Neurology

Treatment with deep brain stimulation improved motor function and quality of life, but depression scores increased after 1 year, based on data from 20 adults.

Subthalamic nucleus deep brain stimulation (STN-DBS) has emerged as an effective treatment for Parkinson’s disease symptoms, with evidence supporting improved motor symptoms and quality of life, wrote Francesca Mameli, PsyD, of Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, and colleagues.

Maggiore Policlinico Hospital of Milan

However, the effect of STN-DBS on personality in Parkinson’s disease (PD) has not been well investigated they said.

In a study published in Neuromodulation, the researchers reviewed data from 12 women and 8 men with PD who underwent bilateral STN-DBS.

Depression was assessed via the Montgomery-Asberg Depressive Rating Scale (MADRS), personality characteristics were assessed via the Minnesota Multiphasic Personality Inventory–2 (MMPI-2), and motor disabilities were assessed via UPDRS-III-Motor. The motor disabilities score was obtained in medication on and medication off conditions; the off condition followed a 12-hour overnight withdrawal of dopaminergic medication. Quality of life was assessed via the Parkinson’s Disease Questionnaire–8 (PDQ-8).

After 12 months, scores on the MMPI-2 were significantly higher on the D subscale, increased from a baseline mean of 56.05 to a 12-month mean of 61.90 (P = .015).

Other MMPI-2 scales showing significant increases included the DEP scale, LSE scale, WRK scale, and TRT scale. No differences appeared between male and female patients.

No significant changes occurred from pre-DBS baseline to the 12-month follow-up in MADRS scale assessment, with mean scores of 8.18 and 9.22, respectively.

A 40% improvement in UPDRS measures of motor function occurred among patients in the “medication-off” condition, although there was no significant change following DBS in the medication-on condition, the researchers said. Among 18 patients with PDQ-8 assessments, quality of life scores were significantly higher at 12 months’ post DBS compared to baseline pre DBS (40.15 vs. 30.73, P = .011).

The researchers also examined the relationship between the total electrical energy delivered (TEED) and the occurrence of personality trait shift. In the TEED analysis, “only the energy on the right side was inversely correlated with the changes in depression,” they wrote.

“Because of the complexity of psychiatric phenomena, it would be advisable to take a cautious approach by including psychiatric evaluation by interview for a better selection of patients who score close to the pathological cutoffs in MADRS and MMPI-2,” the researchers wrote in their discussion.

The study findings were limited by several factors including the small sample size, lack of data on the prevalence and severity of apathy, the use of scales based on self-reports, and inability to control for all factors that might affect depressive traits, the researchers noted. In addition, more research is needed to explore the correlation between TEED and personality trait changes, they said.

However, the results support the value of DBS in PD, but emphasize the need to manage expectations, they emphasized. “Expectations should never be unrealistic, and the caring team should ensure not only that patients fully understand the risks and potential benefits of the DBS but also that it will not stop the neurodegenerative progression of the disease,” they said.

The study was supported in part by the Italian Ministry of Health. The researchers had no financial conflicts to disclose.

Treatment with deep brain stimulation improved motor function and quality of life, but depression scores increased after 1 year, based on data from 20 adults.

Subthalamic nucleus deep brain stimulation (STN-DBS) has emerged as an effective treatment for Parkinson’s disease symptoms, with evidence supporting improved motor symptoms and quality of life, wrote Francesca Mameli, PsyD, of Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, and colleagues.

Maggiore Policlinico Hospital of Milan

However, the effect of STN-DBS on personality in Parkinson’s disease (PD) has not been well investigated they said.

In a study published in Neuromodulation, the researchers reviewed data from 12 women and 8 men with PD who underwent bilateral STN-DBS.

Depression was assessed via the Montgomery-Asberg Depressive Rating Scale (MADRS), personality characteristics were assessed via the Minnesota Multiphasic Personality Inventory–2 (MMPI-2), and motor disabilities were assessed via UPDRS-III-Motor. The motor disabilities score was obtained in medication on and medication off conditions; the off condition followed a 12-hour overnight withdrawal of dopaminergic medication. Quality of life was assessed via the Parkinson’s Disease Questionnaire–8 (PDQ-8).

After 12 months, scores on the MMPI-2 were significantly higher on the D subscale, increased from a baseline mean of 56.05 to a 12-month mean of 61.90 (P = .015).

Other MMPI-2 scales showing significant increases included the DEP scale, LSE scale, WRK scale, and TRT scale. No differences appeared between male and female patients.

No significant changes occurred from pre-DBS baseline to the 12-month follow-up in MADRS scale assessment, with mean scores of 8.18 and 9.22, respectively.

A 40% improvement in UPDRS measures of motor function occurred among patients in the “medication-off” condition, although there was no significant change following DBS in the medication-on condition, the researchers said. Among 18 patients with PDQ-8 assessments, quality of life scores were significantly higher at 12 months’ post DBS compared to baseline pre DBS (40.15 vs. 30.73, P = .011).

The researchers also examined the relationship between the total electrical energy delivered (TEED) and the occurrence of personality trait shift. In the TEED analysis, “only the energy on the right side was inversely correlated with the changes in depression,” they wrote.

“Because of the complexity of psychiatric phenomena, it would be advisable to take a cautious approach by including psychiatric evaluation by interview for a better selection of patients who score close to the pathological cutoffs in MADRS and MMPI-2,” the researchers wrote in their discussion.

The study findings were limited by several factors including the small sample size, lack of data on the prevalence and severity of apathy, the use of scales based on self-reports, and inability to control for all factors that might affect depressive traits, the researchers noted. In addition, more research is needed to explore the correlation between TEED and personality trait changes, they said.

However, the results support the value of DBS in PD, but emphasize the need to manage expectations, they emphasized. “Expectations should never be unrealistic, and the caring team should ensure not only that patients fully understand the risks and potential benefits of the DBS but also that it will not stop the neurodegenerative progression of the disease,” they said.

The study was supported in part by the Italian Ministry of Health. The researchers had no financial conflicts to disclose.

Treatment with deep brain stimulation improved motor function and quality of life, but depression scores increased after 1 year, based on data from 20 adults.

Subthalamic nucleus deep brain stimulation (STN-DBS) has emerged as an effective treatment for Parkinson’s disease symptoms, with evidence supporting improved motor symptoms and quality of life, wrote Francesca Mameli, PsyD, of Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, and colleagues.

Maggiore Policlinico Hospital of Milan

However, the effect of STN-DBS on personality in Parkinson’s disease (PD) has not been well investigated they said.

In a study published in Neuromodulation, the researchers reviewed data from 12 women and 8 men with PD who underwent bilateral STN-DBS.

Depression was assessed via the Montgomery-Asberg Depressive Rating Scale (MADRS), personality characteristics were assessed via the Minnesota Multiphasic Personality Inventory–2 (MMPI-2), and motor disabilities were assessed via UPDRS-III-Motor. The motor disabilities score was obtained in medication on and medication off conditions; the off condition followed a 12-hour overnight withdrawal of dopaminergic medication. Quality of life was assessed via the Parkinson’s Disease Questionnaire–8 (PDQ-8).

After 12 months, scores on the MMPI-2 were significantly higher on the D subscale, increased from a baseline mean of 56.05 to a 12-month mean of 61.90 (P = .015).

Other MMPI-2 scales showing significant increases included the DEP scale, LSE scale, WRK scale, and TRT scale. No differences appeared between male and female patients.

No significant changes occurred from pre-DBS baseline to the 12-month follow-up in MADRS scale assessment, with mean scores of 8.18 and 9.22, respectively.

A 40% improvement in UPDRS measures of motor function occurred among patients in the “medication-off” condition, although there was no significant change following DBS in the medication-on condition, the researchers said. Among 18 patients with PDQ-8 assessments, quality of life scores were significantly higher at 12 months’ post DBS compared to baseline pre DBS (40.15 vs. 30.73, P = .011).

The researchers also examined the relationship between the total electrical energy delivered (TEED) and the occurrence of personality trait shift. In the TEED analysis, “only the energy on the right side was inversely correlated with the changes in depression,” they wrote.

“Because of the complexity of psychiatric phenomena, it would be advisable to take a cautious approach by including psychiatric evaluation by interview for a better selection of patients who score close to the pathological cutoffs in MADRS and MMPI-2,” the researchers wrote in their discussion.

The study findings were limited by several factors including the small sample size, lack of data on the prevalence and severity of apathy, the use of scales based on self-reports, and inability to control for all factors that might affect depressive traits, the researchers noted. In addition, more research is needed to explore the correlation between TEED and personality trait changes, they said.

However, the results support the value of DBS in PD, but emphasize the need to manage expectations, they emphasized. “Expectations should never be unrealistic, and the caring team should ensure not only that patients fully understand the risks and potential benefits of the DBS but also that it will not stop the neurodegenerative progression of the disease,” they said.

The study was supported in part by the Italian Ministry of Health. The researchers had no financial conflicts to disclose.

Extraverts and individuals who are disciplined are less likely to experience cognitive decline later in life, whereas those with neuroticism have an increased risk for cognitive dysfunction, new research shows.

Investigators analyzed data from almost 2,000 individuals enrolled in the Rush Memory and Aging Project (MAP) – a longitudinal study of older adults living in the greater Chicago metropolitan region and northeastern Illinois – with recruitment that began in 1997 and continues through today. Participants received a personality assessment as well as annual assessments of their cognitive abilities.

Those with high scores on measures of conscientiousness were significantly less likely to progress from normal cognition to mild cognitive impairment (MCI) during the study. In fact, scoring an extra 1 standard deviation on the conscientiousness scale was associated with a 22% lower risk of transitioning from no cognitive impairment (NCI) to MCI. On the other hand, scoring an additional 1 SD on a neuroticism scale was associated with a 12% increased risk of transitioning to MCI.

Participants who scored high on extraversion, as well as those who scored high on conscientiousness or low on neuroticism, tended to maintain normal cognitive functioning longer than other participants.

“Personality traits reflect relatively enduring patterns of thinking and behaving, which may cumulatively affect engagement in healthy and unhealthy behaviors and thought patterns across the lifespan,” lead author Tomiko Yoneda, PhD, a postdoctoral researcher in the department of medical social sciences, Northwestern University, Chicago, said in an interview.

“The accumulation of lifelong experiences may then contribute to susceptibility of particular diseases or disorders, such as mild cognitive impairment, or contribute to individual differences in the ability to withstand age-related neurological changes,” she added.

The study was published online in the Journal of Personality and Social Psychology.
 

Competing risk factors

Personality traits “reflect an individual’s persistent patterns of thinking, feeling, and behaving,” Dr. Yoneda said.

“For example, conscientiousness is characterized by competence, dutifulness, and self-discipline, while neuroticism is characterized by anxiety, depressive symptoms, and emotional instability. Likewise, individuals high in extraversion tend to be enthusiastic, gregarious, talkative, and assertive,” she added.

Previous research “suggests that low conscientiousness and high neuroticism are associated with an increased risk of cognitive impairment,” she continued. However, “there is also an increased risk of death in older adulthood – in other words, these outcomes are ‘competing risk factors.’”

Dr. Yoneda said her team wanted to “examine the impact of personality traits on the simultaneous risk of transitioning to mild cognitive impairment, dementia, and death.”  

For the study, the researchers analyzed data from 1,954 participants in MAP (mean age at baseline 80 years, 73.7% female, 86.8% White), who received a personality assessment and annual assessments of their cognitive abilities.

To assess personality traits – in particular, conscientiousness, neuroticism, and extraversion – the researchers used the NEO Five Factor Inventory (NEO-FFI). They also used multistate survival modeling to examine the potential association between these traits and transitions from one cognitive status category to another (NCI, MCI, and dementia) and to death.
 

Cognitive healthspan

By the end of the study, over half of the sample (54%) had died.

Most transitions showed “relative stability in cognitive status across measurement occasions.”

• NCI to NCI (n = 7,368)
• MCI to MCI (n = 1,244)
• Dementia to dementia (n = 876)

There were 725 “backward transitions” from MCI to NCI, “which may reflect improvement or within-person variability in cognitive functioning, or learning effects,” the authors note.

There were only 114 “backward transitions” from dementia to MCI and only 12 from dementia to NCI, “suggesting that improvement in cognitive status was relatively rare, particularly once an individual progresses to dementia.”

After adjusting for demographics, depressive symptoms, and apolipoprotein (APOE) ε4 allele, the researchers found that personality traits were the most important factors in the transition from NCI to MCI.

Higher conscientiousness was associated with a decreased risk of transitioning from NCI to MCI (hazard ratio, 0.78; 95% confidence interval, 0.72-0.85). Conversely, higher neuroticism was associated with an increased risk of transitioning from NCI to MCI (HR, 1.12; 95% CI, 1.04-1.21) and a significantly decreased likelihood of transition back from MCI to NCI (HR, 0.90; 95% CI, 0.81-1.00).

Scoring ~6 points on a conscientiousness scale ranging from 0-48 (that is, 1 SD on the scale) was significantly associated with ~22% lower risk of transitioning forward from NCI to MCI, while scoring ~7 more points on a neuroticism scale (1 SD) was significantly associated with ~12% higher risk of transitioning from NCI to MCI.

Higher extraversion was associated with an increased likelihood of transitioning from MCI back to NCI (HR, 1.12; 95% CI, 1.03-1.22), and although extraversion was not associated with a longer total lifespan, participants who scored high on extraversion, as well as those who scored low on conscientiousness or low on neuroticism, maintained normal cognitive function longer than other participants.

“Our results suggest that high conscientiousness and low neuroticism may protect individuals against mild cognitive impairment,” said Dr. Yoneda.

Importantly, individuals who were either higher in conscientiousness, higher in extraversion, or lower in neuroticism had more years of “cognitive healthspan,” meaning more years without cognitive impairment,” she added.

In addition, “individuals lower in neuroticism and higher in extraversion were more likely to recover after receiving an MCI diagnosis, suggesting that these traits may be protective even after an individual starts to progress to dementia,” she said.

The authors note that the study focused on only three of the Big Five personality traits, while the other 2 – openness to experience and agreeableness – may also be associated with cognitive aging processes and mortality.

Nevertheless, given the current results, alongside extensive research in the personality field, aiming to increase conscientiousness through persistent behavioral change is one potential strategy for promoting healthy cognitive aging, Dr. Yoneda said.
 

‘Invaluable window’

In a comment, Brent Roberts, PhD, professor of psychology, University of Illinois Urbana-Champaign, said the study provides an “invaluable window into how personality affects the process of decline and either accelerates it, as in the role of neuroticism, or decelerates it, as in the role of conscientiousness.”

“I think the most fascinating finding was the fact that extraversion was related to transitioning from MCI back to NCI. These types of transitions have simply not been part of prior research, and it provides utterly unique insights and opportunities for interventions that may actually help people recover from a decline,” said Dr. Roberts, who was not involved in the research.

Claire Sexton, DPhil, Alzheimer’s Association director of scientific programs and outreach, called the paper “novel” because it investigated the transitions between normal cognition and mild impairment and between mild impairment and dementia.

Dr. Sexton, who was associated with this research team, cautioned that is it observational, “so it can illuminate associations or correlations, but not causes. As a result, we can’t say for sure what the mechanisms are behind these potential connections between personality and cognition, and more research is needed.”

The research was supported by the Alzheimer Society Research Program, Social Sciences and Humanities Research Council, and the National Institute on Aging of the National Institutes of Health. Dr. Yoneda and co-authors, Dr. Roberts, and Dr. Sexton have disclosed no relevant financial relationships.

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

Extraverts and individuals who are disciplined are less likely to experience cognitive decline later in life, whereas those with neuroticism have an increased risk for cognitive dysfunction, new research shows.

Investigators analyzed data from almost 2,000 individuals enrolled in the Rush Memory and Aging Project (MAP) – a longitudinal study of older adults living in the greater Chicago metropolitan region and northeastern Illinois – with recruitment that began in 1997 and continues through today. Participants received a personality assessment as well as annual assessments of their cognitive abilities.

Those with high scores on measures of conscientiousness were significantly less likely to progress from normal cognition to mild cognitive impairment (MCI) during the study. In fact, scoring an extra 1 standard deviation on the conscientiousness scale was associated with a 22% lower risk of transitioning from no cognitive impairment (NCI) to MCI. On the other hand, scoring an additional 1 SD on a neuroticism scale was associated with a 12% increased risk of transitioning to MCI.

Participants who scored high on extraversion, as well as those who scored high on conscientiousness or low on neuroticism, tended to maintain normal cognitive functioning longer than other participants.

“Personality traits reflect relatively enduring patterns of thinking and behaving, which may cumulatively affect engagement in healthy and unhealthy behaviors and thought patterns across the lifespan,” lead author Tomiko Yoneda, PhD, a postdoctoral researcher in the department of medical social sciences, Northwestern University, Chicago, said in an interview.

“The accumulation of lifelong experiences may then contribute to susceptibility of particular diseases or disorders, such as mild cognitive impairment, or contribute to individual differences in the ability to withstand age-related neurological changes,” she added.

The study was published online in the Journal of Personality and Social Psychology.
 

Competing risk factors

Personality traits “reflect an individual’s persistent patterns of thinking, feeling, and behaving,” Dr. Yoneda said.

“For example, conscientiousness is characterized by competence, dutifulness, and self-discipline, while neuroticism is characterized by anxiety, depressive symptoms, and emotional instability. Likewise, individuals high in extraversion tend to be enthusiastic, gregarious, talkative, and assertive,” she added.

Previous research “suggests that low conscientiousness and high neuroticism are associated with an increased risk of cognitive impairment,” she continued. However, “there is also an increased risk of death in older adulthood – in other words, these outcomes are ‘competing risk factors.’”

Dr. Yoneda said her team wanted to “examine the impact of personality traits on the simultaneous risk of transitioning to mild cognitive impairment, dementia, and death.”  

For the study, the researchers analyzed data from 1,954 participants in MAP (mean age at baseline 80 years, 73.7% female, 86.8% White), who received a personality assessment and annual assessments of their cognitive abilities.

To assess personality traits – in particular, conscientiousness, neuroticism, and extraversion – the researchers used the NEO Five Factor Inventory (NEO-FFI). They also used multistate survival modeling to examine the potential association between these traits and transitions from one cognitive status category to another (NCI, MCI, and dementia) and to death.
 

Cognitive healthspan

By the end of the study, over half of the sample (54%) had died.

Most transitions showed “relative stability in cognitive status across measurement occasions.”

• NCI to NCI (n = 7,368)
• MCI to MCI (n = 1,244)
• Dementia to dementia (n = 876)

There were 725 “backward transitions” from MCI to NCI, “which may reflect improvement or within-person variability in cognitive functioning, or learning effects,” the authors note.

There were only 114 “backward transitions” from dementia to MCI and only 12 from dementia to NCI, “suggesting that improvement in cognitive status was relatively rare, particularly once an individual progresses to dementia.”

After adjusting for demographics, depressive symptoms, and apolipoprotein (APOE) ε4 allele, the researchers found that personality traits were the most important factors in the transition from NCI to MCI.

Higher conscientiousness was associated with a decreased risk of transitioning from NCI to MCI (hazard ratio, 0.78; 95% confidence interval, 0.72-0.85). Conversely, higher neuroticism was associated with an increased risk of transitioning from NCI to MCI (HR, 1.12; 95% CI, 1.04-1.21) and a significantly decreased likelihood of transition back from MCI to NCI (HR, 0.90; 95% CI, 0.81-1.00).

Scoring ~6 points on a conscientiousness scale ranging from 0-48 (that is, 1 SD on the scale) was significantly associated with ~22% lower risk of transitioning forward from NCI to MCI, while scoring ~7 more points on a neuroticism scale (1 SD) was significantly associated with ~12% higher risk of transitioning from NCI to MCI.

Higher extraversion was associated with an increased likelihood of transitioning from MCI back to NCI (HR, 1.12; 95% CI, 1.03-1.22), and although extraversion was not associated with a longer total lifespan, participants who scored high on extraversion, as well as those who scored low on conscientiousness or low on neuroticism, maintained normal cognitive function longer than other participants.

“Our results suggest that high conscientiousness and low neuroticism may protect individuals against mild cognitive impairment,” said Dr. Yoneda.

Importantly, individuals who were either higher in conscientiousness, higher in extraversion, or lower in neuroticism had more years of “cognitive healthspan,” meaning more years without cognitive impairment,” she added.

In addition, “individuals lower in neuroticism and higher in extraversion were more likely to recover after receiving an MCI diagnosis, suggesting that these traits may be protective even after an individual starts to progress to dementia,” she said.

The authors note that the study focused on only three of the Big Five personality traits, while the other 2 – openness to experience and agreeableness – may also be associated with cognitive aging processes and mortality.

Nevertheless, given the current results, alongside extensive research in the personality field, aiming to increase conscientiousness through persistent behavioral change is one potential strategy for promoting healthy cognitive aging, Dr. Yoneda said.
 

‘Invaluable window’

In a comment, Brent Roberts, PhD, professor of psychology, University of Illinois Urbana-Champaign, said the study provides an “invaluable window into how personality affects the process of decline and either accelerates it, as in the role of neuroticism, or decelerates it, as in the role of conscientiousness.”

“I think the most fascinating finding was the fact that extraversion was related to transitioning from MCI back to NCI. These types of transitions have simply not been part of prior research, and it provides utterly unique insights and opportunities for interventions that may actually help people recover from a decline,” said Dr. Roberts, who was not involved in the research.

Claire Sexton, DPhil, Alzheimer’s Association director of scientific programs and outreach, called the paper “novel” because it investigated the transitions between normal cognition and mild impairment and between mild impairment and dementia.

Dr. Sexton, who was associated with this research team, cautioned that is it observational, “so it can illuminate associations or correlations, but not causes. As a result, we can’t say for sure what the mechanisms are behind these potential connections between personality and cognition, and more research is needed.”

The research was supported by the Alzheimer Society Research Program, Social Sciences and Humanities Research Council, and the National Institute on Aging of the National Institutes of Health. Dr. Yoneda and co-authors, Dr. Roberts, and Dr. Sexton have disclosed no relevant financial relationships.

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

Extraverts and individuals who are disciplined are less likely to experience cognitive decline later in life, whereas those with neuroticism have an increased risk for cognitive dysfunction, new research shows.

Investigators analyzed data from almost 2,000 individuals enrolled in the Rush Memory and Aging Project (MAP) – a longitudinal study of older adults living in the greater Chicago metropolitan region and northeastern Illinois – with recruitment that began in 1997 and continues through today. Participants received a personality assessment as well as annual assessments of their cognitive abilities.

Those with high scores on measures of conscientiousness were significantly less likely to progress from normal cognition to mild cognitive impairment (MCI) during the study. In fact, scoring an extra 1 standard deviation on the conscientiousness scale was associated with a 22% lower risk of transitioning from no cognitive impairment (NCI) to MCI. On the other hand, scoring an additional 1 SD on a neuroticism scale was associated with a 12% increased risk of transitioning to MCI.

Participants who scored high on extraversion, as well as those who scored high on conscientiousness or low on neuroticism, tended to maintain normal cognitive functioning longer than other participants.

“Personality traits reflect relatively enduring patterns of thinking and behaving, which may cumulatively affect engagement in healthy and unhealthy behaviors and thought patterns across the lifespan,” lead author Tomiko Yoneda, PhD, a postdoctoral researcher in the department of medical social sciences, Northwestern University, Chicago, said in an interview.

“The accumulation of lifelong experiences may then contribute to susceptibility of particular diseases or disorders, such as mild cognitive impairment, or contribute to individual differences in the ability to withstand age-related neurological changes,” she added.

The study was published online in the Journal of Personality and Social Psychology.
 

Competing risk factors

Personality traits “reflect an individual’s persistent patterns of thinking, feeling, and behaving,” Dr. Yoneda said.

“For example, conscientiousness is characterized by competence, dutifulness, and self-discipline, while neuroticism is characterized by anxiety, depressive symptoms, and emotional instability. Likewise, individuals high in extraversion tend to be enthusiastic, gregarious, talkative, and assertive,” she added.

Previous research “suggests that low conscientiousness and high neuroticism are associated with an increased risk of cognitive impairment,” she continued. However, “there is also an increased risk of death in older adulthood – in other words, these outcomes are ‘competing risk factors.’”

Dr. Yoneda said her team wanted to “examine the impact of personality traits on the simultaneous risk of transitioning to mild cognitive impairment, dementia, and death.”  

For the study, the researchers analyzed data from 1,954 participants in MAP (mean age at baseline 80 years, 73.7% female, 86.8% White), who received a personality assessment and annual assessments of their cognitive abilities.

To assess personality traits – in particular, conscientiousness, neuroticism, and extraversion – the researchers used the NEO Five Factor Inventory (NEO-FFI). They also used multistate survival modeling to examine the potential association between these traits and transitions from one cognitive status category to another (NCI, MCI, and dementia) and to death.
 

Cognitive healthspan

By the end of the study, over half of the sample (54%) had died.

Most transitions showed “relative stability in cognitive status across measurement occasions.”

• NCI to NCI (n = 7,368)
• MCI to MCI (n = 1,244)
• Dementia to dementia (n = 876)

There were 725 “backward transitions” from MCI to NCI, “which may reflect improvement or within-person variability in cognitive functioning, or learning effects,” the authors note.

There were only 114 “backward transitions” from dementia to MCI and only 12 from dementia to NCI, “suggesting that improvement in cognitive status was relatively rare, particularly once an individual progresses to dementia.”

After adjusting for demographics, depressive symptoms, and apolipoprotein (APOE) ε4 allele, the researchers found that personality traits were the most important factors in the transition from NCI to MCI.

Higher conscientiousness was associated with a decreased risk of transitioning from NCI to MCI (hazard ratio, 0.78; 95% confidence interval, 0.72-0.85). Conversely, higher neuroticism was associated with an increased risk of transitioning from NCI to MCI (HR, 1.12; 95% CI, 1.04-1.21) and a significantly decreased likelihood of transition back from MCI to NCI (HR, 0.90; 95% CI, 0.81-1.00).

Scoring ~6 points on a conscientiousness scale ranging from 0-48 (that is, 1 SD on the scale) was significantly associated with ~22% lower risk of transitioning forward from NCI to MCI, while scoring ~7 more points on a neuroticism scale (1 SD) was significantly associated with ~12% higher risk of transitioning from NCI to MCI.

Higher extraversion was associated with an increased likelihood of transitioning from MCI back to NCI (HR, 1.12; 95% CI, 1.03-1.22), and although extraversion was not associated with a longer total lifespan, participants who scored high on extraversion, as well as those who scored low on conscientiousness or low on neuroticism, maintained normal cognitive function longer than other participants.

“Our results suggest that high conscientiousness and low neuroticism may protect individuals against mild cognitive impairment,” said Dr. Yoneda.

Importantly, individuals who were either higher in conscientiousness, higher in extraversion, or lower in neuroticism had more years of “cognitive healthspan,” meaning more years without cognitive impairment,” she added.

In addition, “individuals lower in neuroticism and higher in extraversion were more likely to recover after receiving an MCI diagnosis, suggesting that these traits may be protective even after an individual starts to progress to dementia,” she said.

The authors note that the study focused on only three of the Big Five personality traits, while the other 2 – openness to experience and agreeableness – may also be associated with cognitive aging processes and mortality.

Nevertheless, given the current results, alongside extensive research in the personality field, aiming to increase conscientiousness through persistent behavioral change is one potential strategy for promoting healthy cognitive aging, Dr. Yoneda said.
 

‘Invaluable window’

In a comment, Brent Roberts, PhD, professor of psychology, University of Illinois Urbana-Champaign, said the study provides an “invaluable window into how personality affects the process of decline and either accelerates it, as in the role of neuroticism, or decelerates it, as in the role of conscientiousness.”

“I think the most fascinating finding was the fact that extraversion was related to transitioning from MCI back to NCI. These types of transitions have simply not been part of prior research, and it provides utterly unique insights and opportunities for interventions that may actually help people recover from a decline,” said Dr. Roberts, who was not involved in the research.

