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AUSTIN, TEX. – The findings could point to new therapeutic strategies, according to study author Ali Ezzati, MD.
“A lot of diagnostic criteria that we have in the migraine world come from consensus groups of experts, and based on their experience and available data. They classify different types of headache and then on top of that different types of migraine. Unfortunately, this type of classification does not necessarily lead to having very homogeneous groups,” said Dr. Ezzati, who presented the study at the annual meeting of the American Headache Society.
Migraines are generally categorized as episodic (0-14 headache days per month) or chronic (15 or more per month), or as with or without aura. But these broad categories fail to capture the true diversity of migraine, according to Dr. Ezzati, and this may contribute to the fact that response to migraine therapy hovers around 60%. “We feel that the key to improving therapeutic efficacy is to identify individuals who are more homogeneous, more similar to each other, so that when we give a treatment, it is specifically targeting the underlying pathophysiology that those people have,” said Dr. Ezzati, who is an associate professor of neurology and director of the neuroinformatics program at University of California, Irvine.
The analysis revealed some clinically interesting results, said Dr. Ezzati. “For example, allodynia is a symptom that is not particularly used for classification of different types of migraine. There was a specific group that was very high in allodynia, and they were not very responsive to treatments, so that might be a [group] that people have to focus on. Also, we talk a lot about comorbidities in migraine, but we don’t talk about how these comorbidities affect the therapeutic strategies and treatment response to specific medications. We showed that people who have depression are actually less responsive than other groups to treatments, especially prescription medications,” he said.
Machine learning reveals clusters
The researchers analyzed data from 4,423 patients drawn from the American Migraine Prevalence and Prevention Study, which was conducted every year between 2005 and 2009. They included adult patients who filled out surveys in both 2006 and 2007. The study population was 83.7% female and had a mean age of 46.8 years, and 6.4% had chronic migraine. The researchers then used a machine-learning based self-organizing map to group patients into similar clusters.
The algorithm produced five such groups: Cluster 1 had the lowest symptom severity, and 0.6% had chronic migraine. Cluster 2 had mild symptom severity with no chronic migraine. Cluster 3 had moderate symptom severity and a high prevalence of allodynia (88.5%, vs. 63.4% overall, P < .001) and no chronic migraine. Cluster 4 had a high frequency of depressive symptoms (63.1% vs. 19.8% overall, P < .001) and 5.2% had chronic migraine. Cluster 5 had frequent and severe migraines, and most (83.0%) had chronic migraine (P < .001).
There were some other broader trends. Triptans were more commonly used in clusters 2 (25.6%), 3 (27.9%), and 5 (28.0%), but less so in cluster 4 (17.1%; P < .001). Pain freedom at 2 hours was most common in cluster 1 (53.1%), followed by cluster 2 (46.4%), but was significantly less frequent in clusters 3 (32.2%), 4 (32.2%), and 5 (34.7%; P < .001).
Therapeutic implications
Dr. Ezzati believes that machine learning and data analysis could point the way to a future of more tailored migraine therapies. “I think we have to in general go down the path of using more evidence and more data to inform us about individualized planning for patients. For that we need larger clinical studies and larger epidemiological studies to help us identify more homogeneous subtypes of patients that we can eventually target in clinical trials,” he said.
Catherine Chong, MD, who chaired the session where the research was presented, praised the study in an interview. “Episodic migraine and chronic migraine have been developed [as categories] by headache frequency per month, and it was basically based on consensus in committee. They made basically a determination that 15 and under migraine days would be episodic migraine and over would be chronic migraine. So they dichotomized migraine, in a way, based on what people thought in the field. Looking at the data freely, and letting the algorithm determine the different subtypes, and putting everybody with migraine in it, and having these groups naturally appear from the data, I think is fascinating,” Dr. Chong said.
She echoed Dr. Ezzati’s call for further research that could create even more subgroups. “Is it really truly the case that somebody with less than 15 migraine days [per month], that 14 migraines days would be so different than somebody with 15 or over, or 8? I think we need to look at it further to see whether there are additional subgroups within that data. I think there are probably more [groups identifiable] from different data that we have out there,” said Dr. Chong.
Dr. Ezzati has consulted for or been a reviewer or advisory board member for Corium, Eisai, GlaxoSmithKline, Mint Research, and Health Care Horizon Scanning System. He has received research funding from Amgen. Dr. Chong has no relevant financial disclosures.
