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

Elizabeth Hunter,John D Kelleher

doi:10.3389/fneur.2022.803749

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.

Highlights

IntroductionIn the US the annual direct medical costs for stroke in 2012 was 71.55 billion US dollars [1]
Stroke is one of the leading causes of mortality
To answer these questions we investigate if there is a difference in risk factor contribution to stroke risk by age to short term stroke risk, and to account for any difference we propose creating a stroke risk prediction model that is made up of four separate models, each covering a different age group

Summary

Introduction

In the US the annual direct medical costs for stroke in 2012 was 71.55 billion US dollars [1] This cost, only takes into account the direct costs and does not consider years lost to work or other societal burdens that are caused by stroke, so the actual cost is much higher. One of the methods used for stroke prevention is to identify patients with a high risk of having a stroke based off of certain risk factors. This identification is often done through risk prediction models, such as SCORE [2] and the Framingham heart study model [3, 4]. Once identified a patient can be advised to adjust their behaviors or be treated with appropriate medications to lower their risk of having a stroke

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