Claire Sexton, DPhil, Alzheimer’s Association director of scientific programs and outreach, called the paper “novel” because it investigated the transitions between normal cognition and mild impairment and between mild impairment and dementia.

Dr. Sexton, who was associated with this research team, cautioned that is it observational, “so it can illuminate associations or correlations, but not causes. As a result, we can’t say for sure what the mechanisms are behind these potential connections between personality and cognition, and more research is needed.”

The research was supported by the Alzheimer Society Research Program, Social Sciences and Humanities Research Council, and the National Institute on Aging of the National Institutes of Health. Dr. Yoneda and co-authors, Dr. Roberts, and Dr. Sexton have disclosed no relevant financial relationships.

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

Emotional or sexual abuse in childhood may increase risk of multiple sclerosis (MS) in women, and risk may increase further with exposure to multiple kinds of abuse, according to the first prospective cohort study of its kind.

More research is needed to uncover underlying mechanisms of action, according to lead author Karine Eid, MD, a PhD candidate at Haukeland University Hospital, Bergen, Norway, and colleagues.

“Trauma and stressful life events have been associated with an increased risk of autoimmune disorders,” the investigators wrote in the Journal Of Neurology, Neurosurgery, & Psychiatry. “Whether adverse events in childhood can have an impact on MS susceptibility is not known.”

The present study recruited participants from the Norwegian Mother, Father and Child cohort, a population consisting of Norwegian women who were pregnant from 1999 to 2008. Of the 77,997 participating women, 14,477 reported emotional, sexual, and/or physical abuse in childhood, while the remaining 63,520 women reported no abuse. After a mean follow-up of 13 years, 300 women were diagnosed with MS, among whom 24% reported a history of childhood abuse, compared with 19% among women who did not develop MS.

To look for associations between childhood abuse and risk of MS, the investigators used a Cox model adjusted for confounders and mediators, including smoking, obesity, adult socioeconomic factors, and childhood social status. The model revealed that emotional abuse increased the risk of MS by 40% (hazard ratio [HR] 1.40; 95% confidence interval [CI], 1.03-1.90), and sexual abuse increased the risk of MS by 65% (HR 1.65; 95% CI, 1.13-2.39).

Although physical abuse alone did not significantly increase risk of MS (HR 1.31; 95% CI, 0.83-2.06), it did contribute to a dose-response relationship when women were exposed to more than one type of childhood abuse. Women exposed to two out of three abuse categories had a 66% increased risk of MS (HR 1.66; 95% CI, 1.04-2.67), whereas women exposed to all three types of abuse had the highest risk of MS, at 93% (HR 1.93; 95% CI, 1.02-3.67).

Dr. Eid and colleagues noted that their findings are supported by previous retrospective research, and discussed possible mechanisms of action.

“The increased risk of MS after exposure to childhood sexual and emotional abuse may have a biological explanation,” they wrote. “Childhood abuse can cause dysregulation of the hypothalamic-pituitary-adrenal axis, lead to oxidative stress, and induce a proinflammatory state decades into adulthood. Psychological stress has been shown to disrupt the blood-brain barrier and cause epigenetic changes that may increase the risk of neurodegenerative disorders, including MS.

“The underlying mechanisms behind this association should be investigated further,” they concluded.
 

Study findings should guide interventions

Commenting on the research, Ruth Ann Marrie, MD, PhD, professor of medicine and community health sciences and director of the multiple sclerosis clinic at Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, said that the present study “has several strengths compared to prior studies – including that it is prospective and the sample size.”

Dr. Marrie, who was not involved in the study, advised clinicians in the field to take note of the findings, as patients with a history of abuse may need unique interventions.

“Providers need to recognize the higher prevalence of childhood maltreatment in people with MS,” Dr. Marrie said in an interview. “These findings dovetail with others that suggest that adverse childhood experiences are associated with increased mental health concerns and pain catastrophizing in people with MS. Affected individuals may benefit from additional psychological supports and trauma-informed care.”

Tiffany Joy Braley, MD, associate professor of neurology, and Carri Polick, RN and PhD candidate at the school of nursing, University of Michigan, Ann Arbor, who published a case report last year highlighting the importance of evaluating stress exposure in MS, suggested that the findings should guide interventions at both a system and patient level.

“Although a cause-and-effect relationship cannot be established by the current study, these and related findings should be considered in the context of system level and policy interventions that address links between environment and health care disparities,” they said in a joint, written comment. “Given recent impetus to provide trauma-informed health care, these data could be particularly informative in neurological conditions which are associated with high mental health comorbidity. Traumatic stress screening practices could lead to referrals for appropriate support services and more personalized health care.”

While several mechanisms have been proposed to explain the link between traumatic stress and MS, more work is needed in this area, they added.

This knowledge gap was acknowledged by Dr. Marrie.

“Our understanding of the etiology of MS remains incomplete,” Dr. Marrie said. “We still need a better understanding of mechanisms by which adverse childhood experiences lead to MS, how they interact with other risk factors for MS (beyond smoking and obesity), and whether there are any interventions that can mitigate the risk of developing MS that is associated with adverse childhood experiences.”

The investigators disclosed relationships with Novartis, Biogen, Merck, and others. Dr. Marrie receives research support from the Canadian Institutes of Health Research, the National Multiple Sclerosis Society, MS Society of Canada, the Consortium of Multiple Sclerosis Centers, Crohn’s and Colitis Canada, Research Manitoba, and the Arthritis Society; she has no pharmaceutical support. Dr. Braley and Ms. Polick reported no conflicts of interest.

Emotional or sexual abuse in childhood may increase risk of multiple sclerosis (MS) in women, and risk may increase further with exposure to multiple kinds of abuse, according to the first prospective cohort study of its kind.

More research is needed to uncover underlying mechanisms of action, according to lead author Karine Eid, MD, a PhD candidate at Haukeland University Hospital, Bergen, Norway, and colleagues.

“Trauma and stressful life events have been associated with an increased risk of autoimmune disorders,” the investigators wrote in the Journal Of Neurology, Neurosurgery, & Psychiatry. “Whether adverse events in childhood can have an impact on MS susceptibility is not known.”

The present study recruited participants from the Norwegian Mother, Father and Child cohort, a population consisting of Norwegian women who were pregnant from 1999 to 2008. Of the 77,997 participating women, 14,477 reported emotional, sexual, and/or physical abuse in childhood, while the remaining 63,520 women reported no abuse. After a mean follow-up of 13 years, 300 women were diagnosed with MS, among whom 24% reported a history of childhood abuse, compared with 19% among women who did not develop MS.

To look for associations between childhood abuse and risk of MS, the investigators used a Cox model adjusted for confounders and mediators, including smoking, obesity, adult socioeconomic factors, and childhood social status. The model revealed that emotional abuse increased the risk of MS by 40% (hazard ratio [HR] 1.40; 95% confidence interval [CI], 1.03-1.90), and sexual abuse increased the risk of MS by 65% (HR 1.65; 95% CI, 1.13-2.39).

Although physical abuse alone did not significantly increase risk of MS (HR 1.31; 95% CI, 0.83-2.06), it did contribute to a dose-response relationship when women were exposed to more than one type of childhood abuse. Women exposed to two out of three abuse categories had a 66% increased risk of MS (HR 1.66; 95% CI, 1.04-2.67), whereas women exposed to all three types of abuse had the highest risk of MS, at 93% (HR 1.93; 95% CI, 1.02-3.67).

Dr. Eid and colleagues noted that their findings are supported by previous retrospective research, and discussed possible mechanisms of action.

“The increased risk of MS after exposure to childhood sexual and emotional abuse may have a biological explanation,” they wrote. “Childhood abuse can cause dysregulation of the hypothalamic-pituitary-adrenal axis, lead to oxidative stress, and induce a proinflammatory state decades into adulthood. Psychological stress has been shown to disrupt the blood-brain barrier and cause epigenetic changes that may increase the risk of neurodegenerative disorders, including MS.

“The underlying mechanisms behind this association should be investigated further,” they concluded.
 

Study findings should guide interventions

Commenting on the research, Ruth Ann Marrie, MD, PhD, professor of medicine and community health sciences and director of the multiple sclerosis clinic at Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, said that the present study “has several strengths compared to prior studies – including that it is prospective and the sample size.”

Dr. Marrie, who was not involved in the study, advised clinicians in the field to take note of the findings, as patients with a history of abuse may need unique interventions.

“Providers need to recognize the higher prevalence of childhood maltreatment in people with MS,” Dr. Marrie said in an interview. “These findings dovetail with others that suggest that adverse childhood experiences are associated with increased mental health concerns and pain catastrophizing in people with MS. Affected individuals may benefit from additional psychological supports and trauma-informed care.”

Tiffany Joy Braley, MD, associate professor of neurology, and Carri Polick, RN and PhD candidate at the school of nursing, University of Michigan, Ann Arbor, who published a case report last year highlighting the importance of evaluating stress exposure in MS, suggested that the findings should guide interventions at both a system and patient level.

“Although a cause-and-effect relationship cannot be established by the current study, these and related findings should be considered in the context of system level and policy interventions that address links between environment and health care disparities,” they said in a joint, written comment. “Given recent impetus to provide trauma-informed health care, these data could be particularly informative in neurological conditions which are associated with high mental health comorbidity. Traumatic stress screening practices could lead to referrals for appropriate support services and more personalized health care.”

While several mechanisms have been proposed to explain the link between traumatic stress and MS, more work is needed in this area, they added.

This knowledge gap was acknowledged by Dr. Marrie.

“Our understanding of the etiology of MS remains incomplete,” Dr. Marrie said. “We still need a better understanding of mechanisms by which adverse childhood experiences lead to MS, how they interact with other risk factors for MS (beyond smoking and obesity), and whether there are any interventions that can mitigate the risk of developing MS that is associated with adverse childhood experiences.”

The investigators disclosed relationships with Novartis, Biogen, Merck, and others. Dr. Marrie receives research support from the Canadian Institutes of Health Research, the National Multiple Sclerosis Society, MS Society of Canada, the Consortium of Multiple Sclerosis Centers, Crohn’s and Colitis Canada, Research Manitoba, and the Arthritis Society; she has no pharmaceutical support. Dr. Braley and Ms. Polick reported no conflicts of interest.

Emotional or sexual abuse in childhood may increase risk of multiple sclerosis (MS) in women, and risk may increase further with exposure to multiple kinds of abuse, according to the first prospective cohort study of its kind.

More research is needed to uncover underlying mechanisms of action, according to lead author Karine Eid, MD, a PhD candidate at Haukeland University Hospital, Bergen, Norway, and colleagues.

“Trauma and stressful life events have been associated with an increased risk of autoimmune disorders,” the investigators wrote in the Journal Of Neurology, Neurosurgery, & Psychiatry. “Whether adverse events in childhood can have an impact on MS susceptibility is not known.”

The present study recruited participants from the Norwegian Mother, Father and Child cohort, a population consisting of Norwegian women who were pregnant from 1999 to 2008. Of the 77,997 participating women, 14,477 reported emotional, sexual, and/or physical abuse in childhood, while the remaining 63,520 women reported no abuse. After a mean follow-up of 13 years, 300 women were diagnosed with MS, among whom 24% reported a history of childhood abuse, compared with 19% among women who did not develop MS.

To look for associations between childhood abuse and risk of MS, the investigators used a Cox model adjusted for confounders and mediators, including smoking, obesity, adult socioeconomic factors, and childhood social status. The model revealed that emotional abuse increased the risk of MS by 40% (hazard ratio [HR] 1.40; 95% confidence interval [CI], 1.03-1.90), and sexual abuse increased the risk of MS by 65% (HR 1.65; 95% CI, 1.13-2.39).

Although physical abuse alone did not significantly increase risk of MS (HR 1.31; 95% CI, 0.83-2.06), it did contribute to a dose-response relationship when women were exposed to more than one type of childhood abuse. Women exposed to two out of three abuse categories had a 66% increased risk of MS (HR 1.66; 95% CI, 1.04-2.67), whereas women exposed to all three types of abuse had the highest risk of MS, at 93% (HR 1.93; 95% CI, 1.02-3.67).

Dr. Eid and colleagues noted that their findings are supported by previous retrospective research, and discussed possible mechanisms of action.

“The increased risk of MS after exposure to childhood sexual and emotional abuse may have a biological explanation,” they wrote. “Childhood abuse can cause dysregulation of the hypothalamic-pituitary-adrenal axis, lead to oxidative stress, and induce a proinflammatory state decades into adulthood. Psychological stress has been shown to disrupt the blood-brain barrier and cause epigenetic changes that may increase the risk of neurodegenerative disorders, including MS.

“The underlying mechanisms behind this association should be investigated further,” they concluded.
 

Study findings should guide interventions

Commenting on the research, Ruth Ann Marrie, MD, PhD, professor of medicine and community health sciences and director of the multiple sclerosis clinic at Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, said that the present study “has several strengths compared to prior studies – including that it is prospective and the sample size.”

Dr. Marrie, who was not involved in the study, advised clinicians in the field to take note of the findings, as patients with a history of abuse may need unique interventions.

“Providers need to recognize the higher prevalence of childhood maltreatment in people with MS,” Dr. Marrie said in an interview. “These findings dovetail with others that suggest that adverse childhood experiences are associated with increased mental health concerns and pain catastrophizing in people with MS. Affected individuals may benefit from additional psychological supports and trauma-informed care.”

Tiffany Joy Braley, MD, associate professor of neurology, and Carri Polick, RN and PhD candidate at the school of nursing, University of Michigan, Ann Arbor, who published a case report last year highlighting the importance of evaluating stress exposure in MS, suggested that the findings should guide interventions at both a system and patient level.

“Although a cause-and-effect relationship cannot be established by the current study, these and related findings should be considered in the context of system level and policy interventions that address links between environment and health care disparities,” they said in a joint, written comment. “Given recent impetus to provide trauma-informed health care, these data could be particularly informative in neurological conditions which are associated with high mental health comorbidity. Traumatic stress screening practices could lead to referrals for appropriate support services and more personalized health care.”

While several mechanisms have been proposed to explain the link between traumatic stress and MS, more work is needed in this area, they added.

This knowledge gap was acknowledged by Dr. Marrie.

“Our understanding of the etiology of MS remains incomplete,” Dr. Marrie said. “We still need a better understanding of mechanisms by which adverse childhood experiences lead to MS, how they interact with other risk factors for MS (beyond smoking and obesity), and whether there are any interventions that can mitigate the risk of developing MS that is associated with adverse childhood experiences.”

The investigators disclosed relationships with Novartis, Biogen, Merck, and others. Dr. Marrie receives research support from the Canadian Institutes of Health Research, the National Multiple Sclerosis Society, MS Society of Canada, the Consortium of Multiple Sclerosis Centers, Crohn’s and Colitis Canada, Research Manitoba, and the Arthritis Society; she has no pharmaceutical support. Dr. Braley and Ms. Polick reported no conflicts of interest.

A 35-year-old man comes to clinic for evaluation of new, severe headaches. He reports that these started 3 days ago. His headache is worse when he stands, and resolves when he lies down. Valsalva maneuver makes the headache much worse. The headaches are present in the occipital region. He also has noticed the onset of tinnitus. A physical exam reveals that his blood pressure is 110/70 mm Hg, his pulse is 60 beats per minute, and his temperature is 36.4° C. His standing BP is 105/60 mm Hg and standing pulse is 66 bpm. Both his neurologic exam and noncontrast head CT scan are normal.


Which of the following is the most likely diagnosis?

A) Subarachnoid hemorrhage

B) POTS (Postural orthostatic tachycardia syndrome)

C) Hypnic headache

D) Spontaneous intracranial hypotension (SIH)

E) Acoustic neuroma

The most likely cause for this patient’s headaches given his set of symptoms is spontaneous intracranial hypotension. Orthostatic headaches are common with POTS, but the absence of tachycardia with standing makes this diagnosis unlikely.

Spontaneous intracranial hypotension has symptoms that we are all familiar with in the post–lumbar puncture patient. In patients with post-LP headache, the positional nature makes it easy to diagnose. Patients who have had a lumbar puncture have a clear reason they have a cerebrospinal fluid (CSF) leak, leading to intracranial hypotension. Those with SIH do not.
 

Related research

Schievink summarized a lot of useful information in a review of patients with spontaneous intracranial hypotension.¹ The incidence is about 5/100,000, with the most common age around 40 years old. The most common symptom is orthostatic headache. The headache usually occurs within 15 minutes upon standing, and many patients have the onset of headache rapidly upon standing.

Usually the headache improves with lying down, and it is often brought on with Valsalva maneuver. Many patients report headaches that are worse in the second half of the day.

Orthostatic headache occurs in almost all patients with spontaneous intracranial hypotension, but in one series it occurred only in 77% of patients with SIH.² The patients who did not have typical headaches are more likely to have auditory symptoms such as tinnitus and muffled hearing.³

When you suspect SIH, appropriate workup is to start with brain MR imaging with contrast. Krantz and colleagues found dural enhancement was present in 83% of cases of SIH, venous distention sign in 75%, and brain sagging in 61%.⁴

About 10% of patients with SIH have normal brain imaging, so if the clinical features strongly suggest the diagnosis, moving on to spinal imaging with CT myelography or spinal MR are appropriate next steps.⁵

The causes of SIH are meningeal diverticula (usually in the thoracic or upper lumbar regions), ventral dural tears (usually from osteophytes), and cerebrospinal fluid–venous fistulas. Treatment of SIH has traditionally included a conservative approach of bed rest, oral hydration, and caffeine. The effectiveness of this is unknown, and, in one small series, 61% had headache symptoms at 6 months.⁶

Epidural blood patches are likely more rapidly effective than conservative therapy. In one study comparing the two treatments, Chung and colleagues found that 77% of the patients who received an epidural blood patch had complete headache relief at 4 weeks, compared with 40% of those who received conservative measures (P < .05).⁷
 

Clinical pearls

• Strongly consider SIH in patients with positional headache.
• Brain MR should be the first diagnostic test.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and serves as 3rd-year medical student clerkship director at the University of Washington. He is a member of the editorial advisory board of Internal Medicine News. Dr. Paauw has no conflicts to disclose. Contact him at [email protected].

References

1. Schievink WI. Spontaneous spinal cerebrospinal fluid leaks and intracranial hypotension. JAMA. 2006;295:2286-96.

2. Mea E et al. Headache attributed to spontaneous intracranial hypotension. Neurol Sci. 2008;29:164-65.

3. Krantz PG et al. Spontaneous Intracranial Hypotension: 10 Myths and Misperceptions. Headache. 2018;58:948-59.

4. Krantz PG et. al. Imaging signs in spontaneous intracranial hypotension: prevalence and relationship to CSF pressure. AJNR Am J Neuroradiol. 2016;37:1374-8.

5. Krantz PG et al. Spontaneous intracranial hypotension: Pathogenesis, diagnosis, and treatment. Neuroimaging Clin N Am. 2019;29:581-94.

6. Kong D-S et. al. Clinical features and long-term results of spontaneous intracranial hypotension. Neurosurgery. 2005;57:91-6.

7. Chung SJ et al. Short- and long-term outcomes of spontaneous CSF hypovolemia. Eur Neurol. 2005;54:63-7.

A 35-year-old man comes to clinic for evaluation of new, severe headaches. He reports that these started 3 days ago. His headache is worse when he stands, and resolves when he lies down. Valsalva maneuver makes the headache much worse. The headaches are present in the occipital region. He also has noticed the onset of tinnitus. A physical exam reveals that his blood pressure is 110/70 mm Hg, his pulse is 60 beats per minute, and his temperature is 36.4° C. His standing BP is 105/60 mm Hg and standing pulse is 66 bpm. Both his neurologic exam and noncontrast head CT scan are normal.


Which of the following is the most likely diagnosis?

A) Subarachnoid hemorrhage

B) POTS (Postural orthostatic tachycardia syndrome)

C) Hypnic headache

D) Spontaneous intracranial hypotension (SIH)

E) Acoustic neuroma

The most likely cause for this patient’s headaches given his set of symptoms is spontaneous intracranial hypotension. Orthostatic headaches are common with POTS, but the absence of tachycardia with standing makes this diagnosis unlikely.

Spontaneous intracranial hypotension has symptoms that we are all familiar with in the post–lumbar puncture patient. In patients with post-LP headache, the positional nature makes it easy to diagnose. Patients who have had a lumbar puncture have a clear reason they have a cerebrospinal fluid (CSF) leak, leading to intracranial hypotension. Those with SIH do not.
 

Related research

Schievink summarized a lot of useful information in a review of patients with spontaneous intracranial hypotension.¹ The incidence is about 5/100,000, with the most common age around 40 years old. The most common symptom is orthostatic headache. The headache usually occurs within 15 minutes upon standing, and many patients have the onset of headache rapidly upon standing.

Usually the headache improves with lying down, and it is often brought on with Valsalva maneuver. Many patients report headaches that are worse in the second half of the day.

Orthostatic headache occurs in almost all patients with spontaneous intracranial hypotension, but in one series it occurred only in 77% of patients with SIH.² The patients who did not have typical headaches are more likely to have auditory symptoms such as tinnitus and muffled hearing.³

When you suspect SIH, appropriate workup is to start with brain MR imaging with contrast. Krantz and colleagues found dural enhancement was present in 83% of cases of SIH, venous distention sign in 75%, and brain sagging in 61%.⁴

About 10% of patients with SIH have normal brain imaging, so if the clinical features strongly suggest the diagnosis, moving on to spinal imaging with CT myelography or spinal MR are appropriate next steps.⁵

The causes of SIH are meningeal diverticula (usually in the thoracic or upper lumbar regions), ventral dural tears (usually from osteophytes), and cerebrospinal fluid–venous fistulas. Treatment of SIH has traditionally included a conservative approach of bed rest, oral hydration, and caffeine. The effectiveness of this is unknown, and, in one small series, 61% had headache symptoms at 6 months.⁶

Epidural blood patches are likely more rapidly effective than conservative therapy. In one study comparing the two treatments, Chung and colleagues found that 77% of the patients who received an epidural blood patch had complete headache relief at 4 weeks, compared with 40% of those who received conservative measures (P < .05).⁷
 

Clinical pearls

• Strongly consider SIH in patients with positional headache.
• Brain MR should be the first diagnostic test.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and serves as 3rd-year medical student clerkship director at the University of Washington. He is a member of the editorial advisory board of Internal Medicine News. Dr. Paauw has no conflicts to disclose. Contact him at [email protected].

References

1. Schievink WI. Spontaneous spinal cerebrospinal fluid leaks and intracranial hypotension. JAMA. 2006;295:2286-96.

2. Mea E et al. Headache attributed to spontaneous intracranial hypotension. Neurol Sci. 2008;29:164-65.

3. Krantz PG et al. Spontaneous Intracranial Hypotension: 10 Myths and Misperceptions. Headache. 2018;58:948-59.

4. Krantz PG et. al. Imaging signs in spontaneous intracranial hypotension: prevalence and relationship to CSF pressure. AJNR Am J Neuroradiol. 2016;37:1374-8.

5. Krantz PG et al. Spontaneous intracranial hypotension: Pathogenesis, diagnosis, and treatment. Neuroimaging Clin N Am. 2019;29:581-94.

6. Kong D-S et. al. Clinical features and long-term results of spontaneous intracranial hypotension. Neurosurgery. 2005;57:91-6.

7. Chung SJ et al. Short- and long-term outcomes of spontaneous CSF hypovolemia. Eur Neurol. 2005;54:63-7.

A 35-year-old man comes to clinic for evaluation of new, severe headaches. He reports that these started 3 days ago. His headache is worse when he stands, and resolves when he lies down. Valsalva maneuver makes the headache much worse. The headaches are present in the occipital region. He also has noticed the onset of tinnitus. A physical exam reveals that his blood pressure is 110/70 mm Hg, his pulse is 60 beats per minute, and his temperature is 36.4° C. His standing BP is 105/60 mm Hg and standing pulse is 66 bpm. Both his neurologic exam and noncontrast head CT scan are normal.


Which of the following is the most likely diagnosis?

A) Subarachnoid hemorrhage

B) POTS (Postural orthostatic tachycardia syndrome)

C) Hypnic headache

D) Spontaneous intracranial hypotension (SIH)

E) Acoustic neuroma

The most likely cause for this patient’s headaches given his set of symptoms is spontaneous intracranial hypotension. Orthostatic headaches are common with POTS, but the absence of tachycardia with standing makes this diagnosis unlikely.

Spontaneous intracranial hypotension has symptoms that we are all familiar with in the post–lumbar puncture patient. In patients with post-LP headache, the positional nature makes it easy to diagnose. Patients who have had a lumbar puncture have a clear reason they have a cerebrospinal fluid (CSF) leak, leading to intracranial hypotension. Those with SIH do not.
 

Related research

Schievink summarized a lot of useful information in a review of patients with spontaneous intracranial hypotension.¹ The incidence is about 5/100,000, with the most common age around 40 years old. The most common symptom is orthostatic headache. The headache usually occurs within 15 minutes upon standing, and many patients have the onset of headache rapidly upon standing.

Usually the headache improves with lying down, and it is often brought on with Valsalva maneuver. Many patients report headaches that are worse in the second half of the day.

Orthostatic headache occurs in almost all patients with spontaneous intracranial hypotension, but in one series it occurred only in 77% of patients with SIH.² The patients who did not have typical headaches are more likely to have auditory symptoms such as tinnitus and muffled hearing.³

When you suspect SIH, appropriate workup is to start with brain MR imaging with contrast. Krantz and colleagues found dural enhancement was present in 83% of cases of SIH, venous distention sign in 75%, and brain sagging in 61%.⁴

About 10% of patients with SIH have normal brain imaging, so if the clinical features strongly suggest the diagnosis, moving on to spinal imaging with CT myelography or spinal MR are appropriate next steps.⁵

The causes of SIH are meningeal diverticula (usually in the thoracic or upper lumbar regions), ventral dural tears (usually from osteophytes), and cerebrospinal fluid–venous fistulas. Treatment of SIH has traditionally included a conservative approach of bed rest, oral hydration, and caffeine. The effectiveness of this is unknown, and, in one small series, 61% had headache symptoms at 6 months.⁶

Epidural blood patches are likely more rapidly effective than conservative therapy. In one study comparing the two treatments, Chung and colleagues found that 77% of the patients who received an epidural blood patch had complete headache relief at 4 weeks, compared with 40% of those who received conservative measures (P < .05).⁷
 

Clinical pearls

• Strongly consider SIH in patients with positional headache.
• Brain MR should be the first diagnostic test.

Dr. Paauw is professor of medicine in the division of general internal medicine at the University of Washington, Seattle, and serves as 3rd-year medical student clerkship director at the University of Washington. He is a member of the editorial advisory board of Internal Medicine News. Dr. Paauw has no conflicts to disclose. Contact him at [email protected].

References

1. Schievink WI. Spontaneous spinal cerebrospinal fluid leaks and intracranial hypotension. JAMA. 2006;295:2286-96.

2. Mea E et al. Headache attributed to spontaneous intracranial hypotension. Neurol Sci. 2008;29:164-65.

3. Krantz PG et al. Spontaneous Intracranial Hypotension: 10 Myths and Misperceptions. Headache. 2018;58:948-59.

4. Krantz PG et. al. Imaging signs in spontaneous intracranial hypotension: prevalence and relationship to CSF pressure. AJNR Am J Neuroradiol. 2016;37:1374-8.

5. Krantz PG et al. Spontaneous intracranial hypotension: Pathogenesis, diagnosis, and treatment. Neuroimaging Clin N Am. 2019;29:581-94.

6. Kong D-S et. al. Clinical features and long-term results of spontaneous intracranial hypotension. Neurosurgery. 2005;57:91-6.

7. Chung SJ et al. Short- and long-term outcomes of spontaneous CSF hypovolemia. Eur Neurol. 2005;54:63-7.

Key takeaways

Metformin may be associated with a lower risk of neurodegenerative disease, in particular when the drug has been prescribed for at least 4 years, according to a systematic review and meta-analysis of longitudinal data.