AUSTIN, TEX. – The findings could point to new therapeutic strategies, according to study author Ali Ezzati, MD.
“A lot of diagnostic criteria that we have in the migraine world come from consensus groups of experts, and based on their experience and available data. They classify different types of headache and then on top of that different types of migraine. Unfortunately, this type of classification does not necessarily lead to having very homogeneous groups,” said Dr. Ezzati, who presented the study at the annual meeting of the American Headache Society.
Migraines are generally categorized as episodic (0-14 headache days per month) or chronic (15 or more per month), or as with or without aura. But these broad categories fail to capture the true diversity of migraine, according to Dr. Ezzati, and this may contribute to the fact that response to migraine therapy hovers around 60%. “We feel that the key to improving therapeutic efficacy is to identify individuals who are more homogeneous, more similar to each other, so that when we give a treatment, it is specifically targeting the underlying pathophysiology that those people have,” said Dr. Ezzati, who is an associate professor of neurology and director of the neuroinformatics program at University of California, Irvine.
The analysis revealed some clinically interesting results, said Dr. Ezzati. “For example, allodynia is a symptom that is not particularly used for classification of different types of migraine. There was a specific group that was very high in allodynia, and they were not very responsive to treatments, so that might be a [group] that people have to focus on. Also, we talk a lot about comorbidities in migraine, but we don’t talk about how these comorbidities affect the therapeutic strategies and treatment response to specific medications. We showed that people who have depression are actually less responsive than other groups to treatments, especially prescription medications,” he said.
Machine learning reveals clusters
The researchers analyzed data from 4,423 patients drawn from the American Migraine Prevalence and Prevention Study, which was conducted every year between 2005 and 2009. They included adult patients who filled out surveys in both 2006 and 2007. The study population was 83.7% female and had a mean age of 46.8 years, and 6.4% had chronic migraine. The researchers then used a machine-learning based self-organizing map to group patients into similar clusters.
The algorithm produced five such groups: Cluster 1 had the lowest symptom severity, and 0.6% had chronic migraine. Cluster 2 had mild symptom severity with no chronic migraine. Cluster 3 had moderate symptom severity and a high prevalence of allodynia (88.5%, vs. 63.4% overall, P < .001) and no chronic migraine. Cluster 4 had a high frequency of depressive symptoms (63.1% vs. 19.8% overall, P < .001) and 5.2% had chronic migraine. Cluster 5 had frequent and severe migraines, and most (83.0%) had chronic migraine (P < .001).
There were some other broader trends. Triptans were more commonly used in clusters 2 (25.6%), 3 (27.9%), and 5 (28.0%), but less so in cluster 4 (17.1%; P < .001). Pain freedom at 2 hours was most common in cluster 1 (53.1%), followed by cluster 2 (46.4%), but was significantly less frequent in clusters 3 (32.2%), 4 (32.2%), and 5 (34.7%; P < .001).
Therapeutic implications
Dr. Ezzati believes that machine learning and data analysis could point the way to a future of more tailored migraine therapies. “I think we have to in general go down the path of using more evidence and more data to inform us about individualized planning for patients. For that we need larger clinical studies and larger epidemiological studies to help us identify more homogeneous subtypes of patients that we can eventually target in clinical trials,” he said.
Catherine Chong, MD, who chaired the session where the research was presented, praised the study in an interview. “Episodic migraine and chronic migraine have been developed [as categories] by headache frequency per month, and it was basically based on consensus in committee. They made basically a determination that 15 and under migraine days would be episodic migraine and over would be chronic migraine. So they dichotomized migraine, in a way, based on what people thought in the field. Looking at the data freely, and letting the algorithm determine the different subtypes, and putting everybody with migraine in it, and having these groups naturally appear from the data, I think is fascinating,” Dr. Chong said.
She echoed Dr. Ezzati’s call for further research that could create even more subgroups. “Is it really truly the case that somebody with less than 15 migraine days [per month], that 14 migraines days would be so different than somebody with 15 or over, or 8? I think we need to look at it further to see whether there are additional subgroups within that data. I think there are probably more [groups identifiable] from different data that we have out there,” said Dr. Chong.
Dr. Ezzati has consulted for or been a reviewer or advisory board member for Corium, Eisai, GlaxoSmithKline, Mint Research, and Health Care Horizon Scanning System. He has received research funding from Amgen. Dr. Chong has no relevant financial disclosures.
