Hunter Elizabeth, Kelleher John D
PRECISE4Q Predictive Modelling in Stroke, Technological University Dublin, Dublin, Ireland.
ADAPT Research Centre, Technological University Dublin, Dublin, Ireland.
Front Neurol. 2022 Feb 17;13:803749. doi: 10.3389/fneur.2022.803749. eCollection 2022.
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.
在中风风险预测方面，年龄是最重要的风险因素之一。然而，将年龄作为中风预测模型中的一个风险因素会引发诸多困难。年龄常常主导风险评分，而且并非所有风险因素对中风风险的贡献都随年龄呈比例变化。在本研究中，我们利用美国国立心肺血液研究所生物标本和数据储存库信息协调中心的弗雷明汉心脏研究数据，对一些常见的中风风险因素进行调查，以确定它们是否随年龄对中风风险评分做出成比例的贡献。由于我们发现有证据表明存在年龄方面的不成比例现象，于是我们创建了一组逻辑回归风险模型，每个模型预测不同年龄组的5年中风风险。与将年龄作为风险因素的所有年龄通用模型相比，年龄组模型显示出更好的校准效果。这表明，为了更好地预测中风风险，可能有必要考虑在中风风险预测模型中纳入年龄的替代方法，这些方法要考虑到随着年龄变化其他风险因素的不成比例性。