However, the heterogeneity between the available studies and the potential heterogeneity of diagnostic criteria may mean that validation studies are needed.
 

Why is this important?

Data suggest that metformin, the most commonly prescribed antidiabetic drug, may be neuroprotective, while diabetes is associated with an excess risk of neurodegenerative disease. Results of studies conducted specifically to investigate the benefit of the antidiabetic drug on cognitive prognosis have been unclear. A meta-analysis was published in 2020, but it included cross-sectional and case-control studies. Given the long observation period needed to measure such an outcome, only cohort studies conducted over several years can provide reliable results. This new meta-analysis attempts to circumvent this limitation.

Methods

The meta-analysis was conducted using studies published up to March 2021 that met the inclusion criteria (population-based cohort studies published in English in which the administration of metformin and associated risk of exposure were reported).

Main results

Twelve studies were included in this analysis, of which eight were retrospective and 11 were considered to be of good methodologic quality. In total, 194,792 patients were included.

Pooled data showed that the relative risk associated with onset of neurodegenerative disease was 0.77 (95% CI, 0.67-0.88) for patients with diabetes taking metformin versus those not taking metformin. However, heterogeneity between studies was high (I2; 78.8%; P < .001).

The effect was greater with longer metformin use, with an RR of 0.29 (95% CI, 0.13-0.44) for those who took metformin for 4 years or more. Similarly, the studies conducted in Asian countries versus other locations suggested an added benefit for this population (RR, 0.69; 95% CI, 0.64-0.74).

Sensitivity analyses confirmed these results, and subtype analyses showed no difference according to the nature of the neurodegenerative disease.

A version of this article first appeared on Univadis.

Key takeaways

Metformin may be associated with a lower risk of neurodegenerative disease, in particular when the drug has been prescribed for at least 4 years, according to a systematic review and meta-analysis of longitudinal data.

However, the heterogeneity between the available studies and the potential heterogeneity of diagnostic criteria may mean that validation studies are needed.
 

Why is this important?

Data suggest that metformin, the most commonly prescribed antidiabetic drug, may be neuroprotective, while diabetes is associated with an excess risk of neurodegenerative disease. Results of studies conducted specifically to investigate the benefit of the antidiabetic drug on cognitive prognosis have been unclear. A meta-analysis was published in 2020, but it included cross-sectional and case-control studies. Given the long observation period needed to measure such an outcome, only cohort studies conducted over several years can provide reliable results. This new meta-analysis attempts to circumvent this limitation.

Methods

The meta-analysis was conducted using studies published up to March 2021 that met the inclusion criteria (population-based cohort studies published in English in which the administration of metformin and associated risk of exposure were reported).

Main results

Twelve studies were included in this analysis, of which eight were retrospective and 11 were considered to be of good methodologic quality. In total, 194,792 patients were included.

Pooled data showed that the relative risk associated with onset of neurodegenerative disease was 0.77 (95% CI, 0.67-0.88) for patients with diabetes taking metformin versus those not taking metformin. However, heterogeneity between studies was high (I2; 78.8%; P < .001).

The effect was greater with longer metformin use, with an RR of 0.29 (95% CI, 0.13-0.44) for those who took metformin for 4 years or more. Similarly, the studies conducted in Asian countries versus other locations suggested an added benefit for this population (RR, 0.69; 95% CI, 0.64-0.74).

Sensitivity analyses confirmed these results, and subtype analyses showed no difference according to the nature of the neurodegenerative disease.

A version of this article first appeared on Univadis.

Key takeaways

Metformin may be associated with a lower risk of neurodegenerative disease, in particular when the drug has been prescribed for at least 4 years, according to a systematic review and meta-analysis of longitudinal data.

However, the heterogeneity between the available studies and the potential heterogeneity of diagnostic criteria may mean that validation studies are needed.
 

Why is this important?

Data suggest that metformin, the most commonly prescribed antidiabetic drug, may be neuroprotective, while diabetes is associated with an excess risk of neurodegenerative disease. Results of studies conducted specifically to investigate the benefit of the antidiabetic drug on cognitive prognosis have been unclear. A meta-analysis was published in 2020, but it included cross-sectional and case-control studies. Given the long observation period needed to measure such an outcome, only cohort studies conducted over several years can provide reliable results. This new meta-analysis attempts to circumvent this limitation.

Methods

The meta-analysis was conducted using studies published up to March 2021 that met the inclusion criteria (population-based cohort studies published in English in which the administration of metformin and associated risk of exposure were reported).

Main results

Twelve studies were included in this analysis, of which eight were retrospective and 11 were considered to be of good methodologic quality. In total, 194,792 patients were included.

Pooled data showed that the relative risk associated with onset of neurodegenerative disease was 0.77 (95% CI, 0.67-0.88) for patients with diabetes taking metformin versus those not taking metformin. However, heterogeneity between studies was high (I2; 78.8%; P < .001).

The effect was greater with longer metformin use, with an RR of 0.29 (95% CI, 0.13-0.44) for those who took metformin for 4 years or more. Similarly, the studies conducted in Asian countries versus other locations suggested an added benefit for this population (RR, 0.69; 95% CI, 0.64-0.74).

Sensitivity analyses confirmed these results, and subtype analyses showed no difference according to the nature of the neurodegenerative disease.

A version of this article first appeared on Univadis.

Long-term cannabis use is linked to hippocampal atrophy and poorer cognitive function in midlife – known risk factors for dementia.

A large prospective, longitudinal study showed long-term cannabis users had an intelligence quotient (IQ) decline from age 18 to midlife (mean, 5.5 IQ points), poorer learning and processing speed, compared with childhood, and self-reported memory and attention problems. Long-term cannabis users also showed hippocampal atrophy at midlife (age 45), which combined with mild midlife cognitive deficits, all known risk factors for dementia.

“Long-term cannabis users – people who have used cannabis from 18 or 19 years old and continued using through midlife – showed cognitive deficits, compared with nonusers. They also showed more severe cognitive deficits, compared with long-term alcohol users and long-term tobacco users. But people who used infrequently or recreationally in midlife did not show as severe cognitive deficits. Cognitive deficits were confined to cannabis users,” lead investigator Madeline Meier, PhD, associate professor of psychology, Arizona State University, Tempe, said in an interview.

“Long-term cannabis users had smaller hippocampal volume, but we also found that smaller hippocampal volume did not explain the cognitive deficits among the long-term cannabis users,” she added.

The study was recently published online in the American Journal of Psychiatry.
 

Growing use in Boomers

Long-term cannabis use has been associated with memory problems. Studies examining the impact of cannabis use on the brain have shown conflicting results. Some suggest regular use in adolescence is associated with altered connectivity and reduced volume of brain regions involved in executive functions such as memory, learning, and impulse control compared with those who do not use cannabis.

Others found no significant structural differences between the brains of cannabis users and nonusers.

An earlier, large longitudinal study in New Zealand found that persistent cannabis use (with frequent use starting in adolescence) was associated with a loss of an average of six (or up to eight) IQ points measured in mid-adulthood.

Cannabis use is increasing among Baby Boomers – a group born between 1946 and 1964 – who used cannabis at historically high rates as young adults, and who now use it at historically high rates in midlife and as older adults.

To date, case-control studies, which are predominantly in adolescents and young adults, have found that cannabis users show subtle cognitive deficits and structural brain differences, but it is unclear whether these differences in young cannabis users might be larger in midlife and in older adults who have longer histories of use.

The study included a representative cohort of 1,037 individuals in Dunedin, New Zealand, born between April 1972 and March 1973, and followed from age 3 to 45.

Cannabis use and dependence were assessed at ages 18, 21, 26, 32, 38, and 45. IQ was assessed at ages 7, 9, 11, and 45. Specific neuropsychological functions and hippocampal volume were assessed at age 45. 

“Most of the previous research has focused on adolescent and young-adult cannabis users. What we’re looking at here is long-term cannabis users in midlife, and we’re finding that long-term users show cognitive deficits. But we’re not just looking at a snapshot of people in midlife, we’re also doing a longitudinal comparison – comparing them to themselves in childhood. We saw that long-term cannabis users showed a decline in IQ from childhood to adulthood,” said Dr. Meier. 

Participants in the study are members of the Dunedin Longitudinal Study, a representative birth cohort (n = 1,037; 91% of eligible births; 52% male) born between April 1972 and March 1973 in Dunedin, New Zealand, who participated in the first assessment at age 3.

This cohort matched socioeconomic status (SES), key health indicators, and demographics. Assessments were carried out at birth and ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32, 38, and 45. IQ was assessed at ages 7, 9, 11, and 45. Specific neuropsychological functions and hippocampal volume were assessed at age 45. 
 

Shrinking hippocampal volume

Cannabis use, cognitive function, and hippocampal volume were assessed comparing long-term cannabis users (n = 84) against five distinct groups:

• Lifelong cannabis nonusers (n = 196) – to replicate the control group most often reported in the case-control literature
• Midlife recreational cannabis users (n = 65) – to determine if cognitive deficits and structural brain differences are apparent in nonproblem users – the majority of cannabis users
• Long-term tobacco users (n = 75)
• Long-term alcohol users (n = 57) – benchmark comparisons for any cannabis findings and to disentangle potential cannabis effects from tobacco and alcohol effects
• Cannabis quitters (n = 58) – to determine whether differences are apparent after cessation

Tests were conducted on dose-response associations using continuously measured persistence of cannabis use, rigorously adjusting for numerous confounders derived from multiple longitudinal waves and data sources.

The investigators also tested whether associations between continuously measured persistence of cannabis use and cognitive deficits were mediated by hippocampal volume differences.

The hippocampus was the area of focus because it has a high density of cannabinoid receptors and is also instrumental for learning and memory, which is one of the most consistently impaired cognitive domains in cannabis users, and has been the brain region that most consistently emerges as smaller in cannabis users relative to controls. Structural MRI was done at age 45 for 875 participants (93% of age 45 participants).

Of 997 cohort members still alive at age 45, 938 (94.1%) were assessed at age 45. Age 45 participants did not differ significantly from other participants on childhood SES, childhood self-control, or childhood IQ. Cognitive functioning among midlife recreational cannabis users was similar to representative cohort norms, suggesting that infrequent recreational cannabis use in midlife is unlikely to compromise cognitive functioning.

However, long-term cannabis users did not perform significantly worse on any test than cannabis quitters. Cannabis quitters showed subtle cognitive deficits that may explain inconsistent findings on the benefits of cessation.

Smaller hippocampal volume is thought to be a possible mediator of cannabis-related cognitive deficits because the hippocampus is rich in CB1 receptors and is involved in learning and memory.

Long-term cannabis users had smaller bilateral volume in total hippocampus and 5 of 12 structurally and functionally distinct subregions (tail, hippocampal amygdala transition area, CA1, molecular layer, and dentate gyrus), and significantly smaller volumes than midlife recreational cannabis users in the left and right hippocampus, and 3 of 12 subfields (tail, CA1, and molecular layer), compared with non-users, consistent with case-control studies.
 

More potent

“If you’ve been using cannabis very long term and now are in midlife, you might want to consider quitting. Quitting is associated with slightly better cognitive performance in midlife. We also need to watch for risk of dementia. We know that people who show cognitive deficits at midlife are at elevated risk for later life dementia. And the deficits we saw among long-term cannabis users (although fairly mild), they were in the range in terms of effect size of what we see among people in other studies who have gone on to develop dementia in later life,” said Dr. Meier.

The study findings conflict with those of other studies, including one by the same research group, which compared the cognitive functioning of twins who were discordant for cannabis use and found little evidence of cannabis-related cognitive deficits. Because long-term cannabis users also use tobacco, alcohol, and other illicit drugs, disentangling cannabis effects from other substances is challenging.

“Long-term cannabis users tend to be long-term polysubstance users, so it’s hard to isolate,” said Dr. Meier.

Additionally, some group sizes were small, raising concerns about low statistical power.

“Group sizes were small but we didn’t rely only on those group comparisons; however, we did find statistical differences. We also tested highly statistically powered dose-response associations between persistence of cannabis use over ages 18-45 and each of our outcomes (IQ, learning, and processing speed in midlife) while adjusting possible alternate explanations such as low childhood IQ, other substance use, [and] socioeconomic backgrounds.

“These dose-response associations used large sample sizes, were highly powered, and took into account a number of alternative explanations. These two different approaches showed very similar findings and one bolstered the other,” said Dr. Meier.

The study’s results were based on individuals who began using cannabis in the 1980s or ‘90s, but the concentration of tetrahydrocannabinol (THC) has risen in recent years.

“When the study began, THC concentration was approximately 4%. Over the last decade we have seen it go up to 12% or even higher. A recent study surveying U.S. dispensaries found 20% THC. If THC accounts for impairment, then the effects can be larger [with higher concentrations]. One of the challenges in the U.S. is that there are laws prohibiting researchers from testing cannabis, so we have to rely on product labels, which we know are unreliable,” said Dr. Meier.

A separate report is forthcoming with results of exploratory analyses of associations between long-term cannabis use and comprehensive MRI measures of global and regional gray and white matter.

The data will also be used to answer a number of different questions about cognitive deficits, brain structure, aging preparedness, social preparedness (strength of social networks), financial and health preparedness, and biological aging (the pace of aging relative to chronological age) in long-term cannabis users, Dr. Meier noted.
 

‘Fantastic’ research

Commenting on the research for this news organization , Andrew J. Saxon, MD, professor, department of psychiatry & behavioral sciences at University of Washington, Seattle, and a member of the American Psychiatric Association’s Council on Addiction Psychiatry, said the study “provides more evidence that heavy and regular cannabis use is not benign behavior.”

“It’s a fantastic piece of research in which they enrolled participants at birth and have followed them up to age 45. In most of the other research that has been done, we have no idea what their baseline was. What’s so remarkable here is that they can clearly demonstrate the loss of IQ points from childhood to age 45,” said Dr. Saxon.

“It is clear that, in people using cannabis long term, cognition is impaired. It would be good to have a better handle on how much cognitive function can be regained if you quit, because that could be a motivator for quitting in people where cannabis is having an adverse effect on their lives,” he added.

On the issue of THC potency, Dr. Saxon said that, while it’s true the potency of cannabis is increasing in terms of THC concentrations, the question is: “Do people who use cannabis use a set amount or do they imbibe until they achieve the state of altered consciousness that they’re seeking? Although there has been some research in the area of self-regulation and cannabis potency, we do not yet have the answers to determine if there is any causation,” said Dr. Saxon.

Dr. Meier and Dr. Saxon reported no relevant financial conflicts of interest.

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

Long-term cannabis use is linked to hippocampal atrophy and poorer cognitive function in midlife – known risk factors for dementia.

A large prospective, longitudinal study showed long-term cannabis users had an intelligence quotient (IQ) decline from age 18 to midlife (mean, 5.5 IQ points), poorer learning and processing speed, compared with childhood, and self-reported memory and attention problems. Long-term cannabis users also showed hippocampal atrophy at midlife (age 45), which combined with mild midlife cognitive deficits, all known risk factors for dementia.

“Long-term cannabis users – people who have used cannabis from 18 or 19 years old and continued using through midlife – showed cognitive deficits, compared with nonusers. They also showed more severe cognitive deficits, compared with long-term alcohol users and long-term tobacco users. But people who used infrequently or recreationally in midlife did not show as severe cognitive deficits. Cognitive deficits were confined to cannabis users,” lead investigator Madeline Meier, PhD, associate professor of psychology, Arizona State University, Tempe, said in an interview.

“Long-term cannabis users had smaller hippocampal volume, but we also found that smaller hippocampal volume did not explain the cognitive deficits among the long-term cannabis users,” she added.

The study was recently published online in the American Journal of Psychiatry.
 

Growing use in Boomers

Long-term cannabis use has been associated with memory problems. Studies examining the impact of cannabis use on the brain have shown conflicting results. Some suggest regular use in adolescence is associated with altered connectivity and reduced volume of brain regions involved in executive functions such as memory, learning, and impulse control compared with those who do not use cannabis.

Others found no significant structural differences between the brains of cannabis users and nonusers.

An earlier, large longitudinal study in New Zealand found that persistent cannabis use (with frequent use starting in adolescence) was associated with a loss of an average of six (or up to eight) IQ points measured in mid-adulthood.

Cannabis use is increasing among Baby Boomers – a group born between 1946 and 1964 – who used cannabis at historically high rates as young adults, and who now use it at historically high rates in midlife and as older adults.

To date, case-control studies, which are predominantly in adolescents and young adults, have found that cannabis users show subtle cognitive deficits and structural brain differences, but it is unclear whether these differences in young cannabis users might be larger in midlife and in older adults who have longer histories of use.

The study included a representative cohort of 1,037 individuals in Dunedin, New Zealand, born between April 1972 and March 1973, and followed from age 3 to 45.

Cannabis use and dependence were assessed at ages 18, 21, 26, 32, 38, and 45. IQ was assessed at ages 7, 9, 11, and 45. Specific neuropsychological functions and hippocampal volume were assessed at age 45. 

“Most of the previous research has focused on adolescent and young-adult cannabis users. What we’re looking at here is long-term cannabis users in midlife, and we’re finding that long-term users show cognitive deficits. But we’re not just looking at a snapshot of people in midlife, we’re also doing a longitudinal comparison – comparing them to themselves in childhood. We saw that long-term cannabis users showed a decline in IQ from childhood to adulthood,” said Dr. Meier. 

Participants in the study are members of the Dunedin Longitudinal Study, a representative birth cohort (n = 1,037; 91% of eligible births; 52% male) born between April 1972 and March 1973 in Dunedin, New Zealand, who participated in the first assessment at age 3.

This cohort matched socioeconomic status (SES), key health indicators, and demographics. Assessments were carried out at birth and ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32, 38, and 45. IQ was assessed at ages 7, 9, 11, and 45. Specific neuropsychological functions and hippocampal volume were assessed at age 45. 
 

Shrinking hippocampal volume

Cannabis use, cognitive function, and hippocampal volume were assessed comparing long-term cannabis users (n = 84) against five distinct groups:

• Lifelong cannabis nonusers (n = 196) – to replicate the control group most often reported in the case-control literature
• Midlife recreational cannabis users (n = 65) – to determine if cognitive deficits and structural brain differences are apparent in nonproblem users – the majority of cannabis users
• Long-term tobacco users (n = 75)
• Long-term alcohol users (n = 57) – benchmark comparisons for any cannabis findings and to disentangle potential cannabis effects from tobacco and alcohol effects
• Cannabis quitters (n = 58) – to determine whether differences are apparent after cessation

Tests were conducted on dose-response associations using continuously measured persistence of cannabis use, rigorously adjusting for numerous confounders derived from multiple longitudinal waves and data sources.

The investigators also tested whether associations between continuously measured persistence of cannabis use and cognitive deficits were mediated by hippocampal volume differences.

The hippocampus was the area of focus because it has a high density of cannabinoid receptors and is also instrumental for learning and memory, which is one of the most consistently impaired cognitive domains in cannabis users, and has been the brain region that most consistently emerges as smaller in cannabis users relative to controls. Structural MRI was done at age 45 for 875 participants (93% of age 45 participants).

Of 997 cohort members still alive at age 45, 938 (94.1%) were assessed at age 45. Age 45 participants did not differ significantly from other participants on childhood SES, childhood self-control, or childhood IQ. Cognitive functioning among midlife recreational cannabis users was similar to representative cohort norms, suggesting that infrequent recreational cannabis use in midlife is unlikely to compromise cognitive functioning.

However, long-term cannabis users did not perform significantly worse on any test than cannabis quitters. Cannabis quitters showed subtle cognitive deficits that may explain inconsistent findings on the benefits of cessation.

Smaller hippocampal volume is thought to be a possible mediator of cannabis-related cognitive deficits because the hippocampus is rich in CB1 receptors and is involved in learning and memory.

Long-term cannabis users had smaller bilateral volume in total hippocampus and 5 of 12 structurally and functionally distinct subregions (tail, hippocampal amygdala transition area, CA1, molecular layer, and dentate gyrus), and significantly smaller volumes than midlife recreational cannabis users in the left and right hippocampus, and 3 of 12 subfields (tail, CA1, and molecular layer), compared with non-users, consistent with case-control studies.
 

More potent

“If you’ve been using cannabis very long term and now are in midlife, you might want to consider quitting. Quitting is associated with slightly better cognitive performance in midlife. We also need to watch for risk of dementia. We know that people who show cognitive deficits at midlife are at elevated risk for later life dementia. And the deficits we saw among long-term cannabis users (although fairly mild), they were in the range in terms of effect size of what we see among people in other studies who have gone on to develop dementia in later life,” said Dr. Meier.

The study findings conflict with those of other studies, including one by the same research group, which compared the cognitive functioning of twins who were discordant for cannabis use and found little evidence of cannabis-related cognitive deficits. Because long-term cannabis users also use tobacco, alcohol, and other illicit drugs, disentangling cannabis effects from other substances is challenging.

“Long-term cannabis users tend to be long-term polysubstance users, so it’s hard to isolate,” said Dr. Meier.

Additionally, some group sizes were small, raising concerns about low statistical power.

“Group sizes were small but we didn’t rely only on those group comparisons; however, we did find statistical differences. We also tested highly statistically powered dose-response associations between persistence of cannabis use over ages 18-45 and each of our outcomes (IQ, learning, and processing speed in midlife) while adjusting possible alternate explanations such as low childhood IQ, other substance use, [and] socioeconomic backgrounds.

“These dose-response associations used large sample sizes, were highly powered, and took into account a number of alternative explanations. These two different approaches showed very similar findings and one bolstered the other,” said Dr. Meier.

The study’s results were based on individuals who began using cannabis in the 1980s or ‘90s, but the concentration of tetrahydrocannabinol (THC) has risen in recent years.

“When the study began, THC concentration was approximately 4%. Over the last decade we have seen it go up to 12% or even higher. A recent study surveying U.S. dispensaries found 20% THC. If THC accounts for impairment, then the effects can be larger [with higher concentrations]. One of the challenges in the U.S. is that there are laws prohibiting researchers from testing cannabis, so we have to rely on product labels, which we know are unreliable,” said Dr. Meier.

A separate report is forthcoming with results of exploratory analyses of associations between long-term cannabis use and comprehensive MRI measures of global and regional gray and white matter.

The data will also be used to answer a number of different questions about cognitive deficits, brain structure, aging preparedness, social preparedness (strength of social networks), financial and health preparedness, and biological aging (the pace of aging relative to chronological age) in long-term cannabis users, Dr. Meier noted.
 

‘Fantastic’ research

Commenting on the research for this news organization , Andrew J. Saxon, MD, professor, department of psychiatry & behavioral sciences at University of Washington, Seattle, and a member of the American Psychiatric Association’s Council on Addiction Psychiatry, said the study “provides more evidence that heavy and regular cannabis use is not benign behavior.”

“It’s a fantastic piece of research in which they enrolled participants at birth and have followed them up to age 45. In most of the other research that has been done, we have no idea what their baseline was. What’s so remarkable here is that they can clearly demonstrate the loss of IQ points from childhood to age 45,” said Dr. Saxon.

“It is clear that, in people using cannabis long term, cognition is impaired. It would be good to have a better handle on how much cognitive function can be regained if you quit, because that could be a motivator for quitting in people where cannabis is having an adverse effect on their lives,” he added.

On the issue of THC potency, Dr. Saxon said that, while it’s true the potency of cannabis is increasing in terms of THC concentrations, the question is: “Do people who use cannabis use a set amount or do they imbibe until they achieve the state of altered consciousness that they’re seeking? Although there has been some research in the area of self-regulation and cannabis potency, we do not yet have the answers to determine if there is any causation,” said Dr. Saxon.

Dr. Meier and Dr. Saxon reported no relevant financial conflicts of interest.

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

Long-term cannabis use is linked to hippocampal atrophy and poorer cognitive function in midlife – known risk factors for dementia.

A large prospective, longitudinal study showed long-term cannabis users had an intelligence quotient (IQ) decline from age 18 to midlife (mean, 5.5 IQ points), poorer learning and processing speed, compared with childhood, and self-reported memory and attention problems. Long-term cannabis users also showed hippocampal atrophy at midlife (age 45), which combined with mild midlife cognitive deficits, all known risk factors for dementia.

“Long-term cannabis users – people who have used cannabis from 18 or 19 years old and continued using through midlife – showed cognitive deficits, compared with nonusers. They also showed more severe cognitive deficits, compared with long-term alcohol users and long-term tobacco users. But people who used infrequently or recreationally in midlife did not show as severe cognitive deficits. Cognitive deficits were confined to cannabis users,” lead investigator Madeline Meier, PhD, associate professor of psychology, Arizona State University, Tempe, said in an interview.

“Long-term cannabis users had smaller hippocampal volume, but we also found that smaller hippocampal volume did not explain the cognitive deficits among the long-term cannabis users,” she added.

The study was recently published online in the American Journal of Psychiatry.
 

Growing use in Boomers

Long-term cannabis use has been associated with memory problems. Studies examining the impact of cannabis use on the brain have shown conflicting results. Some suggest regular use in adolescence is associated with altered connectivity and reduced volume of brain regions involved in executive functions such as memory, learning, and impulse control compared with those who do not use cannabis.

Others found no significant structural differences between the brains of cannabis users and nonusers.

An earlier, large longitudinal study in New Zealand found that persistent cannabis use (with frequent use starting in adolescence) was associated with a loss of an average of six (or up to eight) IQ points measured in mid-adulthood.

Cannabis use is increasing among Baby Boomers – a group born between 1946 and 1964 – who used cannabis at historically high rates as young adults, and who now use it at historically high rates in midlife and as older adults.

To date, case-control studies, which are predominantly in adolescents and young adults, have found that cannabis users show subtle cognitive deficits and structural brain differences, but it is unclear whether these differences in young cannabis users might be larger in midlife and in older adults who have longer histories of use.

The study included a representative cohort of 1,037 individuals in Dunedin, New Zealand, born between April 1972 and March 1973, and followed from age 3 to 45.

Cannabis use and dependence were assessed at ages 18, 21, 26, 32, 38, and 45. IQ was assessed at ages 7, 9, 11, and 45. Specific neuropsychological functions and hippocampal volume were assessed at age 45. 

“Most of the previous research has focused on adolescent and young-adult cannabis users. What we’re looking at here is long-term cannabis users in midlife, and we’re finding that long-term users show cognitive deficits. But we’re not just looking at a snapshot of people in midlife, we’re also doing a longitudinal comparison – comparing them to themselves in childhood. We saw that long-term cannabis users showed a decline in IQ from childhood to adulthood,” said Dr. Meier. 

Participants in the study are members of the Dunedin Longitudinal Study, a representative birth cohort (n = 1,037; 91% of eligible births; 52% male) born between April 1972 and March 1973 in Dunedin, New Zealand, who participated in the first assessment at age 3.

This cohort matched socioeconomic status (SES), key health indicators, and demographics. Assessments were carried out at birth and ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32, 38, and 45. IQ was assessed at ages 7, 9, 11, and 45. Specific neuropsychological functions and hippocampal volume were assessed at age 45. 
 

Shrinking hippocampal volume

Cannabis use, cognitive function, and hippocampal volume were assessed comparing long-term cannabis users (n = 84) against five distinct groups:

• Lifelong cannabis nonusers (n = 196) – to replicate the control group most often reported in the case-control literature
• Midlife recreational cannabis users (n = 65) – to determine if cognitive deficits and structural brain differences are apparent in nonproblem users – the majority of cannabis users
• Long-term tobacco users (n = 75)
• Long-term alcohol users (n = 57) – benchmark comparisons for any cannabis findings and to disentangle potential cannabis effects from tobacco and alcohol effects
• Cannabis quitters (n = 58) – to determine whether differences are apparent after cessation

Tests were conducted on dose-response associations using continuously measured persistence of cannabis use, rigorously adjusting for numerous confounders derived from multiple longitudinal waves and data sources.

The investigators also tested whether associations between continuously measured persistence of cannabis use and cognitive deficits were mediated by hippocampal volume differences.