AUSTIN, TEX. – The findings could point to new therapeutic strategies, according to study author Ali Ezzati, MD.
“A lot of diagnostic criteria that we have in the migraine world come from consensus groups of experts, and based on their experience and available data. They classify different types of headache and then on top of that different types of migraine. Unfortunately, this type of classification does not necessarily lead to having very homogeneous groups,” said Dr. Ezzati, who presented the study at the annual meeting of the American Headache Society.
Migraines are generally categorized as episodic (0-14 headache days per month) or chronic (15 or more per month), or as with or without aura. But these broad categories fail to capture the true diversity of migraine, according to Dr. Ezzati, and this may contribute to the fact that response to migraine therapy hovers around 60%. “We feel that the key to improving therapeutic efficacy is to identify individuals who are more homogeneous, more similar to each other, so that when we give a treatment, it is specifically targeting the underlying pathophysiology that those people have,” said Dr. Ezzati, who is an associate professor of neurology and director of the neuroinformatics program at University of California, Irvine.
The analysis revealed some clinically interesting results, said Dr. Ezzati. “For example, allodynia is a symptom that is not particularly used for classification of different types of migraine. There was a specific group that was very high in allodynia, and they were not very responsive to treatments, so that might be a [group] that people have to focus on. Also, we talk a lot about comorbidities in migraine, but we don’t talk about how these comorbidities affect the therapeutic strategies and treatment response to specific medications. We showed that people who have depression are actually less responsive than other groups to treatments, especially prescription medications,” he said.
Machine learning reveals clusters
The researchers analyzed data from 4,423 patients drawn from the American Migraine Prevalence and Prevention Study, which was conducted every year between 2005 and 2009. They included adult patients who filled out surveys in both 2006 and 2007. The study population was 83.7% female and had a mean age of 46.8 years, and 6.4% had chronic migraine. The researchers then used a machine-learning based self-organizing map to group patients into similar clusters.
The algorithm produced five such groups: Cluster 1 had the lowest symptom severity, and 0.6% had chronic migraine. Cluster 2 had mild symptom severity with no chronic migraine. Cluster 3 had moderate symptom severity and a high prevalence of allodynia (88.5%, vs. 63.4% overall, P < .001) and no chronic migraine. Cluster 4 had a high frequency of depressive symptoms (63.1% vs. 19.8% overall, P < .001) and 5.2% had chronic migraine. Cluster 5 had frequent and severe migraines, and most (83.0%) had chronic migraine (P < .001).
There were some other broader trends. Triptans were more commonly used in clusters 2 (25.6%), 3 (27.9%), and 5 (28.0%), but less so in cluster 4 (17.1%; P < .001). Pain freedom at 2 hours was most common in cluster 1 (53.1%), followed by cluster 2 (46.4%), but was significantly less frequent in clusters 3 (32.2%), 4 (32.2%), and 5 (34.7%; P < .001).
Therapeutic implications
Dr. Ezzati believes that machine learning and data analysis could point the way to a future of more tailored migraine therapies. “I think we have to in general go down the path of using more evidence and more data to inform us about individualized planning for patients. For that we need larger clinical studies and larger epidemiological studies to help us identify more homogeneous subtypes of patients that we can eventually target in clinical trials,” he said.
Catherine Chong, MD, who chaired the session where the research was presented, praised the study in an interview. “Episodic migraine and chronic migraine have been developed [as categories] by headache frequency per month, and it was basically based on consensus in committee. They made basically a determination that 15 and under migraine days would be episodic migraine and over would be chronic migraine. So they dichotomized migraine, in a way, based on what people thought in the field. Looking at the data freely, and letting the algorithm determine the different subtypes, and putting everybody with migraine in it, and having these groups naturally appear from the data, I think is fascinating,” Dr. Chong said.
She echoed Dr. Ezzati’s call for further research that could create even more subgroups. “Is it really truly the case that somebody with less than 15 migraine days [per month], that 14 migraines days would be so different than somebody with 15 or over, or 8? I think we need to look at it further to see whether there are additional subgroups within that data. I think there are probably more [groups identifiable] from different data that we have out there,” said Dr. Chong.
Dr. Ezzati has consulted for or been a reviewer or advisory board member for Corium, Eisai, GlaxoSmithKline, Mint Research, and Health Care Horizon Scanning System. He has received research funding from Amgen. Dr. Chong has no relevant financial disclosures.
FROM AHS 2023