-term follow-up.

In general, “stable” was the most frequent change outcome for the individual measures from postinjury baseline assessment to 7 years post injury.

However, a substantial proportion of patients with TBI (regardless of severity) experienced changes in psychiatric status, cognition, and functional outcomes over the years.

When the GOSE, BSI, and BTACT were considered collectively, rates of decline were 21% for mTBI, 26% for msTBI, and 15% for OTC.

The highest rates of decline were in functional outcomes (GOSE scores). On average, over the course of 2-7 years post injury, 29% of patients with mTBI and 23% of those with msTBI experienced a decline in the ability to function with daily activities.

A pattern of improvement on the GOSE was noted in 36% of patients with msTBI and 22% patients with mTBI.

Notably, said Dr. Brett, patients who experienced greater difficulties near the time of injury showed improvement for a period of 2-7 years post injury. Patient factors, such as older age at the time of the injury, were associated with greater risk of long-term decline.

“Our findings highlight the need to embrace conceptualization of TBI as a chronic condition in order to establish systems of care that provide continued follow-up with treatment and supports that adapt to evolving patient needs, regardless of the directions of change,” Dr. Brett told this news organization.
 

Important and novel work

In a linked editorial, Robynne Braun, MD, PhD, with the department of neurology, University of Maryland, Baltimore, notes that there have been “few prospective studies examining postinjury outcomes on this longer timescale, especially in mild TBI, making this an important and novel body of work.”

The study “effectively demonstrates that changes in function across multiple domains continue to occur well beyond the conventionally tracked 6- to 12-month period of injury recovery,” Dr. Braun writes.

The observation that over the 7-year follow-up, a substantial proportion of patients with mTBI and msTBI exhibited a pattern of decline on the GOSE suggests that they “may have needed more ongoing medical monitoring, rehabilitation, or supportive services to prevent worsening,” Dr. Braun adds.

At the same time, the improvement pattern on the GOSE suggests “opportunities for recovery that further rehabilitative or medical services might have enhanced.”

The study was funded by the National Institute of Neurological Disorders and Stroke, the National Institute on Aging, the National Football League Scientific Advisory Board, and the U.S. Department of Defense. Dr. Brett and Dr. Braun have disclosed no relevant financial relationships.

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

New longitudinal data from the TRACK TBI investigators show that recovery from traumatic brain injury (TBI) is a dynamic process that continues to evolve well beyond the initial 12 months after injury.

The data show that patients with TBI may continue to improve or decline during a period of up to 7 years after injury, making it more of a chronic condition, the investigators report.

“Our results dispute the notion that TBI is a discrete, isolated medical event with a finite, static functional outcome following a relatively short period of upward recovery (typically up to 1 year),” Benjamin Brett, PhD, assistant professor, departments of neurosurgery and neurology, Medical College of Wisconsin, Milwaukee, told this news organization.

“Rather, individuals continue to exhibit improvement and decline across a range of domains, including psychiatric, cognitive, and functional outcomes, even 2-7 years after their injury,” Dr. Brett said.

“Ultimately, our findings support conceptualizing TBI as a chronic condition for many patients, which requires routine follow-up, medical monitoring, responsive care, and support, adapting to their evolving needs many years following injury,” he said.

Results of the TRACK TBI LONG (Transforming Research and Clinical Knowledge in TBI Longitudinal study) were published online in Neurology.
 

Chronic and evolving

The results are based on 1,264 adults (mean age at injury, 41 years) from the initial TRACK TBI study, including 917 with mild TBI (mTBI) and 193 with moderate/severe TBI (msTBI), who were matched to 154 control patients who had experienced orthopedic trauma without evidence of head injury (OTC).

The participants were followed annually for up to 7 years after injury using the Glasgow Outcome Scale–Extended (GOSE), Brief Symptom Inventory–18 (BSI), and the Brief Test of Adult Cognition by Telephone (BTACT), as well as a self-reported perception of function. The researchers calculated rates of change (classified as stable, improved, or declined) for individual outcomes at each long-term follow-up.

In general, “stable” was the most frequent change outcome for the individual measures from postinjury baseline assessment to 7 years post injury.

However, a substantial proportion of patients with TBI (regardless of severity) experienced changes in psychiatric status, cognition, and functional outcomes over the years.

When the GOSE, BSI, and BTACT were considered collectively, rates of decline were 21% for mTBI, 26% for msTBI, and 15% for OTC.

The highest rates of decline were in functional outcomes (GOSE scores). On average, over the course of 2-7 years post injury, 29% of patients with mTBI and 23% of those with msTBI experienced a decline in the ability to function with daily activities.

A pattern of improvement on the GOSE was noted in 36% of patients with msTBI and 22% patients with mTBI.

Notably, said Dr. Brett, patients who experienced greater difficulties near the time of injury showed improvement for a period of 2-7 years post injury. Patient factors, such as older age at the time of the injury, were associated with greater risk of long-term decline.

“Our findings highlight the need to embrace conceptualization of TBI as a chronic condition in order to establish systems of care that provide continued follow-up with treatment and supports that adapt to evolving patient needs, regardless of the directions of change,” Dr. Brett told this news organization.
 