The hippocampus was the area of focus because it has a high density of cannabinoid receptors and is also instrumental for learning and memory, which is one of the most consistently impaired cognitive domains in cannabis users, and has been the brain region that most consistently emerges as smaller in cannabis users relative to controls. Structural MRI was done at age 45 for 875 participants (93% of age 45 participants).

Of 997 cohort members still alive at age 45, 938 (94.1%) were assessed at age 45. Age 45 participants did not differ significantly from other participants on childhood SES, childhood self-control, or childhood IQ. Cognitive functioning among midlife recreational cannabis users was similar to representative cohort norms, suggesting that infrequent recreational cannabis use in midlife is unlikely to compromise cognitive functioning.

However, long-term cannabis users did not perform significantly worse on any test than cannabis quitters. Cannabis quitters showed subtle cognitive deficits that may explain inconsistent findings on the benefits of cessation.

Smaller hippocampal volume is thought to be a possible mediator of cannabis-related cognitive deficits because the hippocampus is rich in CB1 receptors and is involved in learning and memory.

Long-term cannabis users had smaller bilateral volume in total hippocampus and 5 of 12 structurally and functionally distinct subregions (tail, hippocampal amygdala transition area, CA1, molecular layer, and dentate gyrus), and significantly smaller volumes than midlife recreational cannabis users in the left and right hippocampus, and 3 of 12 subfields (tail, CA1, and molecular layer), compared with non-users, consistent with case-control studies.
 

More potent

“If you’ve been using cannabis very long term and now are in midlife, you might want to consider quitting. Quitting is associated with slightly better cognitive performance in midlife. We also need to watch for risk of dementia. We know that people who show cognitive deficits at midlife are at elevated risk for later life dementia. And the deficits we saw among long-term cannabis users (although fairly mild), they were in the range in terms of effect size of what we see among people in other studies who have gone on to develop dementia in later life,” said Dr. Meier.

The study findings conflict with those of other studies, including one by the same research group, which compared the cognitive functioning of twins who were discordant for cannabis use and found little evidence of cannabis-related cognitive deficits. Because long-term cannabis users also use tobacco, alcohol, and other illicit drugs, disentangling cannabis effects from other substances is challenging.

“Long-term cannabis users tend to be long-term polysubstance users, so it’s hard to isolate,” said Dr. Meier.

Additionally, some group sizes were small, raising concerns about low statistical power.

“Group sizes were small but we didn’t rely only on those group comparisons; however, we did find statistical differences. We also tested highly statistically powered dose-response associations between persistence of cannabis use over ages 18-45 and each of our outcomes (IQ, learning, and processing speed in midlife) while adjusting possible alternate explanations such as low childhood IQ, other substance use, [and] socioeconomic backgrounds.

“These dose-response associations used large sample sizes, were highly powered, and took into account a number of alternative explanations. These two different approaches showed very similar findings and one bolstered the other,” said Dr. Meier.

The study’s results were based on individuals who began using cannabis in the 1980s or ‘90s, but the concentration of tetrahydrocannabinol (THC) has risen in recent years.

“When the study began, THC concentration was approximately 4%. Over the last decade we have seen it go up to 12% or even higher. A recent study surveying U.S. dispensaries found 20% THC. If THC accounts for impairment, then the effects can be larger [with higher concentrations]. One of the challenges in the U.S. is that there are laws prohibiting researchers from testing cannabis, so we have to rely on product labels, which we know are unreliable,” said Dr. Meier.

A separate report is forthcoming with results of exploratory analyses of associations between long-term cannabis use and comprehensive MRI measures of global and regional gray and white matter.

The data will also be used to answer a number of different questions about cognitive deficits, brain structure, aging preparedness, social preparedness (strength of social networks), financial and health preparedness, and biological aging (the pace of aging relative to chronological age) in long-term cannabis users, Dr. Meier noted.
 

‘Fantastic’ research

Commenting on the research for this news organization , Andrew J. Saxon, MD, professor, department of psychiatry & behavioral sciences at University of Washington, Seattle, and a member of the American Psychiatric Association’s Council on Addiction Psychiatry, said the study “provides more evidence that heavy and regular cannabis use is not benign behavior.”

“It’s a fantastic piece of research in which they enrolled participants at birth and have followed them up to age 45. In most of the other research that has been done, we have no idea what their baseline was. What’s so remarkable here is that they can clearly demonstrate the loss of IQ points from childhood to age 45,” said Dr. Saxon.

“It is clear that, in people using cannabis long term, cognition is impaired. It would be good to have a better handle on how much cognitive function can be regained if you quit, because that could be a motivator for quitting in people where cannabis is having an adverse effect on their lives,” he added.

On the issue of THC potency, Dr. Saxon said that, while it’s true the potency of cannabis is increasing in terms of THC concentrations, the question is: “Do people who use cannabis use a set amount or do they imbibe until they achieve the state of altered consciousness that they’re seeking? Although there has been some research in the area of self-regulation and cannabis potency, we do not yet have the answers to determine if there is any causation,” said Dr. Saxon.

Dr. Meier and Dr. Saxon reported no relevant financial conflicts of interest.

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

Multiple sclerosis (MS) is a lifelong disease that affects about a million people in the United States.^1,2 Since 1998 more than 45,000 veterans have been diagnosed with MS and about 20,000 are evaluated in the Veterans Health Administration (VHA) annually.³

Magnetic resonance imaging (MRI) is a cornerstone for the assessment of persons with multiple sclerosis (pwMS).^4-6 MRI assists with disease diagnosis, allowing for timely therapeutic interventions and withthe evaluation of its progression, treatment effect, and safety. ^4,5 MRI-based outcomes also are used as primary endpoints in clinical trials.^4,5

MS has its clinical onset in early adulthood in most individuals and is diagnosed at a mean age of 30 years.⁷ As a result, pwMS may receive care and MRIs in different facilities during their lifetime. Mitigating interscan variabilities that can challenge intra- and interperson comparisons is crucial for accurate care. Radiologists may find it difficult to compare scans acquired in different facilities, as dissimilarities in acquisition protocols may mask or uncover focal disease, creating false negative or false positive findings. Moreover, lack of a standardized method to report MRI changes may compromise neurologists’ ability to correctly interpret scans and disease progression.

Accordingly, in October 2019, an international task force of neurologists, radiologists, MRI technologists, and imaging scientists with expertise in MS, including representatives from the VHA, worked together to update guidelines for imaging the brain, spinal cord, and optic nerve in pwMS.^8,9 Recognizing the importance of this effort, the VHA Multiple Sclerosis Centers of Excellence (MSCoE), in collaboration with a team of subject matter expert neuroradiologists promptly committed to this effort, advocating the updated consensus recommendations, and favoring their dissemination within the VHA.¹⁰

As part of this commitment and dissemination effort, in this report we summarize the core points of the newly proposed MRI guidelines and ways to adapt them for use within the VHA. We then discuss key elements for their successful implementation and dissemination, specifically regarding the clinical operations of VHA.

Updated Guidelines

The 2021 MAGNIMS-CMSC-NAIMS Consensus Recommendations on the Use of MRI in Patients With Multiple Sclerosis covered a broad spectrum of recommendations related to MRI indication, acquisition, and interpretation in MS. The recommendations span 3 major areas: (1) indications for an MRI with/without contrast; (2) summary of the MRI protocol for radiologists and technologists; and (3) interpretation of MRI examinations.

MRI Scan at Different Timepoints of MS

There are 3 crucial milestones within a the lifespan of a pwMS that require an MRI to reach appropriate conclusions and avoid clinical errors. These include the initial diagnosis, the follow-up to monitor disease and/or treatment effect, and the assessment of medication safety.

In the interest of efficiency, MRI protocols may vary slightly depending on these clinical indications. The Table lists core sequences of the updated 2021 consensus recommendations at each timepoint along with the proposed alternatives or preferences from the VHA workgroup.

MRI Field, Scan Resolution, and Coverage

Both 1.5-Tesla (1.5-T) and 3-T scans are believed to be equally effective in imaging pwMS, providing that the 1.5-T scans are good quality. Although imaging at < 1.5 T is not recommended due to suboptimal disease detection, the use of scanners > 3 T is equally discouraged outside the supervision of trained investigators. Signal-to-noise ratio and resolution are key factors impacting scan quality, and their optimization is prioritized over the number of sequences in the updated 2021 consensus recommendations. For brain imaging, a resolution of 1 mm³ isotropic is preferred for 3-dimensional (3D) imaging and slice thickness ≤ 3 mm without gap (≤ 5 mm with 10-30% gaps for diffusion-weighted imaging only) is recommended for 2D sequences. Images should cover the entire brain and as much of the cervical spine as possible; images should be prescribed axial for 2D or reformatted axial oblique for 3D using the subcallosal plane as reference. For spine imaging, sites should aim at an in-plane resolution of 1 mm²; using sagittal slices ≤ 3 mm thick and axial slices ≤ 5 mm thick, both with no gap. Scans should cover the entire cervical and thoracolumbar region inclusive of the conus. For the optic nerve images, slices should be ≤ 2 or 3 mm thick with an in-plane resolution of 1 mm². Images should be aligned to the orientation of the optic nerve and chiasms, both of which should be entirely covered.

Postgadolinium Images Use

The discovery of the higher sensitivity of post-gadolinium (Gd) T1-weighted (T1-w) MRI relative to high iodine (88.1 g I) computed tomography scans in demonstrating contrast-enhancing MS lesions has revolutionized the way clinicians diagnose and monitor this disease.¹¹ However, in recent years the role of postcontrast MRI has been debated, considering the potential safety concerns secondary to Gd tissue deposition. For this reason, an intentionally more judicious use of postcontrast MRI is proposed by the consensus recommendations. At disease diagnosis, the use of Gd is advisable to (1) show disease dissemination in time; (2) differentiate the diagnosis based on the Gd pattern; (3) predict short-term disease activity; and (4) characterize activity in the setting of progression. When monitoring pwMS, the use of Gd may be useful in the first year of follow-up, particularly if in the setting of low potency medications or for patients for whom the detection of one or more active lesions would lead to a change in disease-modifying agents. Gd also should be used to first, confirm a clinical exacerbation (if needed); second, further characterize a lesion suggestive of progressive multifocal encephalopathy or monitor this disease over time; and third, monitor lesion burden change in patients with large confluent lesions, the count of which otherwise may be difficult.

MRI During Pregnancy and Lactation

The consensus recommendations state that Gd contrast–enhanced MRI is not absolutely contraindicated during pregnancy, although its use should be limited to strictly necessary situations, particularly those involving differential diagnosis, such as cerebral venous thrombosis or monitoring of possibly enlarging lesion burden. The use of Gd is not contraindicated during lactation, as only a small proportion (< 0.4%) passes into the breast milk, leading to an exposure to < 1% of the permitted Gd dose for neonates.^12,13

Harmonizing MRI Reports

The consensus recommendations propose reporting the exact lesion count on T2-weighted (T2-w) images when lesions are < 20, or specifying if the number of T2 lesions is between 20 and 50, between 50 and 100, or uncountable, eg, confluent large lesions. Similarly, for the spinal cord, the consensus recommendations propose reporting the exact lesion count on T2-w images when lesions are < 10, or otherwise report that > 10 lesions are seen.

The VHA workgroup proposed reporting a mild, moderate, or severe T2-lesion burden for a T2-lesion count < 20, between 20 and 50, and > 50, respectively. For follow-up MRIs, notation should be made if there is any change in lesion number, indicating the number of new lesions whenever possible. At each timepoint, the presence of active lesions on postcontrast images should be accurately defined.

Dissemination and Implementation

To implement and disseminate these proposed recommendations within the VHA, a workgroup of neurologists and radiologists was formed in late 2020. A review and discussion of the importance of each of the proposed MRI protocols for veterans with MS was held along with possible modifications to balance the intent of meeting standards of care with resources of individual US Department of Veterans Affairs (VA) medical centers and veterans’ needs. The final protocol recommendations were agreed on by group consensus.

In general, this VHA workgroup felt that the current adopted MRI protocols in several VA medical centers (based on previously proposed recommendations) were similar to the ones newly proposed and that implementing changes to meet the 2021 criteria would not be a major challenge.^14,15 Possible regional and nonregional barriers were discussed. The result of these discussions led to a modified version of what could be considered more stringent guidelines to accommodate medical centers that had fewer imaging resources. This modified protocol offers a viable alternative that allows for minimizing heterogeneities while recognizing the capabilities of the available scanner fleet and meeting the needs of specific centers or veterans. Finally, the workgroup recognized a fundamental obstacle toward this harmonization process in the heterogeneity in vendors and scanner field strength, factors that have previously limited implementation.

The guidelines and proposed changes were then presented to the VA National Radiology Program Office, examined, and discussed for consensus. No changes were felt to be needed, and the recommendation to implement these guidelines in MS regional programs, whenever possible, was deemed appropriate.

At this time, a focused communication plan has been implemented to diffuse the use of this protocol at MS regional programs in the MSCoE network. We will work iteratively with individual sites to practically apply the guidelines, learn about challenges, and work through them to optimize local implementation.

Conclusions

Standardized MRI protocols are fundamental for the care of veterans with MS. Mitigating interscan variabilities should be recognized as a priority by scientific and clinical expert committees. Several guidelines have been developed over the years to standardize MRI acquisition protocols and interpretations, while updating the same to the latest discoveries.^4,5,8,14,15 The VHA has been historically committed to these international efforts, with the goal to excel in the care of veterans with MS by providing access to state-of-the-art technologies. To this end, the initial Consortium of MS Centers MRI protocol was implemented in several MSCoE VA Regional Program sites a decade ago.¹⁴ Efforts continue to update protocol recommendations as needed and to promote their dissemination across the VHA enterprise.

This commentary is part of the continuous effort of the MSCoE to align with contemporary guidelines, apply the highest scientific standards, and achieve consistent outcomes for veterans with MS. For more important details of the clinical scenarios when additional/optional sequences or scans can be acquired, we advise the reader to refer to the 2021 MAGNIMS-CMSC-NAIMS Consensus Recommendations on the Use of MRI in Patients With Multiple Sclerosis.⁸

Multiple sclerosis (MS) is a lifelong disease that affects about a million people in the United States.^1,2 Since 1998 more than 45,000 veterans have been diagnosed with MS and about 20,000 are evaluated in the Veterans Health Administration (VHA) annually.³

Magnetic resonance imaging (MRI) is a cornerstone for the assessment of persons with multiple sclerosis (pwMS).^4-6 MRI assists with disease diagnosis, allowing for timely therapeutic interventions and withthe evaluation of its progression, treatment effect, and safety. ^4,5 MRI-based outcomes also are used as primary endpoints in clinical trials.^4,5

MS has its clinical onset in early adulthood in most individuals and is diagnosed at a mean age of 30 years.⁷ As a result, pwMS may receive care and MRIs in different facilities during their lifetime. Mitigating interscan variabilities that can challenge intra- and interperson comparisons is crucial for accurate care. Radiologists may find it difficult to compare scans acquired in different facilities, as dissimilarities in acquisition protocols may mask or uncover focal disease, creating false negative or false positive findings. Moreover, lack of a standardized method to report MRI changes may compromise neurologists’ ability to correctly interpret scans and disease progression.

Accordingly, in October 2019, an international task force of neurologists, radiologists, MRI technologists, and imaging scientists with expertise in MS, including representatives from the VHA, worked together to update guidelines for imaging the brain, spinal cord, and optic nerve in pwMS.^8,9 Recognizing the importance of this effort, the VHA Multiple Sclerosis Centers of Excellence (MSCoE), in collaboration with a team of subject matter expert neuroradiologists promptly committed to this effort, advocating the updated consensus recommendations, and favoring their dissemination within the VHA.¹⁰

As part of this commitment and dissemination effort, in this report we summarize the core points of the newly proposed MRI guidelines and ways to adapt them for use within the VHA. We then discuss key elements for their successful implementation and dissemination, specifically regarding the clinical operations of VHA.

Updated Guidelines

The 2021 MAGNIMS-CMSC-NAIMS Consensus Recommendations on the Use of MRI in Patients With Multiple Sclerosis covered a broad spectrum of recommendations related to MRI indication, acquisition, and interpretation in MS. The recommendations span 3 major areas: (1) indications for an MRI with/without contrast; (2) summary of the MRI protocol for radiologists and technologists; and (3) interpretation of MRI examinations.

MRI Scan at Different Timepoints of MS

There are 3 crucial milestones within a the lifespan of a pwMS that require an MRI to reach appropriate conclusions and avoid clinical errors. These include the initial diagnosis, the follow-up to monitor disease and/or treatment effect, and the assessment of medication safety.

In the interest of efficiency, MRI protocols may vary slightly depending on these clinical indications. The Table lists core sequences of the updated 2021 consensus recommendations at each timepoint along with the proposed alternatives or preferences from the VHA workgroup.

MRI Field, Scan Resolution, and Coverage

Both 1.5-Tesla (1.5-T) and 3-T scans are believed to be equally effective in imaging pwMS, providing that the 1.5-T scans are good quality. Although imaging at < 1.5 T is not recommended due to suboptimal disease detection, the use of scanners > 3 T is equally discouraged outside the supervision of trained investigators. Signal-to-noise ratio and resolution are key factors impacting scan quality, and their optimization is prioritized over the number of sequences in the updated 2021 consensus recommendations. For brain imaging, a resolution of 1 mm³ isotropic is preferred for 3-dimensional (3D) imaging and slice thickness ≤ 3 mm without gap (≤ 5 mm with 10-30% gaps for diffusion-weighted imaging only) is recommended for 2D sequences. Images should cover the entire brain and as much of the cervical spine as possible; images should be prescribed axial for 2D or reformatted axial oblique for 3D using the subcallosal plane as reference. For spine imaging, sites should aim at an in-plane resolution of 1 mm²; using sagittal slices ≤ 3 mm thick and axial slices ≤ 5 mm thick, both with no gap. Scans should cover the entire cervical and thoracolumbar region inclusive of the conus. For the optic nerve images, slices should be ≤ 2 or 3 mm thick with an in-plane resolution of 1 mm². Images should be aligned to the orientation of the optic nerve and chiasms, both of which should be entirely covered.

Postgadolinium Images Use

The discovery of the higher sensitivity of post-gadolinium (Gd) T1-weighted (T1-w) MRI relative to high iodine (88.1 g I) computed tomography scans in demonstrating contrast-enhancing MS lesions has revolutionized the way clinicians diagnose and monitor this disease.¹¹ However, in recent years the role of postcontrast MRI has been debated, considering the potential safety concerns secondary to Gd tissue deposition. For this reason, an intentionally more judicious use of postcontrast MRI is proposed by the consensus recommendations. At disease diagnosis, the use of Gd is advisable to (1) show disease dissemination in time; (2) differentiate the diagnosis based on the Gd pattern; (3) predict short-term disease activity; and (4) characterize activity in the setting of progression. When monitoring pwMS, the use of Gd may be useful in the first year of follow-up, particularly if in the setting of low potency medications or for patients for whom the detection of one or more active lesions would lead to a change in disease-modifying agents. Gd also should be used to first, confirm a clinical exacerbation (if needed); second, further characterize a lesion suggestive of progressive multifocal encephalopathy or monitor this disease over time; and third, monitor lesion burden change in patients with large confluent lesions, the count of which otherwise may be difficult.

MRI During Pregnancy and Lactation

The consensus recommendations state that Gd contrast–enhanced MRI is not absolutely contraindicated during pregnancy, although its use should be limited to strictly necessary situations, particularly those involving differential diagnosis, such as cerebral venous thrombosis or monitoring of possibly enlarging lesion burden. The use of Gd is not contraindicated during lactation, as only a small proportion (< 0.4%) passes into the breast milk, leading to an exposure to < 1% of the permitted Gd dose for neonates.^12,13

Harmonizing MRI Reports

The consensus recommendations propose reporting the exact lesion count on T2-weighted (T2-w) images when lesions are < 20, or specifying if the number of T2 lesions is between 20 and 50, between 50 and 100, or uncountable, eg, confluent large lesions. Similarly, for the spinal cord, the consensus recommendations propose reporting the exact lesion count on T2-w images when lesions are < 10, or otherwise report that > 10 lesions are seen.

The VHA workgroup proposed reporting a mild, moderate, or severe T2-lesion burden for a T2-lesion count < 20, between 20 and 50, and > 50, respectively. For follow-up MRIs, notation should be made if there is any change in lesion number, indicating the number of new lesions whenever possible. At each timepoint, the presence of active lesions on postcontrast images should be accurately defined.

Dissemination and Implementation

To implement and disseminate these proposed recommendations within the VHA, a workgroup of neurologists and radiologists was formed in late 2020. A review and discussion of the importance of each of the proposed MRI protocols for veterans with MS was held along with possible modifications to balance the intent of meeting standards of care with resources of individual US Department of Veterans Affairs (VA) medical centers and veterans’ needs. The final protocol recommendations were agreed on by group consensus.

In general, this VHA workgroup felt that the current adopted MRI protocols in several VA medical centers (based on previously proposed recommendations) were similar to the ones newly proposed and that implementing changes to meet the 2021 criteria would not be a major challenge.^14,15 Possible regional and nonregional barriers were discussed. The result of these discussions led to a modified version of what could be considered more stringent guidelines to accommodate medical centers that had fewer imaging resources. This modified protocol offers a viable alternative that allows for minimizing heterogeneities while recognizing the capabilities of the available scanner fleet and meeting the needs of specific centers or veterans. Finally, the workgroup recognized a fundamental obstacle toward this harmonization process in the heterogeneity in vendors and scanner field strength, factors that have previously limited implementation.

The guidelines and proposed changes were then presented to the VA National Radiology Program Office, examined, and discussed for consensus. No changes were felt to be needed, and the recommendation to implement these guidelines in MS regional programs, whenever possible, was deemed appropriate.

At this time, a focused communication plan has been implemented to diffuse the use of this protocol at MS regional programs in the MSCoE network. We will work iteratively with individual sites to practically apply the guidelines, learn about challenges, and work through them to optimize local implementation.

Conclusions

Standardized MRI protocols are fundamental for the care of veterans with MS. Mitigating interscan variabilities should be recognized as a priority by scientific and clinical expert committees. Several guidelines have been developed over the years to standardize MRI acquisition protocols and interpretations, while updating the same to the latest discoveries.^4,5,8,14,15 The VHA has been historically committed to these international efforts, with the goal to excel in the care of veterans with MS by providing access to state-of-the-art technologies. To this end, the initial Consortium of MS Centers MRI protocol was implemented in several MSCoE VA Regional Program sites a decade ago.¹⁴ Efforts continue to update protocol recommendations as needed and to promote their dissemination across the VHA enterprise.

This commentary is part of the continuous effort of the MSCoE to align with contemporary guidelines, apply the highest scientific standards, and achieve consistent outcomes for veterans with MS. For more important details of the clinical scenarios when additional/optional sequences or scans can be acquired, we advise the reader to refer to the 2021 MAGNIMS-CMSC-NAIMS Consensus Recommendations on the Use of MRI in Patients With Multiple Sclerosis.⁸

Multiple sclerosis (MS) is a lifelong disease that affects about a million people in the United States.^1,2 Since 1998 more than 45,000 veterans have been diagnosed with MS and about 20,000 are evaluated in the Veterans Health Administration (VHA) annually.³

Magnetic resonance imaging (MRI) is a cornerstone for the assessment of persons with multiple sclerosis (pwMS).^4-6 MRI assists with disease diagnosis, allowing for timely therapeutic interventions and withthe evaluation of its progression, treatment effect, and safety. ^4,5 MRI-based outcomes also are used as primary endpoints in clinical trials.^4,5

MS has its clinical onset in early adulthood in most individuals and is diagnosed at a mean age of 30 years.⁷ As a result, pwMS may receive care and MRIs in different facilities during their lifetime. Mitigating interscan variabilities that can challenge intra- and interperson comparisons is crucial for accurate care. Radiologists may find it difficult to compare scans acquired in different facilities, as dissimilarities in acquisition protocols may mask or uncover focal disease, creating false negative or false positive findings. Moreover, lack of a standardized method to report MRI changes may compromise neurologists’ ability to correctly interpret scans and disease progression.

Accordingly, in October 2019, an international task force of neurologists, radiologists, MRI technologists, and imaging scientists with expertise in MS, including representatives from the VHA, worked together to update guidelines for imaging the brain, spinal cord, and optic nerve in pwMS.^8,9 Recognizing the importance of this effort, the VHA Multiple Sclerosis Centers of Excellence (MSCoE), in collaboration with a team of subject matter expert neuroradiologists promptly committed to this effort, advocating the updated consensus recommendations, and favoring their dissemination within the VHA.¹⁰

As part of this commitment and dissemination effort, in this report we summarize the core points of the newly proposed MRI guidelines and ways to adapt them for use within the VHA. We then discuss key elements for their successful implementation and dissemination, specifically regarding the clinical operations of VHA.

Updated Guidelines

The 2021 MAGNIMS-CMSC-NAIMS Consensus Recommendations on the Use of MRI in Patients With Multiple Sclerosis covered a broad spectrum of recommendations related to MRI indication, acquisition, and interpretation in MS. The recommendations span 3 major areas: (1) indications for an MRI with/without contrast; (2) summary of the MRI protocol for radiologists and technologists; and (3) interpretation of MRI examinations.

MRI Scan at Different Timepoints of MS

There are 3 crucial milestones within a the lifespan of a pwMS that require an MRI to reach appropriate conclusions and avoid clinical errors. These include the initial diagnosis, the follow-up to monitor disease and/or treatment effect, and the assessment of medication safety.

In the interest of efficiency, MRI protocols may vary slightly depending on these clinical indications. The Table lists core sequences of the updated 2021 consensus recommendations at each timepoint along with the proposed alternatives or preferences from the VHA workgroup.

MRI Field, Scan Resolution, and Coverage

Both 1.5-Tesla (1.5-T) and 3-T scans are believed to be equally effective in imaging pwMS, providing that the 1.5-T scans are good quality. Although imaging at < 1.5 T is not recommended due to suboptimal disease detection, the use of scanners > 3 T is equally discouraged outside the supervision of trained investigators. Signal-to-noise ratio and resolution are key factors impacting scan quality, and their optimization is prioritized over the number of sequences in the updated 2021 consensus recommendations. For brain imaging, a resolution of 1 mm³ isotropic is preferred for 3-dimensional (3D) imaging and slice thickness ≤ 3 mm without gap (≤ 5 mm with 10-30% gaps for diffusion-weighted imaging only) is recommended for 2D sequences. Images should cover the entire brain and as much of the cervical spine as possible; images should be prescribed axial for 2D or reformatted axial oblique for 3D using the subcallosal plane as reference. For spine imaging, sites should aim at an in-plane resolution of 1 mm²; using sagittal slices ≤ 3 mm thick and axial slices ≤ 5 mm thick, both with no gap. Scans should cover the entire cervical and thoracolumbar region inclusive of the conus. For the optic nerve images, slices should be ≤ 2 or 3 mm thick with an in-plane resolution of 1 mm². Images should be aligned to the orientation of the optic nerve and chiasms, both of which should be entirely covered.

Postgadolinium Images Use

The discovery of the higher sensitivity of post-gadolinium (Gd) T1-weighted (T1-w) MRI relative to high iodine (88.1 g I) computed tomography scans in demonstrating contrast-enhancing MS lesions has revolutionized the way clinicians diagnose and monitor this disease.¹¹ However, in recent years the role of postcontrast MRI has been debated, considering the potential safety concerns secondary to Gd tissue deposition. For this reason, an intentionally more judicious use of postcontrast MRI is proposed by the consensus recommendations. At disease diagnosis, the use of Gd is advisable to (1) show disease dissemination in time; (2) differentiate the diagnosis based on the Gd pattern; (3) predict short-term disease activity; and (4) characterize activity in the setting of progression. When monitoring pwMS, the use of Gd may be useful in the first year of follow-up, particularly if in the setting of low potency medications or for patients for whom the detection of one or more active lesions would lead to a change in disease-modifying agents. Gd also should be used to first, confirm a clinical exacerbation (if needed); second, further characterize a lesion suggestive of progressive multifocal encephalopathy or monitor this disease over time; and third, monitor lesion burden change in patients with large confluent lesions, the count of which otherwise may be difficult.

