Researchers at the Science Foundation Ireland Adapt centre claim to have created a new prediction model that can identify people under the age of 60 with a high risk of ischemic stroke, who may not be identified using traditional methods.
Ischemic stroke occurs when a blood clot blocks the blood flow in an artery in the brain, which causes an interruption of oxygen supply to the brain tissue. It is believed to be one of the leading causes of mortality worldwide. A 2020 study published in Oxford Academic said the annual cost of strokes in the UK was around £26bn.
The researchers from the Adapt research centre for AI-driven digital content technology at Technological University Dublin said there are people under the age of 60 who could be at risk of stroke, but may not be properly identified using traditional stroke prediction models.
“Age is a critical risk factor in stroke prediction,” Prof John D Kelleher said. “However, unless we are careful in terms of how age is used to predict stroke risk, then age can dominate the information that comes from other risk factors.
“Another aspect that we need to consider in predicting stroke risk is that not all risk factors contribute proportionally to stroke risk by age.”
In their study published in Frontiers of Neurology, the team said they created a set of models that can predict the individual risk of stroke by age group. Their research looked at whether the contribution of stroke risk factors to an individual’s five-year stroke risk is non-proportional by age.
“This research shows that by creating different risk models for different age groups, we are better able to model the contribution from multiple factors to stroke risk that is appropriate for each age category,” Kelleher added.
One of the examples listed in the study is the fact that previous studies show that younger women have a lower short-term risk of a stroke than men, but this switches as women get older.
“Age is a key factor in an individual’s stroke risk with the probability of having a stroke increasing with age,” the researchers said in the study. “However, there are other risk factors that are important in determining the risk of stroke by age and if age is considered as a risk factor in a model, we have shown that the model may neglect the contribution of these other factors.”
The team believes their research will help identify people at highest risk of ischemic stroke, in order to mitigate those risks and to positively impact patient care. They said their novel platform could impact millions of high-risk people.
The research is part of the Horizon 2020 research project Precise4Q, which looks to create predictive simulation computer models, to enable personalised stroke treatment.
Leigh Mc Gowran
This article originally appeared on www.siliconrepublic.com and can be found at: https://www.siliconrepublic.com/innovation/sfi-adapt-research-stroke-prediction-young
Ischemic stroke occurs when a blood clot blocks the blood flow in an artery in the brain, which causes an interruption of oxygen supply to the brain tissue. It is believed to be one of the leading causes of mortality worldwide. A 2020 study published in Oxford Academic said the annual cost of strokes in the UK was around £26bn.
The researchers from the Adapt research centre for AI-driven digital content technology at Technological University Dublin said there are people under the age of 60 who could be at risk of stroke, but may not be properly identified using traditional stroke prediction models.
“Age is a critical risk factor in stroke prediction,” Prof John D Kelleher said. “However, unless we are careful in terms of how age is used to predict stroke risk, then age can dominate the information that comes from other risk factors.
“Another aspect that we need to consider in predicting stroke risk is that not all risk factors contribute proportionally to stroke risk by age.”
In their study published in Frontiers of Neurology, the team said they created a set of models that can predict the individual risk of stroke by age group. Their research looked at whether the contribution of stroke risk factors to an individual’s five-year stroke risk is non-proportional by age.
“This research shows that by creating different risk models for different age groups, we are better able to model the contribution from multiple factors to stroke risk that is appropriate for each age category,” Kelleher added.
One of the examples listed in the study is the fact that previous studies show that younger women have a lower short-term risk of a stroke than men, but this switches as women get older.
“Age is a key factor in an individual’s stroke risk with the probability of having a stroke increasing with age,” the researchers said in the study. “However, there are other risk factors that are important in determining the risk of stroke by age and if age is considered as a risk factor in a model, we have shown that the model may neglect the contribution of these other factors.”
The team believes their research will help identify people at highest risk of ischemic stroke, in order to mitigate those risks and to positively impact patient care. They said their novel platform could impact millions of high-risk people.
The research is part of the Horizon 2020 research project Precise4Q, which looks to create predictive simulation computer models, to enable personalised stroke treatment.
Leigh Mc Gowran
This article originally appeared on www.siliconrepublic.com and can be found at: https://www.siliconrepublic.com/innovation/sfi-adapt-research-stroke-prediction-young