Important and novel work

In a linked editorial, Robynne Braun, MD, PhD, with the department of neurology, University of Maryland, Baltimore, notes that there have been “few prospective studies examining postinjury outcomes on this longer timescale, especially in mild TBI, making this an important and novel body of work.”

The study “effectively demonstrates that changes in function across multiple domains continue to occur well beyond the conventionally tracked 6- to 12-month period of injury recovery,” Dr. Braun writes.

The observation that over the 7-year follow-up, a substantial proportion of patients with mTBI and msTBI exhibited a pattern of decline on the GOSE suggests that they “may have needed more ongoing medical monitoring, rehabilitation, or supportive services to prevent worsening,” Dr. Braun adds.

At the same time, the improvement pattern on the GOSE suggests “opportunities for recovery that further rehabilitative or medical services might have enhanced.”

The study was funded by the National Institute of Neurological Disorders and Stroke, the National Institute on Aging, the National Football League Scientific Advisory Board, and the U.S. Department of Defense. Dr. Brett and Dr. Braun have disclosed no relevant financial relationships.

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

New longitudinal data from the TRACK TBI investigators show that recovery from traumatic brain injury (TBI) is a dynamic process that continues to evolve well beyond the initial 12 months after injury.

The data show that patients with TBI may continue to improve or decline during a period of up to 7 years after injury, making it more of a chronic condition, the investigators report.

“Our results dispute the notion that TBI is a discrete, isolated medical event with a finite, static functional outcome following a relatively short period of upward recovery (typically up to 1 year),” Benjamin Brett, PhD, assistant professor, departments of neurosurgery and neurology, Medical College of Wisconsin, Milwaukee, told this news organization.

“Rather, individuals continue to exhibit improvement and decline across a range of domains, including psychiatric, cognitive, and functional outcomes, even 2-7 years after their injury,” Dr. Brett said.

“Ultimately, our findings support conceptualizing TBI as a chronic condition for many patients, which requires routine follow-up, medical monitoring, responsive care, and support, adapting to their evolving needs many years following injury,” he said.

Results of the TRACK TBI LONG (Transforming Research and Clinical Knowledge in TBI Longitudinal study) were published online in Neurology.
 

Chronic and evolving

The results are based on 1,264 adults (mean age at injury, 41 years) from the initial TRACK TBI study, including 917 with mild TBI (mTBI) and 193 with moderate/severe TBI (msTBI), who were matched to 154 control patients who had experienced orthopedic trauma without evidence of head injury (OTC).

The participants were followed annually for up to 7 years after injury using the Glasgow Outcome Scale–Extended (GOSE), Brief Symptom Inventory–18 (BSI), and the Brief Test of Adult Cognition by Telephone (BTACT), as well as a self-reported perception of function. The researchers calculated rates of change (classified as stable, improved, or declined) for individual outcomes at each long-term follow-up.

In general, “stable” was the most frequent change outcome for the individual measures from postinjury baseline assessment to 7 years post injury.

However, a substantial proportion of patients with TBI (regardless of severity) experienced changes in psychiatric status, cognition, and functional outcomes over the years.

When the GOSE, BSI, and BTACT were considered collectively, rates of decline were 21% for mTBI, 26% for msTBI, and 15% for OTC.

The highest rates of decline were in functional outcomes (GOSE scores). On average, over the course of 2-7 years post injury, 29% of patients with mTBI and 23% of those with msTBI experienced a decline in the ability to function with daily activities.

A pattern of improvement on the GOSE was noted in 36% of patients with msTBI and 22% patients with mTBI.

Notably, said Dr. Brett, patients who experienced greater difficulties near the time of injury showed improvement for a period of 2-7 years post injury. Patient factors, such as older age at the time of the injury, were associated with greater risk of long-term decline.

“Our findings highlight the need to embrace conceptualization of TBI as a chronic condition in order to establish systems of care that provide continued follow-up with treatment and supports that adapt to evolving patient needs, regardless of the directions of change,” Dr. Brett told this news organization.
 

Important and novel work

In a linked editorial, Robynne Braun, MD, PhD, with the department of neurology, University of Maryland, Baltimore, notes that there have been “few prospective studies examining postinjury outcomes on this longer timescale, especially in mild TBI, making this an important and novel body of work.”

The study “effectively demonstrates that changes in function across multiple domains continue to occur well beyond the conventionally tracked 6- to 12-month period of injury recovery,” Dr. Braun writes.

The observation that over the 7-year follow-up, a substantial proportion of patients with mTBI and msTBI exhibited a pattern of decline on the GOSE suggests that they “may have needed more ongoing medical monitoring, rehabilitation, or supportive services to prevent worsening,” Dr. Braun adds.

At the same time, the improvement pattern on the GOSE suggests “opportunities for recovery that further rehabilitative or medical services might have enhanced.”

The study was funded by the National Institute of Neurological Disorders and Stroke, the National Institute on Aging, the National Football League Scientific Advisory Board, and the U.S. Department of Defense. Dr. Brett and Dr. Braun have disclosed no relevant financial relationships.

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