MRI During Pregnancy and Lactation

The consensus recommendations state that Gd contrast–enhanced MRI is not absolutely contraindicated during pregnancy, although its use should be limited to strictly necessary situations, particularly those involving differential diagnosis, such as cerebral venous thrombosis or monitoring of possibly enlarging lesion burden. The use of Gd is not contraindicated during lactation, as only a small proportion (< 0.4%) passes into the breast milk, leading to an exposure to < 1% of the permitted Gd dose for neonates.^12,13

Harmonizing MRI Reports

The consensus recommendations propose reporting the exact lesion count on T2-weighted (T2-w) images when lesions are < 20, or specifying if the number of T2 lesions is between 20 and 50, between 50 and 100, or uncountable, eg, confluent large lesions. Similarly, for the spinal cord, the consensus recommendations propose reporting the exact lesion count on T2-w images when lesions are < 10, or otherwise report that > 10 lesions are seen.

The VHA workgroup proposed reporting a mild, moderate, or severe T2-lesion burden for a T2-lesion count < 20, between 20 and 50, and > 50, respectively. For follow-up MRIs, notation should be made if there is any change in lesion number, indicating the number of new lesions whenever possible. At each timepoint, the presence of active lesions on postcontrast images should be accurately defined.

Dissemination and Implementation

To implement and disseminate these proposed recommendations within the VHA, a workgroup of neurologists and radiologists was formed in late 2020. A review and discussion of the importance of each of the proposed MRI protocols for veterans with MS was held along with possible modifications to balance the intent of meeting standards of care with resources of individual US Department of Veterans Affairs (VA) medical centers and veterans’ needs. The final protocol recommendations were agreed on by group consensus.

In general, this VHA workgroup felt that the current adopted MRI protocols in several VA medical centers (based on previously proposed recommendations) were similar to the ones newly proposed and that implementing changes to meet the 2021 criteria would not be a major challenge.^14,15 Possible regional and nonregional barriers were discussed. The result of these discussions led to a modified version of what could be considered more stringent guidelines to accommodate medical centers that had fewer imaging resources. This modified protocol offers a viable alternative that allows for minimizing heterogeneities while recognizing the capabilities of the available scanner fleet and meeting the needs of specific centers or veterans. Finally, the workgroup recognized a fundamental obstacle toward this harmonization process in the heterogeneity in vendors and scanner field strength, factors that have previously limited implementation.

The guidelines and proposed changes were then presented to the VA National Radiology Program Office, examined, and discussed for consensus. No changes were felt to be needed, and the recommendation to implement these guidelines in MS regional programs, whenever possible, was deemed appropriate.

At this time, a focused communication plan has been implemented to diffuse the use of this protocol at MS regional programs in the MSCoE network. We will work iteratively with individual sites to practically apply the guidelines, learn about challenges, and work through them to optimize local implementation.

Conclusions

Standardized MRI protocols are fundamental for the care of veterans with MS. Mitigating interscan variabilities should be recognized as a priority by scientific and clinical expert committees. Several guidelines have been developed over the years to standardize MRI acquisition protocols and interpretations, while updating the same to the latest discoveries.^4,5,8,14,15 The VHA has been historically committed to these international efforts, with the goal to excel in the care of veterans with MS by providing access to state-of-the-art technologies. To this end, the initial Consortium of MS Centers MRI protocol was implemented in several MSCoE VA Regional Program sites a decade ago.¹⁴ Efforts continue to update protocol recommendations as needed and to promote their dissemination across the VHA enterprise.

This commentary is part of the continuous effort of the MSCoE to align with contemporary guidelines, apply the highest scientific standards, and achieve consistent outcomes for veterans with MS. For more important details of the clinical scenarios when additional/optional sequences or scans can be acquired, we advise the reader to refer to the 2021 MAGNIMS-CMSC-NAIMS Consensus Recommendations on the Use of MRI in Patients With Multiple Sclerosis.⁸

Epilepsy affects about 1% of the world population and is one of the most burdensome in terms of disability-adjusted life-years.^1,2 Veterans are at increased risk of developing epilepsy when compared with the general population due to a variety of factors, including a higher frequency of traumatic brain injuries.³ A recent study from the US Centers for Disease Control and Prevention found that veterans who developed epilepsy during their service not only had a higher rate of mental and physical comorbidities, but also were 2.6 times more likely to die compared with veterans without epilepsy.⁴

Oral antiseizure medications (ASM) remain the mainstay of outpatient epilepsy treatment. Patterns of ASM use are complex within the US Department of Veterans Affairs (VA) patient population, particularly within patients at the Epilepsy Centers of Excellence (ECoE). For example, many patients are transitioned from older ASMs with greater adverse effects (AEs) to better tolerated newer generation ASMs or polytherapy regimens with complex pharmacokinetic profiles and drug interactions.⁵ Multiple factors are considered when choosing an ASM, including age, sex, epilepsy/seizure type, comorbidities, past medication trials, AEs, and drug interactions. The complex pharmacologic profile of both older and newer ASMs can confound the optimal management of epilepsy, and suboptimal management can lead to neurologic, psychological, physical, and social consequences, including sudden unexplained death in epilepsy.^6,7 Psychiatric and behavioral problems are seen in up to 30% of patients with newly diagnosed epilepsy and 50% in those with pharmacoresistant epilepsy.⁸ Early screening, detection, and treatment for psychiatric comorbidities are an integral part of evidence-based care in epilepsy.

Being familiar with ASM AEs and comorbid conditions such as anxiety and depression can allow for quick identification and intervention to improve safety and quality of life. A 2007 population-based study found that measures of suicidality had a strong association with epilepsy, and performing mental health screenings, such as the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Screener (GAD-7), and the Brief Irritability Test (BITe), can assist in identifying those patients at risk.⁹

During the COVID-19 pandemic, it has become increasingly clear that the health care sector is facing increasing pressure. The combination of patient acuity as well as critical health care professional (HCP) shortages may be of particular concern in certain specialty clinics where access to practitioners may already be limited. While this is a multifaceted problem, a pragmatic approach would be to increase the use of clinicians, such as clinical pharmacist practitioners (CPPs).

The William S. Middleton Memorial Veterans Hospital (WSMVH) in Madison, Wisconsin, is 1 of 17 VA ECoE sites. The VA ECoE provides high-quality, comprehensive epilepsy evaluation and care to veterans. In fiscal year (FY) 2020, the 17 sites provided care to 5544 veterans.¹⁰ The WSMVH epilepsy clinic sees about 400 veterans each year, receiving referrals from other VA medical centers, and prescribes ASMs, neuromodulation devices, and resective surgeries for epilepsy. The multidisciplinary team consists of an epileptologist, neurophysiology fellow, psychiatrist, nurse practitioner, CPP, and neurology residents. The WSMVH epilepsy clinic has employed CPPs at their highest level of clinical practice authority since 1991.

The WSMVH epilepsy clinic is open 4 hours once weekly. The clinic offers fourteen 30-minute appointment slots either in person or via telehealth. The epileptologist reviews patient charts prior to clinic and assigns each patient to the appropriate HCP. When making the determination to assign a patient to a CPP or pharmacy resident, the epileptologist considers current treatment response, mental health issues as well as medication-related concerns (eg, potential pharmacokinetic/pharmacodynamic interactions, AEs, adherence). The CPP can independently lead routine follow-up appointments and address acute as well as ongoing ASM therapy needs. Pharmacy residents are fully integrated into the clinic workflow, seeing assigned patients independently when appropriate but ensuring that each patient has access to either the epileptologist, CPP, or psychiatrist prior to finalizing the treatment plan. The epilepsy clinic rotation is required for first-year pharmacy residents and is an elective rotation in the second year.

While this level of service has been in place at WSMVH for more than 3 decades, a systematic evaluation on workload and clinical impact has not been conducted.¹¹ The purpose of this analysis is to evaluate and quantify the breadth and impact of CPPs in this specialty setting. The WSMVH/University of Wisconsin-Madison institutional review board deemed this quality improvement study exempt from review.

Methods

This study was a single-center, retrospective, quality improvement project evaluating the impact of a CPP and clinical pharmacy resident have within the WSMVH epilepsy clinic on access to epilepsy care and medication management. The secondary outcomes were the types of interventions made by the CPP and mental health screening performed.

Between October 2019 and May 2021, 591 appointments were scheduled at the epilepsy clinic for medical, psychiatry, neurosurgery, and pharmacy residents; the epileptologist; CPP; psychiatrist; epilepsy fellow; or nurse practitioner. A retrospective chart review of the 446 patients seen by either a CPP or clinical pharmacy resident from October 2017 to June 2021 assessed pharmacist-led interventions made during each appointment. The following treatment interventions were assessed: medication initiations/discontinuations, dose changes, and nonpharmacologic interventions, including education. Additionally, any mental health screenings completed, consultations to other specialties placed, or laboratory tests ordered were documented.

Results

In the epilepsy clinic, 591 appointments were completed from October 1, 2019, to May 31, 2021. Of those appointments, 255 (43.2%) were led by pharmacists; 156 (26.4%) by pharmacy residents and 99 (16.8%) by CPPs (16.8%) (Table 1). Appointments held by other HCPs included 139 (23.5%) by nurse practitioner, 108 (18.3%) by the attending epileptologist, 41 (6.9%) by fellows, 22 (3.7%) by psychiatrists, 19 (3.2) by medical residents, 4 (0.7%) by neurosurgery residents, and 3 (0.5%) by psychiatry residents. Medication interventions included 55 (11.8%) dose increases, 52 (11.1%) medication initiations, and 32 (6.9%) dose decreases (Table 2). Mental health screening was conducted for 229 (49.1%) patients with PHQ-9, 225 (48.3%) with GAD-7, and 111 (23.8) with BITe. Some veterans received multiple screeners at a clinic visit, and others received none (most commonly during telephone follow-up appointments). The mean time spent with each patient was 27 minutes.

Discussion

Within the private sector, access to a neurologist or epileptologist is limited, and the US Health Resources and Services Administration National Center for Workforce Analysis projected that the demand for these specialists would exceed supply by 2025.¹² In 2017, Kobau and colleagues found that only 1 in 10 adults with epilepsy saw a neurologist within the year, similar to previous years. As demand for specialty care exceeds capacity, additional members of the health care team are needed to ensure timely, effective, and safe care for patients with epilepsy.

One way to increase health care access is to use an interdisciplinary model of care, integrating pharmacists in the management of epilepsy in collaboration with other HCPs, a strategy that has been endorsed by the American Epilepsy Society (AES).¹³ As experts in pharmacotherapy, pharmacists can uniquely provide medication management for this complex disease as ASMs continue to remain the first-line treatment.¹⁴

In addition to increased demand for specialty services, there also is an increase in health care spending with a push to limit additional spending. In 2016, despite similar health care use in other high-income countries, health care costs are approximately twice as much in the US, mostly driven by prices of pharmaceuticals and administrative costs.¹⁵ Bond and colleagues evaluated 9380 Medicare patients with epilepsy or seizure disorders throughout US hospitals in 1998.¹⁶ They found that hospitals without pharmacist-managed ASM therapy had Medicare charges that were 11.2% higher than hospitals with pharmacist-managed therapy. Many factors contribute to the rise in cost, including an increase in laboratory charges for serum drug assays, legal litigations related to drug AEs, and an increase in hospital length of stay (about 14 additional days). Similar to pharmacist-managed anticoagulation, vancomycin, and aminoglycoside therapy, direct involvement of pharmacists with ASM management decreases health care costs.¹⁴

The American Academy of Neurology (AAN) developed 8 epilepsy quality measures: seizure type and frequency, etiology or epilepsy syndrome, review of electroencephalogram and imaging findings, counseling of ASM AEs, consideration of surgical treatment of intractable epilepsy, epilepsy-specific safety issues, and counseling for women of childbearing potential on contraception and pregnancy. These measures serve as a guide for evidence-based therapy and standardization of epilepsy care.¹⁷ Additionally, bone health, depression, and awareness of sudden unexplained death in epilepsy are increasing in importance when providing quality epilepsy care. Wasade and colleagues surveyed Michigan neurologists and found that only 37% of the respondents addressed ASM AEs at every clinic visit. They also found that just 26% of responding neurologists inquire about depression at every clinic visit, and 17% inquire only once a year. In our practice, screening for depression, suicidality, and counseling on ASM AEs are routinely provided by CPPs during each clinic visit.

Within the VA, CPPs are granted a scope of practice that allows them to perform comprehensive medication management, including but not limited to, prescribing medication regimens, ordering laboratory tests and diagnostic studies, and performing physical assessments. In our practice, the most common interventions made by CPPs were patient-focused counseling, bone health screening, mental health triage and referral, and ASM regimen adjustments. Assessment of ASM adherence also was noted to be an active area of CPP-patient engagement. These most common interventions align well with the AAN quality measures. It is now well recognized that nonadherence in patients with epilepsy not only can lead to loss of seizure control, but injury and death as well.^18,19 Malek and colleagues found that patients with epilepsy who are nonadherent to their ASM regimens have a 3-times greater risk of mortality compared with those who were adherent.²⁰ Adherence to the appropriate medication regimen in epilepsy can result in seizure-freedom in 70% of patients; therefore, exploring nonadherence in this population is crucial.²¹

The COVID-19 pandemic precipitated changes to the health care industry, including the heavy reliance on telehealth. Following the Wisconsin stay-at-home order on March 25, 2020, all nonessential face-to-face appointments at the WSMVH halted. The epilepsy clinic transitioned the majority of appointments to either telephone or VA Video Connect (VVC), which is a program on the veteran’s computer, tablet, or mobile device upon which the appointment is held. Although it became more challenging to obtain a mental health screening during virtual appointments and the frequency did decrease, patients were asked for a subjective report of their mood during each telephone or video appointment. The AES has since put forth a statement of support for the continuation of telehealth following the COVID-19 pandemic due to the flexibility that telehealth provides people with epilepsy. Additionally, the AES taskforce provided suggestions for continued pharmacist engagement within the epilepsy care team, including the triaging of patients, management of ASMs, and involvement in the delivery of telehealth.

WSMVH clinic CPPs and clinical pharmacy residents saw a high proportion of all veterans, which allowed the epileptologist time to focus on new consults and higher acuity cases. At WSMVH, screening for depression with the PHQ-9 must be completed at least annually for any patient regardless of their involvement in mental health care. This typically occurs at a patient’s annual primary care visit. Patients who receive epilepsy clinic care will often receive more frequent and thorough depression screening with the PHQ-9, in addition to screening for anxiety, irritability, and sleep disorders. CPPs ability to identify, evaluate, and triage psychiatric concerns ensures that the patient has support and care.

Limitations

There is limited research available on the impact that a CPP has on medication management and access to care within an epilepsy clinic, especially those with a scope of practice. One limitation of this retrospective chart review is that the appropriateness of each medication intervention was not assessed; therefore, the impact of each intervention was not captured. Additionally, this single-site study of veterans may not reflect the general population. However, we believe that this model could be adapted to nonspecialty neurology practices. Of note the scope of this study did not include a comparison of medication interventions for the other specialties within the clinic.

Conclusions

The integration of a CPP and pharmacy residents into the WSMVH epilepsy clinic has allowed for greater and more timely access to care, managing 43.2% of all patients within the clinic during the study. Pharmacy scope of practice allows for collaborative autonomy with ASM adjustments and for the epileptologist time to focus on higher acuity cases. In settings where pharmacists do not have prescriptive status, medication management services, such as comprehensive medication reviews, identifying drug-drug and drug-disease interactions, recognizing adherence barriers, and medication safety surveillance, can still be performed to improve management of epilepsy.

Acknowledgments

Ellina S. Seckel, PharmD, BCACP, DPLA; Anita Kashyap, PharmD, BCACP; Brooke Keenan, NP; Leigh Heffner, PharmD

Epilepsy affects about 1% of the world population and is one of the most burdensome in terms of disability-adjusted life-years.^1,2 Veterans are at increased risk of developing epilepsy when compared with the general population due to a variety of factors, including a higher frequency of traumatic brain injuries.³ A recent study from the US Centers for Disease Control and Prevention found that veterans who developed epilepsy during their service not only had a higher rate of mental and physical comorbidities, but also were 2.6 times more likely to die compared with veterans without epilepsy.⁴

Oral antiseizure medications (ASM) remain the mainstay of outpatient epilepsy treatment. Patterns of ASM use are complex within the US Department of Veterans Affairs (VA) patient population, particularly within patients at the Epilepsy Centers of Excellence (ECoE). For example, many patients are transitioned from older ASMs with greater adverse effects (AEs) to better tolerated newer generation ASMs or polytherapy regimens with complex pharmacokinetic profiles and drug interactions.⁵ Multiple factors are considered when choosing an ASM, including age, sex, epilepsy/seizure type, comorbidities, past medication trials, AEs, and drug interactions. The complex pharmacologic profile of both older and newer ASMs can confound the optimal management of epilepsy, and suboptimal management can lead to neurologic, psychological, physical, and social consequences, including sudden unexplained death in epilepsy.^6,7 Psychiatric and behavioral problems are seen in up to 30% of patients with newly diagnosed epilepsy and 50% in those with pharmacoresistant epilepsy.⁸ Early screening, detection, and treatment for psychiatric comorbidities are an integral part of evidence-based care in epilepsy.

Being familiar with ASM AEs and comorbid conditions such as anxiety and depression can allow for quick identification and intervention to improve safety and quality of life. A 2007 population-based study found that measures of suicidality had a strong association with epilepsy, and performing mental health screenings, such as the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Screener (GAD-7), and the Brief Irritability Test (BITe), can assist in identifying those patients at risk.⁹

During the COVID-19 pandemic, it has become increasingly clear that the health care sector is facing increasing pressure. The combination of patient acuity as well as critical health care professional (HCP) shortages may be of particular concern in certain specialty clinics where access to practitioners may already be limited. While this is a multifaceted problem, a pragmatic approach would be to increase the use of clinicians, such as clinical pharmacist practitioners (CPPs).

The William S. Middleton Memorial Veterans Hospital (WSMVH) in Madison, Wisconsin, is 1 of 17 VA ECoE sites. The VA ECoE provides high-quality, comprehensive epilepsy evaluation and care to veterans. In fiscal year (FY) 2020, the 17 sites provided care to 5544 veterans.¹⁰ The WSMVH epilepsy clinic sees about 400 veterans each year, receiving referrals from other VA medical centers, and prescribes ASMs, neuromodulation devices, and resective surgeries for epilepsy. The multidisciplinary team consists of an epileptologist, neurophysiology fellow, psychiatrist, nurse practitioner, CPP, and neurology residents. The WSMVH epilepsy clinic has employed CPPs at their highest level of clinical practice authority since 1991.

The WSMVH epilepsy clinic is open 4 hours once weekly. The clinic offers fourteen 30-minute appointment slots either in person or via telehealth. The epileptologist reviews patient charts prior to clinic and assigns each patient to the appropriate HCP. When making the determination to assign a patient to a CPP or pharmacy resident, the epileptologist considers current treatment response, mental health issues as well as medication-related concerns (eg, potential pharmacokinetic/pharmacodynamic interactions, AEs, adherence). The CPP can independently lead routine follow-up appointments and address acute as well as ongoing ASM therapy needs. Pharmacy residents are fully integrated into the clinic workflow, seeing assigned patients independently when appropriate but ensuring that each patient has access to either the epileptologist, CPP, or psychiatrist prior to finalizing the treatment plan. The epilepsy clinic rotation is required for first-year pharmacy residents and is an elective rotation in the second year.

While this level of service has been in place at WSMVH for more than 3 decades, a systematic evaluation on workload and clinical impact has not been conducted.¹¹ The purpose of this analysis is to evaluate and quantify the breadth and impact of CPPs in this specialty setting. The WSMVH/University of Wisconsin-Madison institutional review board deemed this quality improvement study exempt from review.

Methods

This study was a single-center, retrospective, quality improvement project evaluating the impact of a CPP and clinical pharmacy resident have within the WSMVH epilepsy clinic on access to epilepsy care and medication management. The secondary outcomes were the types of interventions made by the CPP and mental health screening performed.

Between October 2019 and May 2021, 591 appointments were scheduled at the epilepsy clinic for medical, psychiatry, neurosurgery, and pharmacy residents; the epileptologist; CPP; psychiatrist; epilepsy fellow; or nurse practitioner. A retrospective chart review of the 446 patients seen by either a CPP or clinical pharmacy resident from October 2017 to June 2021 assessed pharmacist-led interventions made during each appointment. The following treatment interventions were assessed: medication initiations/discontinuations, dose changes, and nonpharmacologic interventions, including education. Additionally, any mental health screenings completed, consultations to other specialties placed, or laboratory tests ordered were documented.

Results

In the epilepsy clinic, 591 appointments were completed from October 1, 2019, to May 31, 2021. Of those appointments, 255 (43.2%) were led by pharmacists; 156 (26.4%) by pharmacy residents and 99 (16.8%) by CPPs (16.8%) (Table 1). Appointments held by other HCPs included 139 (23.5%) by nurse practitioner, 108 (18.3%) by the attending epileptologist, 41 (6.9%) by fellows, 22 (3.7%) by psychiatrists, 19 (3.2) by medical residents, 4 (0.7%) by neurosurgery residents, and 3 (0.5%) by psychiatry residents. Medication interventions included 55 (11.8%) dose increases, 52 (11.1%) medication initiations, and 32 (6.9%) dose decreases (Table 2). Mental health screening was conducted for 229 (49.1%) patients with PHQ-9, 225 (48.3%) with GAD-7, and 111 (23.8) with BITe. Some veterans received multiple screeners at a clinic visit, and others received none (most commonly during telephone follow-up appointments). The mean time spent with each patient was 27 minutes.

Discussion

Within the private sector, access to a neurologist or epileptologist is limited, and the US Health Resources and Services Administration National Center for Workforce Analysis projected that the demand for these specialists would exceed supply by 2025.¹² In 2017, Kobau and colleagues found that only 1 in 10 adults with epilepsy saw a neurologist within the year, similar to previous years. As demand for specialty care exceeds capacity, additional members of the health care team are needed to ensure timely, effective, and safe care for patients with epilepsy.

One way to increase health care access is to use an interdisciplinary model of care, integrating pharmacists in the management of epilepsy in collaboration with other HCPs, a strategy that has been endorsed by the American Epilepsy Society (AES).¹³ As experts in pharmacotherapy, pharmacists can uniquely provide medication management for this complex disease as ASMs continue to remain the first-line treatment.¹⁴

In addition to increased demand for specialty services, there also is an increase in health care spending with a push to limit additional spending. In 2016, despite similar health care use in other high-income countries, health care costs are approximately twice as much in the US, mostly driven by prices of pharmaceuticals and administrative costs.¹⁵ Bond and colleagues evaluated 9380 Medicare patients with epilepsy or seizure disorders throughout US hospitals in 1998.¹⁶ They found that hospitals without pharmacist-managed ASM therapy had Medicare charges that were 11.2% higher than hospitals with pharmacist-managed therapy. Many factors contribute to the rise in cost, including an increase in laboratory charges for serum drug assays, legal litigations related to drug AEs, and an increase in hospital length of stay (about 14 additional days). Similar to pharmacist-managed anticoagulation, vancomycin, and aminoglycoside therapy, direct involvement of pharmacists with ASM management decreases health care costs.¹⁴

The American Academy of Neurology (AAN) developed 8 epilepsy quality measures: seizure type and frequency, etiology or epilepsy syndrome, review of electroencephalogram and imaging findings, counseling of ASM AEs, consideration of surgical treatment of intractable epilepsy, epilepsy-specific safety issues, and counseling for women of childbearing potential on contraception and pregnancy. These measures serve as a guide for evidence-based therapy and standardization of epilepsy care.¹⁷ Additionally, bone health, depression, and awareness of sudden unexplained death in epilepsy are increasing in importance when providing quality epilepsy care. Wasade and colleagues surveyed Michigan neurologists and found that only 37% of the respondents addressed ASM AEs at every clinic visit. They also found that just 26% of responding neurologists inquire about depression at every clinic visit, and 17% inquire only once a year. In our practice, screening for depression, suicidality, and counseling on ASM AEs are routinely provided by CPPs during each clinic visit.

Within the VA, CPPs are granted a scope of practice that allows them to perform comprehensive medication management, including but not limited to, prescribing medication regimens, ordering laboratory tests and diagnostic studies, and performing physical assessments. In our practice, the most common interventions made by CPPs were patient-focused counseling, bone health screening, mental health triage and referral, and ASM regimen adjustments. Assessment of ASM adherence also was noted to be an active area of CPP-patient engagement. These most common interventions align well with the AAN quality measures. It is now well recognized that nonadherence in patients with epilepsy not only can lead to loss of seizure control, but injury and death as well.^18,19 Malek and colleagues found that patients with epilepsy who are nonadherent to their ASM regimens have a 3-times greater risk of mortality compared with those who were adherent.²⁰ Adherence to the appropriate medication regimen in epilepsy can result in seizure-freedom in 70% of patients; therefore, exploring nonadherence in this population is crucial.²¹

The COVID-19 pandemic precipitated changes to the health care industry, including the heavy reliance on telehealth. Following the Wisconsin stay-at-home order on March 25, 2020, all nonessential face-to-face appointments at the WSMVH halted. The epilepsy clinic transitioned the majority of appointments to either telephone or VA Video Connect (VVC), which is a program on the veteran’s computer, tablet, or mobile device upon which the appointment is held. Although it became more challenging to obtain a mental health screening during virtual appointments and the frequency did decrease, patients were asked for a subjective report of their mood during each telephone or video appointment. The AES has since put forth a statement of support for the continuation of telehealth following the COVID-19 pandemic due to the flexibility that telehealth provides people with epilepsy. Additionally, the AES taskforce provided suggestions for continued pharmacist engagement within the epilepsy care team, including the triaging of patients, management of ASMs, and involvement in the delivery of telehealth.

WSMVH clinic CPPs and clinical pharmacy residents saw a high proportion of all veterans, which allowed the epileptologist time to focus on new consults and higher acuity cases. At WSMVH, screening for depression with the PHQ-9 must be completed at least annually for any patient regardless of their involvement in mental health care. This typically occurs at a patient’s annual primary care visit. Patients who receive epilepsy clinic care will often receive more frequent and thorough depression screening with the PHQ-9, in addition to screening for anxiety, irritability, and sleep disorders. CPPs ability to identify, evaluate, and triage psychiatric concerns ensures that the patient has support and care.

Limitations

There is limited research available on the impact that a CPP has on medication management and access to care within an epilepsy clinic, especially those with a scope of practice. One limitation of this retrospective chart review is that the appropriateness of each medication intervention was not assessed; therefore, the impact of each intervention was not captured. Additionally, this single-site study of veterans may not reflect the general population. However, we believe that this model could be adapted to nonspecialty neurology practices. Of note the scope of this study did not include a comparison of medication interventions for the other specialties within the clinic.

Conclusions

The integration of a CPP and pharmacy residents into the WSMVH epilepsy clinic has allowed for greater and more timely access to care, managing 43.2% of all patients within the clinic during the study. Pharmacy scope of practice allows for collaborative autonomy with ASM adjustments and for the epileptologist time to focus on higher acuity cases. In settings where pharmacists do not have prescriptive status, medication management services, such as comprehensive medication reviews, identifying drug-drug and drug-disease interactions, recognizing adherence barriers, and medication safety surveillance, can still be performed to improve management of epilepsy.

