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

Authors

Elizabeth Hunter, Technological University DublinFollow
John D. Kelleher, Technological University DublinFollow

Author ORCID Identifier

https://orcid.org/ 0000-0002-1767-4744

Document Type

Article

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE, 3. MEDICAL AND HEALTH SCIENCES

Publication Details

Frontiers in Neurology: Stroke

Abstract

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.

DOI

https://doi.org/10.3389/fneur.2022.803749

Recommended Citation

Hunter E and Kelleher JD (2022) Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke Risk. Front. Neurol. 13:803749. doi: 10.3389/fneur.2022.803749

Funder

e EU’s Horizon 2020 research and innovation programme under grant agreement No. 777107 and the ADAPT Centre for Digital Content Technology funded under the SFI Research Centres Programme (Grant 13/RC/2106_P2) and co-funded under the European Regional Development Funds

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.