Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios

Herrgårdh, Tilda; Madai, Vince I.; Kelleher, John D.; Magnusson, Rasmus; Gustafsson, Mika; Milani, Lili; Gennemark, Peter; Cedersund, Gunnar

doi:10.1016/j.nicl.2021.102694

Cited by 12 publications

(15 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The age models allow us to look at risk factors and their changing contribution and difference in importance by age. Although stroke is often seen as a health event, there are a number of processes in the etiology that have lead up to that event ( 20 ). Identifying the risk factors and interactions between risk factors that change in importance between age groups cannot only provide us with a better short term risk prediction but may help to identify those who might have a high long term risk despite a low short term risk because of the impact of a risk factor over a long period of time.…”

Section: Discussionmentioning

confidence: 99%

Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke Risk

Hunter¹,

Kelleher²

2022

Front. Neurol.

Self Cite

View full text Add to dashboard Cite

Age is one of the most important risk factors when it comes to stroke risk prediction. However, including age as a risk factor in a stroke prediction model can give rise to a number of difficulties. Age often dominates the risk score, and also not all risk factors contribute proportionally to stroke risk by age. In this study we investigate a number of common stroke risk factors, using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if they appear to contribute proportionally by age to a stroke risk score. As we find evidence that there is some non-proportionality by age, we then create a set of logistic regression risk models that each predict the 5 year stroke risk for a different age group. The age group models are shown to be better calibrated when compared to a model for all ages that includes age as a risk factor. This suggests that to get better predictions for stroke risk it may be necessary to consider alternative methods for including age in stroke risk prediction models that account for the non-proportionality of the other risk factors as age changes.

show abstract

Section: Discussionmentioning

confidence: 99%

Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke Risk

Hunter¹,

Kelleher²

2022

Front. Neurol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…be a polygenic risk score model, which takes all the genes and calculate a smaller set of risk factors, which then goes into the ultimate risk model. These different future possibilities are reviewed for the context of stroke in (10).…”

Section: Discussionmentioning

confidence: 99%

“…Since the two modelling approaches have complementary abilities, a combination of the two has sometimes been proposed (10,(16)(17)(18)(19), even though few examples exist. Such combinations have for instance been used to improve MRI classification (20)(21)(22), for predicting tumor growth and density (23), and for predicting phenotypes based on genotypes (24).…”

Section: C) the Person Participates In His/her Own Care By Choosing W...mentioning

confidence: 99%

“…These diseases are at least in part preventable with life-style changes and early medical interventions, and have a complex pathophysiology. The complexity of cardiovascular diseases comprises several medical and environmental factors, and involves multiple organs, timescales, and control mechanisms (10). This complexity makes it difficult to make individual predictions without help from advanced analytical tools such as digital twins.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital twins and hybrid modelling for simulation of physiological variables and stroke risk

Herrgårdh

Hunter²,

Tunedal

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

One of the more interesting ideas for achieving personalized, preventive, and participatory medicine is the concept of a digital twin. A digital twin is a personalized computer model of a patient. So far, digital twins have been constructed using either mechanistic models, which can simulate the trajectory of physiological and biochemical processes in a person, or using machine learning models, which for example can be used to estimate the risk of having a stroke given a cross-section profile at a given timepoint. These two modelling approaches have complementary strengths which can be combined into a hybrid model. However, even though hybrid modelling combining mechanistic modelling and machine learning have been proposed, there are few, if any, real examples of hybrid digital twins available. We now present such a hybrid model for the simulation of ischemic stroke. On the mechanistic side, we develop a new model for blood pressure and integrate this with an existing multi-level and multi-timescale model for the development of type 2 diabetes. This mechanistic model can simulate the evolution of known physiological risk factors (such as weight, diabetes development, and blood pressure) through time, under different intervention scenarios, involving a change in diet, exercise, and certain medications. These forecast trajectories of the physiological risk factors are then used by a machine learning model to calculate the 5-year risk of stroke, which thus also can be calculated for each timepoint in the simulated scenarios. We discuss and illustrate practical issues with clinical implementation, such as data gathering and harmonization. By improving patients' understanding of their body and health, the digital twin can serve as a valuable tool for patient education and as a conversation aid during the clinical encounter. As such, it can facilitate shared decision-making, promote behavior change towards a healthy lifestyle, and improve adherence to prescribed medications.

show abstract

“…For instance, the use of the hybrid modeling approach has been shown to support short-term decision-making in emergency health care by helping emergency service facilities avoid overcrowding [ 6 ]. In the case of stroke care and within the context of precision medicine for stroke, a hybrid modeling approach has been shown to be the most beneficial [ 7 ]. The application of hybrid modeling has also been observed in the field of cardiovascular diseases; hybrid modeling approaches have been used to assess integrated care for challenging and complex diseases to offer comprehensive tools for decision-making [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing Patient Engagement in Health Care: Proposal for a Modeling and Simulation Framework for Behavioral Analysis

Alwasel¹,

Stergioulas²,

Fakhimi³

et al. 2021

JMIR Res Protoc

View full text Add to dashboard Cite

show abstract

Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios

Cited by 12 publications

References 92 publications

Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke Risk

Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke Risk

Digital twins and hybrid modelling for simulation of physiological variables and stroke risk

Assessing Patient Engagement in Health Care: Proposal for a Modeling and Simulation Framework for Behavioral Analysis

Contact Info

Product

Resources

About