Acknowledgments

Ellina S. Seckel, PharmD, BCACP, DPLA; Anita Kashyap, PharmD, BCACP; Brooke Keenan, NP; Leigh Heffner, PharmD

Epilepsy affects about 1% of the world population and is one of the most burdensome in terms of disability-adjusted life-years.^1,2 Veterans are at increased risk of developing epilepsy when compared with the general population due to a variety of factors, including a higher frequency of traumatic brain injuries.³ A recent study from the US Centers for Disease Control and Prevention found that veterans who developed epilepsy during their service not only had a higher rate of mental and physical comorbidities, but also were 2.6 times more likely to die compared with veterans without epilepsy.⁴

Oral antiseizure medications (ASM) remain the mainstay of outpatient epilepsy treatment. Patterns of ASM use are complex within the US Department of Veterans Affairs (VA) patient population, particularly within patients at the Epilepsy Centers of Excellence (ECoE). For example, many patients are transitioned from older ASMs with greater adverse effects (AEs) to better tolerated newer generation ASMs or polytherapy regimens with complex pharmacokinetic profiles and drug interactions.⁵ Multiple factors are considered when choosing an ASM, including age, sex, epilepsy/seizure type, comorbidities, past medication trials, AEs, and drug interactions. The complex pharmacologic profile of both older and newer ASMs can confound the optimal management of epilepsy, and suboptimal management can lead to neurologic, psychological, physical, and social consequences, including sudden unexplained death in epilepsy.^6,7 Psychiatric and behavioral problems are seen in up to 30% of patients with newly diagnosed epilepsy and 50% in those with pharmacoresistant epilepsy.⁸ Early screening, detection, and treatment for psychiatric comorbidities are an integral part of evidence-based care in epilepsy.

Being familiar with ASM AEs and comorbid conditions such as anxiety and depression can allow for quick identification and intervention to improve safety and quality of life. A 2007 population-based study found that measures of suicidality had a strong association with epilepsy, and performing mental health screenings, such as the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Screener (GAD-7), and the Brief Irritability Test (BITe), can assist in identifying those patients at risk.⁹

During the COVID-19 pandemic, it has become increasingly clear that the health care sector is facing increasing pressure. The combination of patient acuity as well as critical health care professional (HCP) shortages may be of particular concern in certain specialty clinics where access to practitioners may already be limited. While this is a multifaceted problem, a pragmatic approach would be to increase the use of clinicians, such as clinical pharmacist practitioners (CPPs).

The William S. Middleton Memorial Veterans Hospital (WSMVH) in Madison, Wisconsin, is 1 of 17 VA ECoE sites. The VA ECoE provides high-quality, comprehensive epilepsy evaluation and care to veterans. In fiscal year (FY) 2020, the 17 sites provided care to 5544 veterans.¹⁰ The WSMVH epilepsy clinic sees about 400 veterans each year, receiving referrals from other VA medical centers, and prescribes ASMs, neuromodulation devices, and resective surgeries for epilepsy. The multidisciplinary team consists of an epileptologist, neurophysiology fellow, psychiatrist, nurse practitioner, CPP, and neurology residents. The WSMVH epilepsy clinic has employed CPPs at their highest level of clinical practice authority since 1991.

The WSMVH epilepsy clinic is open 4 hours once weekly. The clinic offers fourteen 30-minute appointment slots either in person or via telehealth. The epileptologist reviews patient charts prior to clinic and assigns each patient to the appropriate HCP. When making the determination to assign a patient to a CPP or pharmacy resident, the epileptologist considers current treatment response, mental health issues as well as medication-related concerns (eg, potential pharmacokinetic/pharmacodynamic interactions, AEs, adherence). The CPP can independently lead routine follow-up appointments and address acute as well as ongoing ASM therapy needs. Pharmacy residents are fully integrated into the clinic workflow, seeing assigned patients independently when appropriate but ensuring that each patient has access to either the epileptologist, CPP, or psychiatrist prior to finalizing the treatment plan. The epilepsy clinic rotation is required for first-year pharmacy residents and is an elective rotation in the second year.

While this level of service has been in place at WSMVH for more than 3 decades, a systematic evaluation on workload and clinical impact has not been conducted.¹¹ The purpose of this analysis is to evaluate and quantify the breadth and impact of CPPs in this specialty setting. The WSMVH/University of Wisconsin-Madison institutional review board deemed this quality improvement study exempt from review.

Methods

This study was a single-center, retrospective, quality improvement project evaluating the impact of a CPP and clinical pharmacy resident have within the WSMVH epilepsy clinic on access to epilepsy care and medication management. The secondary outcomes were the types of interventions made by the CPP and mental health screening performed.

Between October 2019 and May 2021, 591 appointments were scheduled at the epilepsy clinic for medical, psychiatry, neurosurgery, and pharmacy residents; the epileptologist; CPP; psychiatrist; epilepsy fellow; or nurse practitioner. A retrospective chart review of the 446 patients seen by either a CPP or clinical pharmacy resident from October 2017 to June 2021 assessed pharmacist-led interventions made during each appointment. The following treatment interventions were assessed: medication initiations/discontinuations, dose changes, and nonpharmacologic interventions, including education. Additionally, any mental health screenings completed, consultations to other specialties placed, or laboratory tests ordered were documented.

Results

In the epilepsy clinic, 591 appointments were completed from October 1, 2019, to May 31, 2021. Of those appointments, 255 (43.2%) were led by pharmacists; 156 (26.4%) by pharmacy residents and 99 (16.8%) by CPPs (16.8%) (Table 1). Appointments held by other HCPs included 139 (23.5%) by nurse practitioner, 108 (18.3%) by the attending epileptologist, 41 (6.9%) by fellows, 22 (3.7%) by psychiatrists, 19 (3.2) by medical residents, 4 (0.7%) by neurosurgery residents, and 3 (0.5%) by psychiatry residents. Medication interventions included 55 (11.8%) dose increases, 52 (11.1%) medication initiations, and 32 (6.9%) dose decreases (Table 2). Mental health screening was conducted for 229 (49.1%) patients with PHQ-9, 225 (48.3%) with GAD-7, and 111 (23.8) with BITe. Some veterans received multiple screeners at a clinic visit, and others received none (most commonly during telephone follow-up appointments). The mean time spent with each patient was 27 minutes.

Discussion

Within the private sector, access to a neurologist or epileptologist is limited, and the US Health Resources and Services Administration National Center for Workforce Analysis projected that the demand for these specialists would exceed supply by 2025.¹² In 2017, Kobau and colleagues found that only 1 in 10 adults with epilepsy saw a neurologist within the year, similar to previous years. As demand for specialty care exceeds capacity, additional members of the health care team are needed to ensure timely, effective, and safe care for patients with epilepsy.

One way to increase health care access is to use an interdisciplinary model of care, integrating pharmacists in the management of epilepsy in collaboration with other HCPs, a strategy that has been endorsed by the American Epilepsy Society (AES).¹³ As experts in pharmacotherapy, pharmacists can uniquely provide medication management for this complex disease as ASMs continue to remain the first-line treatment.¹⁴

In addition to increased demand for specialty services, there also is an increase in health care spending with a push to limit additional spending. In 2016, despite similar health care use in other high-income countries, health care costs are approximately twice as much in the US, mostly driven by prices of pharmaceuticals and administrative costs.¹⁵ Bond and colleagues evaluated 9380 Medicare patients with epilepsy or seizure disorders throughout US hospitals in 1998.¹⁶ They found that hospitals without pharmacist-managed ASM therapy had Medicare charges that were 11.2% higher than hospitals with pharmacist-managed therapy. Many factors contribute to the rise in cost, including an increase in laboratory charges for serum drug assays, legal litigations related to drug AEs, and an increase in hospital length of stay (about 14 additional days). Similar to pharmacist-managed anticoagulation, vancomycin, and aminoglycoside therapy, direct involvement of pharmacists with ASM management decreases health care costs.¹⁴

The American Academy of Neurology (AAN) developed 8 epilepsy quality measures: seizure type and frequency, etiology or epilepsy syndrome, review of electroencephalogram and imaging findings, counseling of ASM AEs, consideration of surgical treatment of intractable epilepsy, epilepsy-specific safety issues, and counseling for women of childbearing potential on contraception and pregnancy. These measures serve as a guide for evidence-based therapy and standardization of epilepsy care.¹⁷ Additionally, bone health, depression, and awareness of sudden unexplained death in epilepsy are increasing in importance when providing quality epilepsy care. Wasade and colleagues surveyed Michigan neurologists and found that only 37% of the respondents addressed ASM AEs at every clinic visit. They also found that just 26% of responding neurologists inquire about depression at every clinic visit, and 17% inquire only once a year. In our practice, screening for depression, suicidality, and counseling on ASM AEs are routinely provided by CPPs during each clinic visit.

Within the VA, CPPs are granted a scope of practice that allows them to perform comprehensive medication management, including but not limited to, prescribing medication regimens, ordering laboratory tests and diagnostic studies, and performing physical assessments. In our practice, the most common interventions made by CPPs were patient-focused counseling, bone health screening, mental health triage and referral, and ASM regimen adjustments. Assessment of ASM adherence also was noted to be an active area of CPP-patient engagement. These most common interventions align well with the AAN quality measures. It is now well recognized that nonadherence in patients with epilepsy not only can lead to loss of seizure control, but injury and death as well.^18,19 Malek and colleagues found that patients with epilepsy who are nonadherent to their ASM regimens have a 3-times greater risk of mortality compared with those who were adherent.²⁰ Adherence to the appropriate medication regimen in epilepsy can result in seizure-freedom in 70% of patients; therefore, exploring nonadherence in this population is crucial.²¹

The COVID-19 pandemic precipitated changes to the health care industry, including the heavy reliance on telehealth. Following the Wisconsin stay-at-home order on March 25, 2020, all nonessential face-to-face appointments at the WSMVH halted. The epilepsy clinic transitioned the majority of appointments to either telephone or VA Video Connect (VVC), which is a program on the veteran’s computer, tablet, or mobile device upon which the appointment is held. Although it became more challenging to obtain a mental health screening during virtual appointments and the frequency did decrease, patients were asked for a subjective report of their mood during each telephone or video appointment. The AES has since put forth a statement of support for the continuation of telehealth following the COVID-19 pandemic due to the flexibility that telehealth provides people with epilepsy. Additionally, the AES taskforce provided suggestions for continued pharmacist engagement within the epilepsy care team, including the triaging of patients, management of ASMs, and involvement in the delivery of telehealth.

WSMVH clinic CPPs and clinical pharmacy residents saw a high proportion of all veterans, which allowed the epileptologist time to focus on new consults and higher acuity cases. At WSMVH, screening for depression with the PHQ-9 must be completed at least annually for any patient regardless of their involvement in mental health care. This typically occurs at a patient’s annual primary care visit. Patients who receive epilepsy clinic care will often receive more frequent and thorough depression screening with the PHQ-9, in addition to screening for anxiety, irritability, and sleep disorders. CPPs ability to identify, evaluate, and triage psychiatric concerns ensures that the patient has support and care.

Limitations

There is limited research available on the impact that a CPP has on medication management and access to care within an epilepsy clinic, especially those with a scope of practice. One limitation of this retrospective chart review is that the appropriateness of each medication intervention was not assessed; therefore, the impact of each intervention was not captured. Additionally, this single-site study of veterans may not reflect the general population. However, we believe that this model could be adapted to nonspecialty neurology practices. Of note the scope of this study did not include a comparison of medication interventions for the other specialties within the clinic.

Conclusions

The integration of a CPP and pharmacy residents into the WSMVH epilepsy clinic has allowed for greater and more timely access to care, managing 43.2% of all patients within the clinic during the study. Pharmacy scope of practice allows for collaborative autonomy with ASM adjustments and for the epileptologist time to focus on higher acuity cases. In settings where pharmacists do not have prescriptive status, medication management services, such as comprehensive medication reviews, identifying drug-drug and drug-disease interactions, recognizing adherence barriers, and medication safety surveillance, can still be performed to improve management of epilepsy.

Acknowledgments

Ellina S. Seckel, PharmD, BCACP, DPLA; Anita Kashyap, PharmD, BCACP; Brooke Keenan, NP; Leigh Heffner, PharmD

Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI refers to computer systems designed to perform tasks that normally require human intelligence.¹ Machine learning (ML), a field in which computers learn from data without being specifically programmed, is the AI subset responsible for its success in matching or even surpassing humans in certain tasks.²

Supervised learning, a subset of ML, uses an algorithm with annotated data from which to learn.³ The program will use the characteristics of a training data set to predict a specific outcome or target when exposed to a sample data set of the same type. Unsupervised learning finds naturally occurring patterns or groupings within the data.⁴ With deep learning (DL) algorithms, computers learn the features that optimally represent the data for the problem at hand.⁵ Both ML and DL are meant to emulate neural networks in the brain, giving rise to artificial neural networks composed of nodes structured within input, hidden, and output layers.

The DL neural network differs from a conventional one by having many hidden layers instead of just 1 layer that extracts patterns within the data.⁶ Convolutional neural networks (CNNs) are the most prevalent DL architecture used in medical imaging. CNN’s hidden layers apply convolution and pooling operations to break down an image into features containing the most valuable information. The connecting layer applies high-level reasoning before the output layer provides predictions for the image. This framework has applications within radiology, such as predicting a lesion category or condition from an image, determining whether a specific pixel belongs to background or a target class, and predicting the location of lesions.¹

AI promises to increase efficiency and reduces errors. With increased data processing and image interpretation, AI technology may help radiologists improve the quality of patient care.⁶ This article discusses the current applications and future integration of AI in neuroradiology.

Neuroimaging Applications

AI can improve the quality of neuroimaging and reduce the clinical and systemic loads of other imaging modalities. AI can predict patient wait times for computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and X-ray imaging.⁷ A ML-based AI has detected the variables that most affected patient wait times, including proximity to federal holidays and severity of the patient’s condition, and calculated how long patients would be delayed after their scheduled appointment time. This AI modality could allow more efficient patient scheduling and reveal areas of patient processing that could be changed, potentially improving patient satisfaction and outcomes for time-sensitive neurologic conditions.

AI can save patient and health care practitioner time for repeat MRIs. An estimated 20% of MRI scans require a repeat series—a massive loss of time and funds for both patients and the health care system.⁸ A DL approach can determine whether an MRI is usable clinically or unclear enough to require repetition.⁹ This initial screening measure can prevent patients from making return visits and neuroradiologists from reading inconclusive images. AI offers the opportunity to reduce time and costs incurred by optimizing the health care process before imaging is obtained.

Speeding Up Neuroimaging

AI can reduce the time spent performing imaging. Because MRIs consume time and resources, compressed sensing (CS) is commonly used. CS preferentially maintains in-plane resolution at the expense of through-plane resolution to produce a scan with a single, usable viewpoint that preserves signal-to-noise ratio (SNR). CS, however, limits interpretation to single directions and can create aliasing artifacts. An AI algorithm known as synthetic multi-orientation resolution enhancement works in real time to reduce aliasing and improve resolution in these compressed scans.¹⁰ This AI improved resolution of white matter lesions in patients with multiple sclerosis (MS) on FLAIR (fluid-attenuated inversion recovery) images, and permitted multiview reconstruction from these limited scans.

Tasks of reconstructing and anti-aliasing come with high computational costs that vary inversely with the extent of scanning compression, potentially negating the time and resource savings of CS. DL AI modalities have been developed to reduce operational loads and further improve image resolution in several directions from CS. One such deep residual learning AI was trained with compressed MRIs and used the framelet method to create a CNN that could rapidly remove global and deeply coherent aliasing artifacts.¹¹ This system, compared with synthetic multi-orientation resolution enhancement, uses a pretrained, pretested AI that does not require additional time during scanning for computational analysis, thereby multiplying the time benefit of CS while retaining the benefits of multidirectional reconstruction and increased resolution. This methodology suffers from inherent degradation of perceptual image quality in its reconstructions because of the L2 loss function the CNN uses to reduce mean squared error, which causes blurring by averaging all possible outcomes of signal distribution during reconstruction. To combat this, researchers have developed another AI to reduce reconstruction times that uses a different loss function in a generative adversarial network to retain image quality, while offering reconstruction times several hundred times faster than current CS-MRI structures.¹² So-called sparse-coding methods promise further reduction in reconstruction times, with the possibility of processing completed online with a lightweight architecture rather than on a local system.¹³

Neuroimaging of acute cases benefits most directly from these technologies because MRIs and their high resolution and SNR begin to approach CT imaging time scales. This could have important implications in clinical care, particularly for stroke imaging and evaluating spinal cord compression. CS-MRI optimization represents one of the greatest areas of neuroimaging cost savings and neurologic care improvement in the modern radiology era.

Reducing Contrast and Radiation Doses

AI has the ability to read CT, MRI, and positron emission tomography (PET) with reduced or without contrast without significant loss in sensitivity for detecting lesions. With MRI, gadolinium-based contrast can cause injection site reactions, allergic reactions, metal deposition throughout the body, and nephrogenic systemic fibrosis in the most severe instances.¹⁴ DL has been applied to brain MRIs performed with 10% of a full dose of contrast without significant degradation of image quality. Neuroradiologists did not rate the AI-synthesized images for several MRI indications lower than their full-dose counterparts.¹⁵ Low-dose contrast imaging, regardless of modality, generates greater noise with a significantly reduced signal. However, with AI applied, researchers found that the software suppressed motion and aliasing artifacts and improved image quality, perhaps evidence that this low-dose modality is less vulnerable to the most common pitfalls of MRI.

Recently, low-dose MRI moved into the spotlight when Subtle Medical SubtleGAD software received a National Institutes of Health grant and an expedited pathway to phase 2 clinical trials.¹⁶ SubtleGAD, a DL AI that enables low-dose MRI interpretation, might allow contrast MRI for patients with advanced kidney disease or contrast allergies. At some point, contrast with MRI might not be necessary because DL AI applied to noncontrast MRs for detecting MS lesions was found to be preliminarily effective with 78% lesion detection sensitivity.¹⁷

PET-MRI combines simultaneous PET and MRI and has been used to evaluate neurologic disorders. PET-MRI can detect amyloid plaques in Alzheimer disease 10 to 20 years before clinical signs of dementia emerge.¹⁸ PET-MRI has sparked DL AI development to decrease the dose of the IV radioactive tracer 18F-florbetaben used in imaging to reduce radiation exposure and imaging costs.This reduction is critical if PET-MRI is to become used widely.^19-21

An initial CNN could reconstruct low-dose amyloid scans to full-dose resolution, albeit with a greater susceptibility to some artifacts and motion blurring.²² Similar to the synthetic multi-orientation resolution enhancement CNN, this program showed signal blurring from the L2 loss function, which was corrected in a later AI that used a generative adversarial network to minimize perceptual loss.²³ This new AI demonstrated greater image resolution, feature preservation, and radiologist rating over the previous AI and was capable of reconstructing low-dose PET scans to full-dose resolution without an accompanying MRI. Applications of this algorithm are far-reaching, potentially allowing neuroimaging of brain tumors at more frequent intervals with higher resolution and lower total radiation exposure.

AI also has been applied to neurologic CT to reduce radiation exposure.²⁴ Because it is critical to abide by the principles of ALARA (as low as reasonably achievable), the ability of AI to reduce radiation exposure holds significant promise. A CNN has been used to transform low-dose CTs of anthropomorphic models with calcium inserts and cardiac patients to normal-dose CTs, with the goal of improving the SNR.²⁵ By training a noise-discriminating CNN and a noise-generating CNN together in a generative adversarial network, the AI improved image feature preservation during transformation. This algorithm has a direct application in imaging cerebral vasculature, including calcification that can explain lacunar infarcts and tracking systemic atherosclerosis.²⁶

Another CNN has been applied to remove more complex noise patterns from the phenomena of beam hardening and photon starvation common in low-dose CT. This algorithm extracts the directional components of artifacts and compares them to known artifact patterns, allowing for highly specific suppression of unwanted signals.²⁷ In June 2019, the US Food and Drug Administration (FDA) approved ClariPi, a deep CNN program for advanced denoising and resolution improvement of low- and ultra low-dose CTs.²⁸ Aside from only low-dose settings, this AI could reduce artifacts in all CT imaging modalities and improve therapeutic value of procedures, including cerebral angiograms and emergency cranial scans. As the average CT radiation dose decreased from 12 mSv in 2009 to 1.5 mSv in 2014 and continues to fall, these algorithms will become increasingly necessary to retain the high resolution and diagnostic power expected of neurologic CTs.^29,30

Downstream Applications

Downstream applications refer to AI use after a radiologic study is acquired, mostly image interpretation. More than 70% of FDA-approved AI medical devices are in radiology, and many of these relate to image analysis.^6,31 Although AI is not limited to black-and-white image interpretation, it is hypothesized that one of the reasons radiology is inviting to AI is because gray-scale images lend themselves to standardization.³ Moreover, most radiology departments already use AI-friendly picture archiving and communication systems.^31,32

AI has been applied to a range of radiologic modalities, including MRI, CT, ultrasonography, PET, and mammography.^32-38 AI also has been specifically applied to radiography, including the interpretation of tuberculosis, pneumonia, lung lesions, and COVID-19.^33,39-45 AI also can assist triage, patient screening, providing a “second opinion” rapidly, shortening the time needed for attaining a diagnosis, monitoring disease progression, and predicting prognosis.^{37-39,43,45-47} Downstream applications of AI in neuroradiology and neurology include using CT to aid in detecting hemorrhage or ischemic stroke; using MRI to automatically segment lesions, such as tumors or MS lesions; assisting in early diagnosis and predicting prognosis in MS; assisting in treating paralysis, including from spinal cord injury; determining seizure type and localizing area of seizure onset; and using cameras, wearable devices, and smartphone applications to diagnose and assess treatment response in neurodegenerative disorders, such as Parkinson or Alzheimer diseases (Figure).^37,48-56

Practical and Ethical Considerations

In any discussion of the benefits of AI, it is prudent to address its shortcomings. Chief among these is overfitting, which occurs when an AI is too closely aligned with its training dataset and prone to error when applied to novel cases. Often this is a byproduct of a small training set.⁶⁰ Neuroradiology, particularly with uncommon, advanced imaging methods, has a smaller number of available studies.⁶¹ Even with more prevalent imaging modalities, such as head CT, the work of collecting training scans from patients with the prerequisite disease processes, particularly if these processes are rare, can limit the number of datapoints collected. Neuroradiologists should understand how an AI tool was generated, including the size and variety of the training dataset used, to best gauge the clinical applicability and fitness of the system.

Another point of concern for AI clinical decision support tools’ implementation is automation bias—the tendency for clinicians to favor machine-generated decisions and ignore contrary data or conflicting human decisions.⁶² This situation often arises when radiologists experience overwhelming patient loads or are in underresourced settings, where there is little ability to review every AI-based diagnosis. Although AI might be of benefit in such conditions by reducing physician workload and streamlining the diagnostic process, there is the propensity to improperly rely on a tool meant to augment, not replace, a radiologist’s judgment. Such cases have led to adverse outcomes for patients, and legal precedence shows that this constitutes negligence.⁶³ Maintaining awareness of each tool’s limitations and proper application is the only remedy for such situations.

Ethically, we must consider the opaqueness of ML-developed neuroimaging AIs. For many systems, the specific process by which an AI arrives at its conclusions is unknown. This AI “black box” can conceal potential errors and biases that are masked by overall positive performance metrics. The lack of understanding about how a tool functions in the zero-failure clinical setting understandably gives radiologists pause. The question must be asked: Is it ethical to use a system that is a relatively unknown quantity? Entities, including state governments, Canada, and the European Union, have produced an answer. Each of these governments have implemented policies requiring that health care AIs use some method to display to end users the process by which they arrive at conclusions.^64-68

The 21st Century Cures Act declares that to attain approval, clinical AIs must demonstrate this explainability to clinicians and patients.⁶⁹ The response has been an explosion in the development of explainable AI. Systems that visualize the areas where AI attention most often rests with heatmaps, generate labels for the most heavily weighted features of radiographic images, and create full diagnostic reports to justify AI conclusions aim to meet the goal of transparency and inspiring confidence in clinical end users.⁷⁰ The ability to understand the “thought process” of a system proves useful for error correction and retooling. A trend toward under- or overdetecting conditions, flagging seemingly irrelevant image regions, or low reproducibility can be better addressed when it is clear how the AI is drawing its false conclusions. With an iterative process of testing and redesigning, false positive and negative rates can be reduced, the need for human intervention can be lowered to an appropriate minimum, and patient outcomes can be improved.⁷¹

Data collection raises another ethical concern. To train functional clinical decision support tools, massive amounts of patient demographic, laboratory, and imaging data are required. With incentives to develop the most powerful AI systems, record collection can venture down a path where patient autonomy and privacy are threatened. Radiologists have a duty to ensure data mining serves patients and improves the practice of radiology while protecting patients’ personal information.⁶² Policies have placed similar limits on the access to and use of patient records.^64-69 Patients have the right to request explanation of the AI systems their data have been used to train. Approval for data acquisition requires the use of explainable AI, standardized data security protocol implementation, and adequate proof of communal benefit from the clinical decision support tool. Establishment of state-mandated protections bodes well for a future when developers can access enormous caches of data while patients and health care professionals are assured that no identifying information has escaped a well-regulated space. On the level of the individual radiologist, the knowledge that each datum represents a human life. These are people who has made themselves vulnerable by seeking relief for what ails them, which should serve as a lasting reminder to operate with utmost care when handling sensitive information.

Conclusions

The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI use for detecting important neurologic conditions holds promise in combatting ever greater imaging volumes and providing timely diagnoses. As medicine witnesses the continuing adoption of AI, it is important that practitioners possess an understanding of its current and emerging uses.

Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI refers to computer systems designed to perform tasks that normally require human intelligence.¹ Machine learning (ML), a field in which computers learn from data without being specifically programmed, is the AI subset responsible for its success in matching or even surpassing humans in certain tasks.²

Supervised learning, a subset of ML, uses an algorithm with annotated data from which to learn.³ The program will use the characteristics of a training data set to predict a specific outcome or target when exposed to a sample data set of the same type. Unsupervised learning finds naturally occurring patterns or groupings within the data.⁴ With deep learning (DL) algorithms, computers learn the features that optimally represent the data for the problem at hand.⁵ Both ML and DL are meant to emulate neural networks in the brain, giving rise to artificial neural networks composed of nodes structured within input, hidden, and output layers.

The DL neural network differs from a conventional one by having many hidden layers instead of just 1 layer that extracts patterns within the data.⁶ Convolutional neural networks (CNNs) are the most prevalent DL architecture used in medical imaging. CNN’s hidden layers apply convolution and pooling operations to break down an image into features containing the most valuable information. The connecting layer applies high-level reasoning before the output layer provides predictions for the image. This framework has applications within radiology, such as predicting a lesion category or condition from an image, determining whether a specific pixel belongs to background or a target class, and predicting the location of lesions.¹

AI promises to increase efficiency and reduces errors. With increased data processing and image interpretation, AI technology may help radiologists improve the quality of patient care.⁶ This article discusses the current applications and future integration of AI in neuroradiology.

Neuroimaging Applications

AI can improve the quality of neuroimaging and reduce the clinical and systemic loads of other imaging modalities. AI can predict patient wait times for computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and X-ray imaging.⁷ A ML-based AI has detected the variables that most affected patient wait times, including proximity to federal holidays and severity of the patient’s condition, and calculated how long patients would be delayed after their scheduled appointment time. This AI modality could allow more efficient patient scheduling and reveal areas of patient processing that could be changed, potentially improving patient satisfaction and outcomes for time-sensitive neurologic conditions.

AI can save patient and health care practitioner time for repeat MRIs. An estimated 20% of MRI scans require a repeat series—a massive loss of time and funds for both patients and the health care system.⁸ A DL approach can determine whether an MRI is usable clinically or unclear enough to require repetition.⁹ This initial screening measure can prevent patients from making return visits and neuroradiologists from reading inconclusive images. AI offers the opportunity to reduce time and costs incurred by optimizing the health care process before imaging is obtained.

Speeding Up Neuroimaging

AI can reduce the time spent performing imaging. Because MRIs consume time and resources, compressed sensing (CS) is commonly used. CS preferentially maintains in-plane resolution at the expense of through-plane resolution to produce a scan with a single, usable viewpoint that preserves signal-to-noise ratio (SNR). CS, however, limits interpretation to single directions and can create aliasing artifacts. An AI algorithm known as synthetic multi-orientation resolution enhancement works in real time to reduce aliasing and improve resolution in these compressed scans.¹⁰ This AI improved resolution of white matter lesions in patients with multiple sclerosis (MS) on FLAIR (fluid-attenuated inversion recovery) images, and permitted multiview reconstruction from these limited scans.

Tasks of reconstructing and anti-aliasing come with high computational costs that vary inversely with the extent of scanning compression, potentially negating the time and resource savings of CS. DL AI modalities have been developed to reduce operational loads and further improve image resolution in several directions from CS. One such deep residual learning AI was trained with compressed MRIs and used the framelet method to create a CNN that could rapidly remove global and deeply coherent aliasing artifacts.¹¹ This system, compared with synthetic multi-orientation resolution enhancement, uses a pretrained, pretested AI that does not require additional time during scanning for computational analysis, thereby multiplying the time benefit of CS while retaining the benefits of multidirectional reconstruction and increased resolution. This methodology suffers from inherent degradation of perceptual image quality in its reconstructions because of the L2 loss function the CNN uses to reduce mean squared error, which causes blurring by averaging all possible outcomes of signal distribution during reconstruction. To combat this, researchers have developed another AI to reduce reconstruction times that uses a different loss function in a generative adversarial network to retain image quality, while offering reconstruction times several hundred times faster than current CS-MRI structures.¹² So-called sparse-coding methods promise further reduction in reconstruction times, with the possibility of processing completed online with a lightweight architecture rather than on a local system.¹³

Neuroimaging of acute cases benefits most directly from these technologies because MRIs and their high resolution and SNR begin to approach CT imaging time scales. This could have important implications in clinical care, particularly for stroke imaging and evaluating spinal cord compression. CS-MRI optimization represents one of the greatest areas of neuroimaging cost savings and neurologic care improvement in the modern radiology era.

Reducing Contrast and Radiation Doses

AI has the ability to read CT, MRI, and positron emission tomography (PET) with reduced or without contrast without significant loss in sensitivity for detecting lesions. With MRI, gadolinium-based contrast can cause injection site reactions, allergic reactions, metal deposition throughout the body, and nephrogenic systemic fibrosis in the most severe instances.¹⁴ DL has been applied to brain MRIs performed with 10% of a full dose of contrast without significant degradation of image quality. Neuroradiologists did not rate the AI-synthesized images for several MRI indications lower than their full-dose counterparts.¹⁵ Low-dose contrast imaging, regardless of modality, generates greater noise with a significantly reduced signal. However, with AI applied, researchers found that the software suppressed motion and aliasing artifacts and improved image quality, perhaps evidence that this low-dose modality is less vulnerable to the most common pitfalls of MRI.

Recently, low-dose MRI moved into the spotlight when Subtle Medical SubtleGAD software received a National Institutes of Health grant and an expedited pathway to phase 2 clinical trials.¹⁶ SubtleGAD, a DL AI that enables low-dose MRI interpretation, might allow contrast MRI for patients with advanced kidney disease or contrast allergies. At some point, contrast with MRI might not be necessary because DL AI applied to noncontrast MRs for detecting MS lesions was found to be preliminarily effective with 78% lesion detection sensitivity.¹⁷

PET-MRI combines simultaneous PET and MRI and has been used to evaluate neurologic disorders. PET-MRI can detect amyloid plaques in Alzheimer disease 10 to 20 years before clinical signs of dementia emerge.¹⁸ PET-MRI has sparked DL AI development to decrease the dose of the IV radioactive tracer 18F-florbetaben used in imaging to reduce radiation exposure and imaging costs.This reduction is critical if PET-MRI is to become used widely.^19-21

An initial CNN could reconstruct low-dose amyloid scans to full-dose resolution, albeit with a greater susceptibility to some artifacts and motion blurring.²² Similar to the synthetic multi-orientation resolution enhancement CNN, this program showed signal blurring from the L2 loss function, which was corrected in a later AI that used a generative adversarial network to minimize perceptual loss.²³ This new AI demonstrated greater image resolution, feature preservation, and radiologist rating over the previous AI and was capable of reconstructing low-dose PET scans to full-dose resolution without an accompanying MRI. Applications of this algorithm are far-reaching, potentially allowing neuroimaging of brain tumors at more frequent intervals with higher resolution and lower total radiation exposure.

AI also has been applied to neurologic CT to reduce radiation exposure.²⁴ Because it is critical to abide by the principles of ALARA (as low as reasonably achievable), the ability of AI to reduce radiation exposure holds significant promise. A CNN has been used to transform low-dose CTs of anthropomorphic models with calcium inserts and cardiac patients to normal-dose CTs, with the goal of improving the SNR.²⁵ By training a noise-discriminating CNN and a noise-generating CNN together in a generative adversarial network, the AI improved image feature preservation during transformation. This algorithm has a direct application in imaging cerebral vasculature, including calcification that can explain lacunar infarcts and tracking systemic atherosclerosis.²⁶

Another CNN has been applied to remove more complex noise patterns from the phenomena of beam hardening and photon starvation common in low-dose CT. This algorithm extracts the directional components of artifacts and compares them to known artifact patterns, allowing for highly specific suppression of unwanted signals.²⁷ In June 2019, the US Food and Drug Administration (FDA) approved ClariPi, a deep CNN program for advanced denoising and resolution improvement of low- and ultra low-dose CTs.²⁸ Aside from only low-dose settings, this AI could reduce artifacts in all CT imaging modalities and improve therapeutic value of procedures, including cerebral angiograms and emergency cranial scans. As the average CT radiation dose decreased from 12 mSv in 2009 to 1.5 mSv in 2014 and continues to fall, these algorithms will become increasingly necessary to retain the high resolution and diagnostic power expected of neurologic CTs.^29,30

Downstream Applications

Downstream applications refer to AI use after a radiologic study is acquired, mostly image interpretation. More than 70% of FDA-approved AI medical devices are in radiology, and many of these relate to image analysis.^6,31 Although AI is not limited to black-and-white image interpretation, it is hypothesized that one of the reasons radiology is inviting to AI is because gray-scale images lend themselves to standardization.³ Moreover, most radiology departments already use AI-friendly picture archiving and communication systems.^31,32

AI has been applied to a range of radiologic modalities, including MRI, CT, ultrasonography, PET, and mammography.^32-38 AI also has been specifically applied to radiography, including the interpretation of tuberculosis, pneumonia, lung lesions, and COVID-19.^33,39-45 AI also can assist triage, patient screening, providing a “second opinion” rapidly, shortening the time needed for attaining a diagnosis, monitoring disease progression, and predicting prognosis.^{37-39,43,45-47} Downstream applications of AI in neuroradiology and neurology include using CT to aid in detecting hemorrhage or ischemic stroke; using MRI to automatically segment lesions, such as tumors or MS lesions; assisting in early diagnosis and predicting prognosis in MS; assisting in treating paralysis, including from spinal cord injury; determining seizure type and localizing area of seizure onset; and using cameras, wearable devices, and smartphone applications to diagnose and assess treatment response in neurodegenerative disorders, such as Parkinson or Alzheimer diseases (Figure).^37,48-56

Practical and Ethical Considerations

In any discussion of the benefits of AI, it is prudent to address its shortcomings. Chief among these is overfitting, which occurs when an AI is too closely aligned with its training dataset and prone to error when applied to novel cases. Often this is a byproduct of a small training set.⁶⁰ Neuroradiology, particularly with uncommon, advanced imaging methods, has a smaller number of available studies.⁶¹ Even with more prevalent imaging modalities, such as head CT, the work of collecting training scans from patients with the prerequisite disease processes, particularly if these processes are rare, can limit the number of datapoints collected. Neuroradiologists should understand how an AI tool was generated, including the size and variety of the training dataset used, to best gauge the clinical applicability and fitness of the system.

Another point of concern for AI clinical decision support tools’ implementation is automation bias—the tendency for clinicians to favor machine-generated decisions and ignore contrary data or conflicting human decisions.⁶² This situation often arises when radiologists experience overwhelming patient loads or are in underresourced settings, where there is little ability to review every AI-based diagnosis. Although AI might be of benefit in such conditions by reducing physician workload and streamlining the diagnostic process, there is the propensity to improperly rely on a tool meant to augment, not replace, a radiologist’s judgment. Such cases have led to adverse outcomes for patients, and legal precedence shows that this constitutes negligence.⁶³ Maintaining awareness of each tool’s limitations and proper application is the only remedy for such situations.

Ethically, we must consider the opaqueness of ML-developed neuroimaging AIs. For many systems, the specific process by which an AI arrives at its conclusions is unknown. This AI “black box” can conceal potential errors and biases that are masked by overall positive performance metrics. The lack of understanding about how a tool functions in the zero-failure clinical setting understandably gives radiologists pause. The question must be asked: Is it ethical to use a system that is a relatively unknown quantity? Entities, including state governments, Canada, and the European Union, have produced an answer. Each of these governments have implemented policies requiring that health care AIs use some method to display to end users the process by which they arrive at conclusions.^64-68

The 21st Century Cures Act declares that to attain approval, clinical AIs must demonstrate this explainability to clinicians and patients.⁶⁹ The response has been an explosion in the development of explainable AI. Systems that visualize the areas where AI attention most often rests with heatmaps, generate labels for the most heavily weighted features of radiographic images, and create full diagnostic reports to justify AI conclusions aim to meet the goal of transparency and inspiring confidence in clinical end users.⁷⁰ The ability to understand the “thought process” of a system proves useful for error correction and retooling. A trend toward under- or overdetecting conditions, flagging seemingly irrelevant image regions, or low reproducibility can be better addressed when it is clear how the AI is drawing its false conclusions. With an iterative process of testing and redesigning, false positive and negative rates can be reduced, the need for human intervention can be lowered to an appropriate minimum, and patient outcomes can be improved.⁷¹

Data collection raises another ethical concern. To train functional clinical decision support tools, massive amounts of patient demographic, laboratory, and imaging data are required. With incentives to develop the most powerful AI systems, record collection can venture down a path where patient autonomy and privacy are threatened. Radiologists have a duty to ensure data mining serves patients and improves the practice of radiology while protecting patients’ personal information.⁶² Policies have placed similar limits on the access to and use of patient records.^64-69 Patients have the right to request explanation of the AI systems their data have been used to train. Approval for data acquisition requires the use of explainable AI, standardized data security protocol implementation, and adequate proof of communal benefit from the clinical decision support tool. Establishment of state-mandated protections bodes well for a future when developers can access enormous caches of data while patients and health care professionals are assured that no identifying information has escaped a well-regulated space. On the level of the individual radiologist, the knowledge that each datum represents a human life. These are people who has made themselves vulnerable by seeking relief for what ails them, which should serve as a lasting reminder to operate with utmost care when handling sensitive information.

Conclusions

The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI use for detecting important neurologic conditions holds promise in combatting ever greater imaging volumes and providing timely diagnoses. As medicine witnesses the continuing adoption of AI, it is important that practitioners possess an understanding of its current and emerging uses.

Artificial intelligence (AI) in medicine has shown significant promise, particularly in neuroimaging. AI refers to computer systems designed to perform tasks that normally require human intelligence.¹ Machine learning (ML), a field in which computers learn from data without being specifically programmed, is the AI subset responsible for its success in matching or even surpassing humans in certain tasks.²

Supervised learning, a subset of ML, uses an algorithm with annotated data from which to learn.³ The program will use the characteristics of a training data set to predict a specific outcome or target when exposed to a sample data set of the same type. Unsupervised learning finds naturally occurring patterns or groupings within the data.⁴ With deep learning (DL) algorithms, computers learn the features that optimally represent the data for the problem at hand.⁵ Both ML and DL are meant to emulate neural networks in the brain, giving rise to artificial neural networks composed of nodes structured within input, hidden, and output layers.

The DL neural network differs from a conventional one by having many hidden layers instead of just 1 layer that extracts patterns within the data.⁶ Convolutional neural networks (CNNs) are the most prevalent DL architecture used in medical imaging. CNN’s hidden layers apply convolution and pooling operations to break down an image into features containing the most valuable information. The connecting layer applies high-level reasoning before the output layer provides predictions for the image. This framework has applications within radiology, such as predicting a lesion category or condition from an image, determining whether a specific pixel belongs to background or a target class, and predicting the location of lesions.¹

AI promises to increase efficiency and reduces errors. With increased data processing and image interpretation, AI technology may help radiologists improve the quality of patient care.⁶ This article discusses the current applications and future integration of AI in neuroradiology.

Neuroimaging Applications

AI can improve the quality of neuroimaging and reduce the clinical and systemic loads of other imaging modalities. AI can predict patient wait times for computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and X-ray imaging.⁷ A ML-based AI has detected the variables that most affected patient wait times, including proximity to federal holidays and severity of the patient’s condition, and calculated how long patients would be delayed after their scheduled appointment time. This AI modality could allow more efficient patient scheduling and reveal areas of patient processing that could be changed, potentially improving patient satisfaction and outcomes for time-sensitive neurologic conditions.

AI can save patient and health care practitioner time for repeat MRIs. An estimated 20% of MRI scans require a repeat series—a massive loss of time and funds for both patients and the health care system.⁸ A DL approach can determine whether an MRI is usable clinically or unclear enough to require repetition.⁹ This initial screening measure can prevent patients from making return visits and neuroradiologists from reading inconclusive images. AI offers the opportunity to reduce time and costs incurred by optimizing the health care process before imaging is obtained.

Speeding Up Neuroimaging

AI can reduce the time spent performing imaging. Because MRIs consume time and resources, compressed sensing (CS) is commonly used. CS preferentially maintains in-plane resolution at the expense of through-plane resolution to produce a scan with a single, usable viewpoint that preserves signal-to-noise ratio (SNR). CS, however, limits interpretation to single directions and can create aliasing artifacts. An AI algorithm known as synthetic multi-orientation resolution enhancement works in real time to reduce aliasing and improve resolution in these compressed scans.¹⁰ This AI improved resolution of white matter lesions in patients with multiple sclerosis (MS) on FLAIR (fluid-attenuated inversion recovery) images, and permitted multiview reconstruction from these limited scans.

Tasks of reconstructing and anti-aliasing come with high computational costs that vary inversely with the extent of scanning compression, potentially negating the time and resource savings of CS. DL AI modalities have been developed to reduce operational loads and further improve image resolution in several directions from CS. One such deep residual learning AI was trained with compressed MRIs and used the framelet method to create a CNN that could rapidly remove global and deeply coherent aliasing artifacts.¹¹ This system, compared with synthetic multi-orientation resolution enhancement, uses a pretrained, pretested AI that does not require additional time during scanning for computational analysis, thereby multiplying the time benefit of CS while retaining the benefits of multidirectional reconstruction and increased resolution. This methodology suffers from inherent degradation of perceptual image quality in its reconstructions because of the L2 loss function the CNN uses to reduce mean squared error, which causes blurring by averaging all possible outcomes of signal distribution during reconstruction. To combat this, researchers have developed another AI to reduce reconstruction times that uses a different loss function in a generative adversarial network to retain image quality, while offering reconstruction times several hundred times faster than current CS-MRI structures.¹² So-called sparse-coding methods promise further reduction in reconstruction times, with the possibility of processing completed online with a lightweight architecture rather than on a local system.¹³

Neuroimaging of acute cases benefits most directly from these technologies because MRIs and their high resolution and SNR begin to approach CT imaging time scales. This could have important implications in clinical care, particularly for stroke imaging and evaluating spinal cord compression. CS-MRI optimization represents one of the greatest areas of neuroimaging cost savings and neurologic care improvement in the modern radiology era.

Reducing Contrast and Radiation Doses

AI has the ability to read CT, MRI, and positron emission tomography (PET) with reduced or without contrast without significant loss in sensitivity for detecting lesions. With MRI, gadolinium-based contrast can cause injection site reactions, allergic reactions, metal deposition throughout the body, and nephrogenic systemic fibrosis in the most severe instances.¹⁴ DL has been applied to brain MRIs performed with 10% of a full dose of contrast without significant degradation of image quality. Neuroradiologists did not rate the AI-synthesized images for several MRI indications lower than their full-dose counterparts.¹⁵ Low-dose contrast imaging, regardless of modality, generates greater noise with a significantly reduced signal. However, with AI applied, researchers found that the software suppressed motion and aliasing artifacts and improved image quality, perhaps evidence that this low-dose modality is less vulnerable to the most common pitfalls of MRI.

Recently, low-dose MRI moved into the spotlight when Subtle Medical SubtleGAD software received a National Institutes of Health grant and an expedited pathway to phase 2 clinical trials.¹⁶ SubtleGAD, a DL AI that enables low-dose MRI interpretation, might allow contrast MRI for patients with advanced kidney disease or contrast allergies. At some point, contrast with MRI might not be necessary because DL AI applied to noncontrast MRs for detecting MS lesions was found to be preliminarily effective with 78% lesion detection sensitivity.¹⁷

PET-MRI combines simultaneous PET and MRI and has been used to evaluate neurologic disorders. PET-MRI can detect amyloid plaques in Alzheimer disease 10 to 20 years before clinical signs of dementia emerge.¹⁸ PET-MRI has sparked DL AI development to decrease the dose of the IV radioactive tracer 18F-florbetaben used in imaging to reduce radiation exposure and imaging costs.This reduction is critical if PET-MRI is to become used widely.^19-21

An initial CNN could reconstruct low-dose amyloid scans to full-dose resolution, albeit with a greater susceptibility to some artifacts and motion blurring.²² Similar to the synthetic multi-orientation resolution enhancement CNN, this program showed signal blurring from the L2 loss function, which was corrected in a later AI that used a generative adversarial network to minimize perceptual loss.²³ This new AI demonstrated greater image resolution, feature preservation, and radiologist rating over the previous AI and was capable of reconstructing low-dose PET scans to full-dose resolution without an accompanying MRI. Applications of this algorithm are far-reaching, potentially allowing neuroimaging of brain tumors at more frequent intervals with higher resolution and lower total radiation exposure.

AI also has been applied to neurologic CT to reduce radiation exposure.²⁴ Because it is critical to abide by the principles of ALARA (as low as reasonably achievable), the ability of AI to reduce radiation exposure holds significant promise. A CNN has been used to transform low-dose CTs of anthropomorphic models with calcium inserts and cardiac patients to normal-dose CTs, with the goal of improving the SNR.²⁵ By training a noise-discriminating CNN and a noise-generating CNN together in a generative adversarial network, the AI improved image feature preservation during transformation. This algorithm has a direct application in imaging cerebral vasculature, including calcification that can explain lacunar infarcts and tracking systemic atherosclerosis.²⁶

Another CNN has been applied to remove more complex noise patterns from the phenomena of beam hardening and photon starvation common in low-dose CT. This algorithm extracts the directional components of artifacts and compares them to known artifact patterns, allowing for highly specific suppression of unwanted signals.²⁷ In June 2019, the US Food and Drug Administration (FDA) approved ClariPi, a deep CNN program for advanced denoising and resolution improvement of low- and ultra low-dose CTs.²⁸ Aside from only low-dose settings, this AI could reduce artifacts in all CT imaging modalities and improve therapeutic value of procedures, including cerebral angiograms and emergency cranial scans. As the average CT radiation dose decreased from 12 mSv in 2009 to 1.5 mSv in 2014 and continues to fall, these algorithms will become increasingly necessary to retain the high resolution and diagnostic power expected of neurologic CTs.^29,30

Downstream Applications

Downstream applications refer to AI use after a radiologic study is acquired, mostly image interpretation. More than 70% of FDA-approved AI medical devices are in radiology, and many of these relate to image analysis.^6,31 Although AI is not limited to black-and-white image interpretation, it is hypothesized that one of the reasons radiology is inviting to AI is because gray-scale images lend themselves to standardization.³ Moreover, most radiology departments already use AI-friendly picture archiving and communication systems.^31,32

AI has been applied to a range of radiologic modalities, including MRI, CT, ultrasonography, PET, and mammography.^32-38 AI also has been specifically applied to radiography, including the interpretation of tuberculosis, pneumonia, lung lesions, and COVID-19.^33,39-45 AI also can assist triage, patient screening, providing a “second opinion” rapidly, shortening the time needed for attaining a diagnosis, monitoring disease progression, and predicting prognosis.^{37-39,43,45-47} Downstream applications of AI in neuroradiology and neurology include using CT to aid in detecting hemorrhage or ischemic stroke; using MRI to automatically segment lesions, such as tumors or MS lesions; assisting in early diagnosis and predicting prognosis in MS; assisting in treating paralysis, including from spinal cord injury; determining seizure type and localizing area of seizure onset; and using cameras, wearable devices, and smartphone applications to diagnose and assess treatment response in neurodegenerative disorders, such as Parkinson or Alzheimer diseases (Figure).^37,48-56

Practical and Ethical Considerations

In any discussion of the benefits of AI, it is prudent to address its shortcomings. Chief among these is overfitting, which occurs when an AI is too closely aligned with its training dataset and prone to error when applied to novel cases. Often this is a byproduct of a small training set.⁶⁰ Neuroradiology, particularly with uncommon, advanced imaging methods, has a smaller number of available studies.⁶¹ Even with more prevalent imaging modalities, such as head CT, the work of collecting training scans from patients with the prerequisite disease processes, particularly if these processes are rare, can limit the number of datapoints collected. Neuroradiologists should understand how an AI tool was generated, including the size and variety of the training dataset used, to best gauge the clinical applicability and fitness of the system.

Another point of concern for AI clinical decision support tools’ implementation is automation bias—the tendency for clinicians to favor machine-generated decisions and ignore contrary data or conflicting human decisions.⁶² This situation often arises when radiologists experience overwhelming patient loads or are in underresourced settings, where there is little ability to review every AI-based diagnosis. Although AI might be of benefit in such conditions by reducing physician workload and streamlining the diagnostic process, there is the propensity to improperly rely on a tool meant to augment, not replace, a radiologist’s judgment. Such cases have led to adverse outcomes for patients, and legal precedence shows that this constitutes negligence.⁶³ Maintaining awareness of each tool’s limitations and proper application is the only remedy for such situations.

Ethically, we must consider the opaqueness of ML-developed neuroimaging AIs. For many systems, the specific process by which an AI arrives at its conclusions is unknown. This AI “black box” can conceal potential errors and biases that are masked by overall positive performance metrics. The lack of understanding about how a tool functions in the zero-failure clinical setting understandably gives radiologists pause. The question must be asked: Is it ethical to use a system that is a relatively unknown quantity? Entities, including state governments, Canada, and the European Union, have produced an answer. Each of these governments have implemented policies requiring that health care AIs use some method to display to end users the process by which they arrive at conclusions.^64-68

The 21st Century Cures Act declares that to attain approval, clinical AIs must demonstrate this explainability to clinicians and patients.⁶⁹ The response has been an explosion in the development of explainable AI. Systems that visualize the areas where AI attention most often rests with heatmaps, generate labels for the most heavily weighted features of radiographic images, and create full diagnostic reports to justify AI conclusions aim to meet the goal of transparency and inspiring confidence in clinical end users.⁷⁰ The ability to understand the “thought process” of a system proves useful for error correction and retooling. A trend toward under- or overdetecting conditions, flagging seemingly irrelevant image regions, or low reproducibility can be better addressed when it is clear how the AI is drawing its false conclusions. With an iterative process of testing and redesigning, false positive and negative rates can be reduced, the need for human intervention can be lowered to an appropriate minimum, and patient outcomes can be improved.⁷¹

Data collection raises another ethical concern. To train functional clinical decision support tools, massive amounts of patient demographic, laboratory, and imaging data are required. With incentives to develop the most powerful AI systems, record collection can venture down a path where patient autonomy and privacy are threatened. Radiologists have a duty to ensure data mining serves patients and improves the practice of radiology while protecting patients’ personal information.⁶² Policies have placed similar limits on the access to and use of patient records.^64-69 Patients have the right to request explanation of the AI systems their data have been used to train. Approval for data acquisition requires the use of explainable AI, standardized data security protocol implementation, and adequate proof of communal benefit from the clinical decision support tool. Establishment of state-mandated protections bodes well for a future when developers can access enormous caches of data while patients and health care professionals are assured that no identifying information has escaped a well-regulated space. On the level of the individual radiologist, the knowledge that each datum represents a human life. These are people who has made themselves vulnerable by seeking relief for what ails them, which should serve as a lasting reminder to operate with utmost care when handling sensitive information.

Conclusions

The demonstrated applications of AI in neuroimaging are numerous and varied, and it is reasonable to assume that its implementation will increase as the technology matures. AI use for detecting important neurologic conditions holds promise in combatting ever greater imaging volumes and providing timely diagnoses. As medicine witnesses the continuing adoption of AI, it is important that practitioners possess an understanding of its current and emerging uses.

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) syndrome is the most common monogenic inherited cause of stroke. CADASIL syndrome is a nonsclerotic angiopathy resulting from a mutation of the NOTCH3 gene on chromosome 19p13, encoding a receptor expressed by vascular smooth muscle cells.¹ This mutation results in migraine, recurrent ischemic stroke, affective disorders, and dementia, with migraine often manifesting earliest.^2,3

The onset of stroke symptoms occurs typically in ages ≥ 60 years with some patients experiencing stroke as early as in their 30s.^1,4 Presentation varies among patients even within the same family.⁵ CADASIL syndrome is frequently mistaken for other more common neurologic conditions due to the low prevalence of CADASIL syndrome, reported to be between 2 and 5 per 100,000.^3,6 The cumulative nature of multiple ischemic episodes seen in 85% of symptomatic individuals leads to disability. Dementia is often hallmarked as one of the features of end-stage CADASIL syndrome.⁷ Extent and severity of brain tissue damage are shown to be the most critical factors of clinical symptoms.⁸ There is no specific treatment for CADASIL syndrome other than addressing risk factors.⁹

Symptoms are traditionally described to be limited to the central nervous system (CNS); however, reports of other organ system effects exist. Twenty-six percent of premature mortality relating to CADASIL syndrome is sudden unexpected death, which several authors have postulated could be attributed to cardiac events.^10,11

The NOTCH3 gene encodes a protein expressed during gastrulation and in the CNS during embryological development. The expression of this protein decreases with time and has limited expression in adulthood.¹² The pathophysiology of CADASIL syndrome includes myriad changes, including cerebral vessels narrowed by intimal thickening due to expansion of the extracellular matrix, degeneration of smooth muscle cells of the cerebral vessel walls, and osmiophilic material deposition in patients with CADASIL syndrome.¹³ Granular osmiophilic material in the vascular basal lamina can be observed on electron microscopy of patients with CADASIL syndrome and are used for diagnostic purposes.¹⁴

CADASIL syndrome often presents a diagnostic dilemma for physicians and is easy to misdiagnose in the early stages. The diagnostic dilemma arises given the subacute onset of CADASIL syndrome with vague early presenting symptoms, such as headache, prior to more specific findings (ie, multiple early strokes or transient ischemic attacks [TIA]). Patients presenting with CADASIL syndrome may be misdiagnosed with other neurologic conditions, including migraine or multiple sclerosis (MS).¹⁵ Especially in the case of MS, lesions visible on magnetic resonance imaging (MRI) may be differentiated by the higher rates of temporo polar lesions seen in CADASIL syndrome in comparison with those in MS.³

It is important to consider CADASIL syndrome in patients presenting at a young age with stroke due to the compounding effects of multiple ischemic episodes and subsequent motor/sensory and neuropsychologic deficits. This necessitates increasing awareness of CADASIL syndrome in the neurologic and radiologic community and the importance of educating families of patients on the importance of being evaluated. This diagnostic dilemma can lead to delay in appropriate therapy and control of related modifiable risk factors, including hypertension, hyperlipidemia, etc. Delays in initiation of anti-stroke pharmacotherapy can lead to additional morbidity and mortality in these patients.

The radiology of CADASIL syndrome is unique and particularly important due to the possible confusion with MS. MRI is an important tool in the evaluation of the cerebral pathology of CADASIL syndrome, revealing white matter and microangiopathic signal abnormalities, indicative of ischemic infarcts, lacunar strokes, and diffuse leukoencephalopathy.^13,16 MRI lesions are often seen in the basal ganglia, thalamus, external capsule, and pons.⁷ The lesions also are seen in the periventricular region, explaining its misperception as MS.¹⁷ In addition, cerebral microhemorrhages have been seen. To further differentiate these lesions, the anterior temporal lobe should be observed for gliosis or hyperintensities, which correlates with CADASIL syndrome.¹⁸ Location of hyperintensity in the temporal lobes, relative sparing of the occipital/orbitofrontal white matter, corpus callosum, subcortical u-fibers, and cortex is helpful in differentiating from other etiologies, such as microvascular white matter ischemic disease, MS, and mitochondrial encephalopathy with lactic acidosis and strokelike symptoms (MELAS).

Case Presentation

A patient aged > 50 years presented to the emergency department (ED) due to numbness of the right perioral area, gait difficulties, difficulty speaking, and increasing right lower extremity weakness with no numbness or paresthesia. The patient’s medical history is relevant for CADASIL syndrome, hypertension, prior cerebrovascular accident, recurrent TIAs, multinodular goiter with a history of radioactive iodine treatment, and neurogenic bladder controlled with oxybutynin since age 30 years. The patient had a significant stroke history: the first stroke occurred at age 36 years and 3 more strokes at ages 38, 44, and 53 years and 4 TIAs over that period. This patient reported no recent headache or memory changes and had no history of smoking, alcohol, or recreational drug use. Family history was pertinent for the mother’s death secondary to stroke, with a history of multiple strokes beginning at a young, undetermined age and no major motor, sensory, or neuropsychologic deficits prior to her death. A sister and first cousin had been diagnosed with MS.

On triage in the ED, stroke alert was called but tissue plasminogen activator was not given due to time eligibility. The patient’s numbness and weakness were improved within 7 hours, but she continued to have difficulty with dysarthric speech and unsteady gait following this incident. Antihypertensive medications were discontinued on admission to allow for permissive hypertension to improve cerebral blood flow. A brain MRI revealed bilateral increased T2 fluid-attenuated inversion recovery (FLAIR) signal in the anterior temporal lobes, confluent increased T2 FLAIR signal in the periventricular/deep white matter, bilateral basal ganglia chronic lacunar infarcts, and several chronic microbleeds (Figure 1). There was no evidence for an acute infarct on the MRI. Recrudescence of prior stroke symptoms secondary to CADASIL syndrome was suspected as a primary diagnosis with a differential of TIA.

Discussion

This patient with CADASIL syndrome and significant stroke history with cardiac symptoms demonstrates 3 important discussion points: the difficulty of early diagnosis, high rates of morbidity/mortality, and the need for further research into the cardiac effects of CADASIL syndrome. Due to this patient’s bradycardic episodes while being monitored on telemetry, it is possible that the cause of the strokelike symptoms was a TIA, secondary to decreased perfusion pressure, explaining the lack of acute ischemia on imaging. With regards to the history of thyroid dysfunction, this particular episode of bradycardia was unlikely to be related as the thyroid-stimulating hormone was reflective of subclinical hyperthyroidism with T4 levels within normal limits.

This case demonstrates a potential link between CADASIL syndrome and autonomic dysfunction. Similar to general stroke patients, patients with CADASIL syndrome are at an increased risk of hypoperfusion injury secondary to cardiovascular and autonomic dysfunction. This raises a question of initial and surveillance screening tests on diagnosis of CADASIL syndrome. It may be appropriate to obtain routine echocardiogram and ECG and other arrhythmia screening tests in these patients, especially during or following an ischemic episode. However, more evidence is required to support creation of a formal recommendation.

In a study of cardiac rhythm abnormalities in a half-million adults, 1.57% of women aged 55 to 64 years were found to have rhythm abnormality with 0.27% having a bradyarrhythmia.¹⁹ In the setting of neurologic disease, ECG changes such as arrhythmias and repolarization changes are regularly noted.²⁰ However, it is unlikely that the bradycardia would be causing the brain lesions. In CADASIL syndrome, there is relative sparing of the occipital, orbitofrontal subcortical white matter, subcortical fibers, and cortex. Specifically, within CADASIL syndrome, a study of 23 patients showed no ECG changes regarding infarction/ischemia, conduction disturbances, or arrhythmias compared with that of controls.²¹

Further research into the cardiac effects of CADASIL syndrome is needed. As CADASIL syndrome is primarily a disorder of the vasculature, the disease has potential to affect the heart in addition to the brain.¹ This theory is well supported by the embryologic effects of the NOTCH3 receptor pathways, which are responsible for the development of the cardiovascular system.²² Anecdotal evidence supports this theory as few case reports have been published that describe various cardiac abnormalities in patients with CADASIL syndrome, including myocardial infarction (MI), conduction abnormalities, and arrhythmias.^{2, 23-25}

There have only been 2 published studies regarding investigations into CADASIL syndrome and cardiac disease. The first paper was a case-control study that investigated ECG changes in the setting of CADASIL syndrome. The study found no evidence for MI, ischemia, conduction disorder, or arrhythmias in patients with CADASIL syndrome.²¹ Unfortunately, this study was underpowered and limited in scope, only investigating a single ECG recording from 23 patients with CADASIL syndrome in a single clinic.²¹ Other cardiac markers, such as echocardiogram, stress test, and contractility, and longitudinal cardiac outcomes were not investigated in this study.²¹ The second paper was also a case-control study by Rufa and colleagues that investigated HR variability and other ECG changes during a 10-minute rest recording on 23 patients with CADASIL syndrome and compared the results to 22 age- and gender-matched patients in good health.¹¹

This study found reduced HR variability and an increased ratio of low-frequency to high-frequency variability, which the authors claimed demonstrates autonomic dysfunction in patients with CADASIL syndrome.¹¹ Rufa and colleagues concluded that patients with CADASIL syndrome are at higher risk for cardiac arrhythmias.¹¹ This study also found no evidence for MI, ischemia, conduction disorder, or arrhythmias in the patients with CADASIL syndrome compared with that of age-matched controls.¹¹ Similar to the first paper, this study is underpowered, only looks at a single timepoint recording, and uses incomplete and indirect measurements of cardiac function.

There is a need for a longitudinal review of cardiac outcomes in the CADASIL syndrome population to determine whether these patients require additional surveillance or prophylaxis. While the variability in HR of our patient cannot be definitively attributed solely to CADASIL syndrome, the subsequent admissions demonstrate that long-term monitoring may be warranted.

Conclusions

CADASIL syndrome is an autosomal dominant NOTCH3 signaling disease that affects the small vessel vasculature and leads to early ischemic events, headache, dementia, and death. CADASIL syndrome is frequently misdiagnosed due to insidious onset and vague presenting symptoms. Delay in diagnosis often results in nonoptimized medical management. Current guidelines recommend following poststroke protocol and minimizing individual risk factors by using antiplatelet, antihypertensive, and dyslipidemia medications. This case demonstrates a classic presentation of CADASIL syndrome with lesser described cardiac symptoms. Few cases of unusual cardiac symptoms in the setting of CADASIL syndrome have been reported. The relationship between cardiovascular disease and CADASIL syndrome is not well described. Further research is needed to elucidate any links between CADASIL syndrome and cardiovascular disease and to optimize management for these patients.

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) syndrome is the most common monogenic inherited cause of stroke. CADASIL syndrome is a nonsclerotic angiopathy resulting from a mutation of the NOTCH3 gene on chromosome 19p13, encoding a receptor expressed by vascular smooth muscle cells.¹ This mutation results in migraine, recurrent ischemic stroke, affective disorders, and dementia, with migraine often manifesting earliest.^2,3

The onset of stroke symptoms occurs typically in ages ≥ 60 years with some patients experiencing stroke as early as in their 30s.^1,4 Presentation varies among patients even within the same family.⁵ CADASIL syndrome is frequently mistaken for other more common neurologic conditions due to the low prevalence of CADASIL syndrome, reported to be between 2 and 5 per 100,000.^3,6 The cumulative nature of multiple ischemic episodes seen in 85% of symptomatic individuals leads to disability. Dementia is often hallmarked as one of the features of end-stage CADASIL syndrome.⁷ Extent and severity of brain tissue damage are shown to be the most critical factors of clinical symptoms.⁸ There is no specific treatment for CADASIL syndrome other than addressing risk factors.⁹

Symptoms are traditionally described to be limited to the central nervous system (CNS); however, reports of other organ system effects exist. Twenty-six percent of premature mortality relating to CADASIL syndrome is sudden unexpected death, which several authors have postulated could be attributed to cardiac events.^10,11

The NOTCH3 gene encodes a protein expressed during gastrulation and in the CNS during embryological development. The expression of this protein decreases with time and has limited expression in adulthood.¹² The pathophysiology of CADASIL syndrome includes myriad changes, including cerebral vessels narrowed by intimal thickening due to expansion of the extracellular matrix, degeneration of smooth muscle cells of the cerebral vessel walls, and osmiophilic material deposition in patients with CADASIL syndrome.¹³ Granular osmiophilic material in the vascular basal lamina can be observed on electron microscopy of patients with CADASIL syndrome and are used for diagnostic purposes.¹⁴

CADASIL syndrome often presents a diagnostic dilemma for physicians and is easy to misdiagnose in the early stages. The diagnostic dilemma arises given the subacute onset of CADASIL syndrome with vague early presenting symptoms, such as headache, prior to more specific findings (ie, multiple early strokes or transient ischemic attacks [TIA]). Patients presenting with CADASIL syndrome may be misdiagnosed with other neurologic conditions, including migraine or multiple sclerosis (MS).¹⁵ Especially in the case of MS, lesions visible on magnetic resonance imaging (MRI) may be differentiated by the higher rates of temporo polar lesions seen in CADASIL syndrome in comparison with those in MS.³

It is important to consider CADASIL syndrome in patients presenting at a young age with stroke due to the compounding effects of multiple ischemic episodes and subsequent motor/sensory and neuropsychologic deficits. This necessitates increasing awareness of CADASIL syndrome in the neurologic and radiologic community and the importance of educating families of patients on the importance of being evaluated. This diagnostic dilemma can lead to delay in appropriate therapy and control of related modifiable risk factors, including hypertension, hyperlipidemia, etc. Delays in initiation of anti-stroke pharmacotherapy can lead to additional morbidity and mortality in these patients.

The radiology of CADASIL syndrome is unique and particularly important due to the possible confusion with MS. MRI is an important tool in the evaluation of the cerebral pathology of CADASIL syndrome, revealing white matter and microangiopathic signal abnormalities, indicative of ischemic infarcts, lacunar strokes, and diffuse leukoencephalopathy.^13,16 MRI lesions are often seen in the basal ganglia, thalamus, external capsule, and pons.⁷ The lesions also are seen in the periventricular region, explaining its misperception as MS.¹⁷ In addition, cerebral microhemorrhages have been seen. To further differentiate these lesions, the anterior temporal lobe should be observed for gliosis or hyperintensities, which correlates with CADASIL syndrome.¹⁸ Location of hyperintensity in the temporal lobes, relative sparing of the occipital/orbitofrontal white matter, corpus callosum, subcortical u-fibers, and cortex is helpful in differentiating from other etiologies, such as microvascular white matter ischemic disease, MS, and mitochondrial encephalopathy with lactic acidosis and strokelike symptoms (MELAS).

Case Presentation

A patient aged > 50 years presented to the emergency department (ED) due to numbness of the right perioral area, gait difficulties, difficulty speaking, and increasing right lower extremity weakness with no numbness or paresthesia. The patient’s medical history is relevant for CADASIL syndrome, hypertension, prior cerebrovascular accident, recurrent TIAs, multinodular goiter with a history of radioactive iodine treatment, and neurogenic bladder controlled with oxybutynin since age 30 years. The patient had a significant stroke history: the first stroke occurred at age 36 years and 3 more strokes at ages 38, 44, and 53 years and 4 TIAs over that period. This patient reported no recent headache or memory changes and had no history of smoking, alcohol, or recreational drug use. Family history was pertinent for the mother’s death secondary to stroke, with a history of multiple strokes beginning at a young, undetermined age and no major motor, sensory, or neuropsychologic deficits prior to her death. A sister and first cousin had been diagnosed with MS.

On triage in the ED, stroke alert was called but tissue plasminogen activator was not given due to time eligibility. The patient’s numbness and weakness were improved within 7 hours, but she continued to have difficulty with dysarthric speech and unsteady gait following this incident. Antihypertensive medications were discontinued on admission to allow for permissive hypertension to improve cerebral blood flow. A brain MRI revealed bilateral increased T2 fluid-attenuated inversion recovery (FLAIR) signal in the anterior temporal lobes, confluent increased T2 FLAIR signal in the periventricular/deep white matter, bilateral basal ganglia chronic lacunar infarcts, and several chronic microbleeds (Figure 1). There was no evidence for an acute infarct on the MRI. Recrudescence of prior stroke symptoms secondary to CADASIL syndrome was suspected as a primary diagnosis with a differential of TIA.

Discussion

This patient with CADASIL syndrome and significant stroke history with cardiac symptoms demonstrates 3 important discussion points: the difficulty of early diagnosis, high rates of morbidity/mortality, and the need for further research into the cardiac effects of CADASIL syndrome. Due to this patient’s bradycardic episodes while being monitored on telemetry, it is possible that the cause of the strokelike symptoms was a TIA, secondary to decreased perfusion pressure, explaining the lack of acute ischemia on imaging. With regards to the history of thyroid dysfunction, this particular episode of bradycardia was unlikely to be related as the thyroid-stimulating hormone was reflective of subclinical hyperthyroidism with T4 levels within normal limits.

This case demonstrates a potential link between CADASIL syndrome and autonomic dysfunction. Similar to general stroke patients, patients with CADASIL syndrome are at an increased risk of hypoperfusion injury secondary to cardiovascular and autonomic dysfunction. This raises a question of initial and surveillance screening tests on diagnosis of CADASIL syndrome. It may be appropriate to obtain routine echocardiogram and ECG and other arrhythmia screening tests in these patients, especially during or following an ischemic episode. However, more evidence is required to support creation of a formal recommendation.

In a study of cardiac rhythm abnormalities in a half-million adults, 1.57% of women aged 55 to 64 years were found to have rhythm abnormality with 0.27% having a bradyarrhythmia.¹⁹ In the setting of neurologic disease, ECG changes such as arrhythmias and repolarization changes are regularly noted.²⁰ However, it is unlikely that the bradycardia would be causing the brain lesions. In CADASIL syndrome, there is relative sparing of the occipital, orbitofrontal subcortical white matter, subcortical fibers, and cortex. Specifically, within CADASIL syndrome, a study of 23 patients showed no ECG changes regarding infarction/ischemia, conduction disturbances, or arrhythmias compared with that of controls.²¹

Further research into the cardiac effects of CADASIL syndrome is needed. As CADASIL syndrome is primarily a disorder of the vasculature, the disease has potential to affect the heart in addition to the brain.¹ This theory is well supported by the embryologic effects of the NOTCH3 receptor pathways, which are responsible for the development of the cardiovascular system.²² Anecdotal evidence supports this theory as few case reports have been published that describe various cardiac abnormalities in patients with CADASIL syndrome, including myocardial infarction (MI), conduction abnormalities, and arrhythmias.^{2, 23-25}

There have only been 2 published studies regarding investigations into CADASIL syndrome and cardiac disease. The first paper was a case-control study that investigated ECG changes in the setting of CADASIL syndrome. The study found no evidence for MI, ischemia, conduction disorder, or arrhythmias in patients with CADASIL syndrome.²¹ Unfortunately, this study was underpowered and limited in scope, only investigating a single ECG recording from 23 patients with CADASIL syndrome in a single clinic.²¹ Other cardiac markers, such as echocardiogram, stress test, and contractility, and longitudinal cardiac outcomes were not investigated in this study.²¹ The second paper was also a case-control study by Rufa and colleagues that investigated HR variability and other ECG changes during a 10-minute rest recording on 23 patients with CADASIL syndrome and compared the results to 22 age- and gender-matched patients in good health.¹¹

This study found reduced HR variability and an increased ratio of low-frequency to high-frequency variability, which the authors claimed demonstrates autonomic dysfunction in patients with CADASIL syndrome.¹¹ Rufa and colleagues concluded that patients with CADASIL syndrome are at higher risk for cardiac arrhythmias.¹¹ This study also found no evidence for MI, ischemia, conduction disorder, or arrhythmias in the patients with CADASIL syndrome compared with that of age-matched controls.¹¹ Similar to the first paper, this study is underpowered, only looks at a single timepoint recording, and uses incomplete and indirect measurements of cardiac function.

There is a need for a longitudinal review of cardiac outcomes in the CADASIL syndrome population to determine whether these patients require additional surveillance or prophylaxis. While the variability in HR of our patient cannot be definitively attributed solely to CADASIL syndrome, the subsequent admissions demonstrate that long-term monitoring may be warranted.

Conclusions

CADASIL syndrome is an autosomal dominant NOTCH3 signaling disease that affects the small vessel vasculature and leads to early ischemic events, headache, dementia, and death. CADASIL syndrome is frequently misdiagnosed due to insidious onset and vague presenting symptoms. Delay in diagnosis often results in nonoptimized medical management. Current guidelines recommend following poststroke protocol and minimizing individual risk factors by using antiplatelet, antihypertensive, and dyslipidemia medications. This case demonstrates a classic presentation of CADASIL syndrome with lesser described cardiac symptoms. Few cases of unusual cardiac symptoms in the setting of CADASIL syndrome have been reported. The relationship between cardiovascular disease and CADASIL syndrome is not well described. Further research is needed to elucidate any links between CADASIL syndrome and cardiovascular disease and to optimize management for these patients.

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) syndrome is the most common monogenic inherited cause of stroke. CADASIL syndrome is a nonsclerotic angiopathy resulting from a mutation of the NOTCH3 gene on chromosome 19p13, encoding a receptor expressed by vascular smooth muscle cells.¹ This mutation results in migraine, recurrent ischemic stroke, affective disorders, and dementia, with migraine often manifesting earliest.^2,3

The onset of stroke symptoms occurs typically in ages ≥ 60 years with some patients experiencing stroke as early as in their 30s.^1,4 Presentation varies among patients even within the same family.⁵ CADASIL syndrome is frequently mistaken for other more common neurologic conditions due to the low prevalence of CADASIL syndrome, reported to be between 2 and 5 per 100,000.^3,6 The cumulative nature of multiple ischemic episodes seen in 85% of symptomatic individuals leads to disability. Dementia is often hallmarked as one of the features of end-stage CADASIL syndrome.⁷ Extent and severity of brain tissue damage are shown to be the most critical factors of clinical symptoms.⁸ There is no specific treatment for CADASIL syndrome other than addressing risk factors.⁹

Symptoms are traditionally described to be limited to the central nervous system (CNS); however, reports of other organ system effects exist. Twenty-six percent of premature mortality relating to CADASIL syndrome is sudden unexpected death, which several authors have postulated could be attributed to cardiac events.^10,11

The NOTCH3 gene encodes a protein expressed during gastrulation and in the CNS during embryological development. The expression of this protein decreases with time and has limited expression in adulthood.¹² The pathophysiology of CADASIL syndrome includes myriad changes, including cerebral vessels narrowed by intimal thickening due to expansion of the extracellular matrix, degeneration of smooth muscle cells of the cerebral vessel walls, and osmiophilic material deposition in patients with CADASIL syndrome.¹³ Granular osmiophilic material in the vascular basal lamina can be observed on electron microscopy of patients with CADASIL syndrome and are used for diagnostic purposes.¹⁴

CADASIL syndrome often presents a diagnostic dilemma for physicians and is easy to misdiagnose in the early stages. The diagnostic dilemma arises given the subacute onset of CADASIL syndrome with vague early presenting symptoms, such as headache, prior to more specific findings (ie, multiple early strokes or transient ischemic attacks [TIA]). Patients presenting with CADASIL syndrome may be misdiagnosed with other neurologic conditions, including migraine or multiple sclerosis (MS).¹⁵ Especially in the case of MS, lesions visible on magnetic resonance imaging (MRI) may be differentiated by the higher rates of temporo polar lesions seen in CADASIL syndrome in comparison with those in MS.³

It is important to consider CADASIL syndrome in patients presenting at a young age with stroke due to the compounding effects of multiple ischemic episodes and subsequent motor/sensory and neuropsychologic deficits. This necessitates increasing awareness of CADASIL syndrome in the neurologic and radiologic community and the importance of educating families of patients on the importance of being evaluated. This diagnostic dilemma can lead to delay in appropriate therapy and control of related modifiable risk factors, including hypertension, hyperlipidemia, etc. Delays in initiation of anti-stroke pharmacotherapy can lead to additional morbidity and mortality in these patients.

The radiology of CADASIL syndrome is unique and particularly important due to the possible confusion with MS. MRI is an important tool in the evaluation of the cerebral pathology of CADASIL syndrome, revealing white matter and microangiopathic signal abnormalities, indicative of ischemic infarcts, lacunar strokes, and diffuse leukoencephalopathy.^13,16 MRI lesions are often seen in the basal ganglia, thalamus, external capsule, and pons.⁷ The lesions also are seen in the periventricular region, explaining its misperception as MS.¹⁷ In addition, cerebral microhemorrhages have been seen. To further differentiate these lesions, the anterior temporal lobe should be observed for gliosis or hyperintensities, which correlates with CADASIL syndrome.¹⁸ Location of hyperintensity in the temporal lobes, relative sparing of the occipital/orbitofrontal white matter, corpus callosum, subcortical u-fibers, and cortex is helpful in differentiating from other etiologies, such as microvascular white matter ischemic disease, MS, and mitochondrial encephalopathy with lactic acidosis and strokelike symptoms (MELAS).

Case Presentation

A patient aged > 50 years presented to the emergency department (ED) due to numbness of the right perioral area, gait difficulties, difficulty speaking, and increasing right lower extremity weakness with no numbness or paresthesia. The patient’s medical history is relevant for CADASIL syndrome, hypertension, prior cerebrovascular accident, recurrent TIAs, multinodular goiter with a history of radioactive iodine treatment, and neurogenic bladder controlled with oxybutynin since age 30 years. The patient had a significant stroke history: the first stroke occurred at age 36 years and 3 more strokes at ages 38, 44, and 53 years and 4 TIAs over that period. This patient reported no recent headache or memory changes and had no history of smoking, alcohol, or recreational drug use. Family history was pertinent for the mother’s death secondary to stroke, with a history of multiple strokes beginning at a young, undetermined age and no major motor, sensory, or neuropsychologic deficits prior to her death. A sister and first cousin had been diagnosed with MS.

On triage in the ED, stroke alert was called but tissue plasminogen activator was not given due to time eligibility. The patient’s numbness and weakness were improved within 7 hours, but she continued to have difficulty with dysarthric speech and unsteady gait following this incident. Antihypertensive medications were discontinued on admission to allow for permissive hypertension to improve cerebral blood flow. A brain MRI revealed bilateral increased T2 fluid-attenuated inversion recovery (FLAIR) signal in the anterior temporal lobes, confluent increased T2 FLAIR signal in the periventricular/deep white matter, bilateral basal ganglia chronic lacunar infarcts, and several chronic microbleeds (Figure 1). There was no evidence for an acute infarct on the MRI. Recrudescence of prior stroke symptoms secondary to CADASIL syndrome was suspected as a primary diagnosis with a differential of TIA.

Discussion

This patient with CADASIL syndrome and significant stroke history with cardiac symptoms demonstrates 3 important discussion points: the difficulty of early diagnosis, high rates of morbidity/mortality, and the need for further research into the cardiac effects of CADASIL syndrome. Due to this patient’s bradycardic episodes while being monitored on telemetry, it is possible that the cause of the strokelike symptoms was a TIA, secondary to decreased perfusion pressure, explaining the lack of acute ischemia on imaging. With regards to the history of thyroid dysfunction, this particular episode of bradycardia was unlikely to be related as the thyroid-stimulating hormone was reflective of subclinical hyperthyroidism with T4 levels within normal limits.

This case demonstrates a potential link between CADASIL syndrome and autonomic dysfunction. Similar to general stroke patients, patients with CADASIL syndrome are at an increased risk of hypoperfusion injury secondary to cardiovascular and autonomic dysfunction. This raises a question of initial and surveillance screening tests on diagnosis of CADASIL syndrome. It may be appropriate to obtain routine echocardiogram and ECG and other arrhythmia screening tests in these patients, especially during or following an ischemic episode. However, more evidence is required to support creation of a formal recommendation.

In a study of cardiac rhythm abnormalities in a half-million adults, 1.57% of women aged 55 to 64 years were found to have rhythm abnormality with 0.27% having a bradyarrhythmia.¹⁹ In the setting of neurologic disease, ECG changes such as arrhythmias and repolarization changes are regularly noted.²⁰ However, it is unlikely that the bradycardia would be causing the brain lesions. In CADASIL syndrome, there is relative sparing of the occipital, orbitofrontal subcortical white matter, subcortical fibers, and cortex. Specifically, within CADASIL syndrome, a study of 23 patients showed no ECG changes regarding infarction/ischemia, conduction disturbances, or arrhythmias compared with that of controls.²¹

Further research into the cardiac effects of CADASIL syndrome is needed. As CADASIL syndrome is primarily a disorder of the vasculature, the disease has potential to affect the heart in addition to the brain.¹ This theory is well supported by the embryologic effects of the NOTCH3 receptor pathways, which are responsible for the development of the cardiovascular system.²² Anecdotal evidence supports this theory as few case reports have been published that describe various cardiac abnormalities in patients with CADASIL syndrome, including myocardial infarction (MI), conduction abnormalities, and arrhythmias.^{2, 23-25}

There have only been 2 published studies regarding investigations into CADASIL syndrome and cardiac disease. The first paper was a case-control study that investigated ECG changes in the setting of CADASIL syndrome. The study found no evidence for MI, ischemia, conduction disorder, or arrhythmias in patients with CADASIL syndrome.²¹ Unfortunately, this study was underpowered and limited in scope, only investigating a single ECG recording from 23 patients with CADASIL syndrome in a single clinic.²¹ Other cardiac markers, such as echocardiogram, stress test, and contractility, and longitudinal cardiac outcomes were not investigated in this study.²¹ The second paper was also a case-control study by Rufa and colleagues that investigated HR variability and other ECG changes during a 10-minute rest recording on 23 patients with CADASIL syndrome and compared the results to 22 age- and gender-matched patients in good health.¹¹

This study found reduced HR variability and an increased ratio of low-frequency to high-frequency variability, which the authors claimed demonstrates autonomic dysfunction in patients with CADASIL syndrome.¹¹ Rufa and colleagues concluded that patients with CADASIL syndrome are at higher risk for cardiac arrhythmias.¹¹ This study also found no evidence for MI, ischemia, conduction disorder, or arrhythmias in the patients with CADASIL syndrome compared with that of age-matched controls.¹¹ Similar to the first paper, this study is underpowered, only looks at a single timepoint recording, and uses incomplete and indirect measurements of cardiac function.

There is a need for a longitudinal review of cardiac outcomes in the CADASIL syndrome population to determine whether these patients require additional surveillance or prophylaxis. While the variability in HR of our patient cannot be definitively attributed solely to CADASIL syndrome, the subsequent admissions demonstrate that long-term monitoring may be warranted.

Conclusions

CADASIL syndrome is an autosomal dominant NOTCH3 signaling disease that affects the small vessel vasculature and leads to early ischemic events, headache, dementia, and death. CADASIL syndrome is frequently misdiagnosed due to insidious onset and vague presenting symptoms. Delay in diagnosis often results in nonoptimized medical management. Current guidelines recommend following poststroke protocol and minimizing individual risk factors by using antiplatelet, antihypertensive, and dyslipidemia medications. This case demonstrates a classic presentation of CADASIL syndrome with lesser described cardiac symptoms. Few cases of unusual cardiac symptoms in the setting of CADASIL syndrome have been reported. The relationship between cardiovascular disease and CADASIL syndrome is not well described. Further research is needed to elucidate any links between CADASIL syndrome and cardiovascular disease and to optimize management for these patients.