Leonardo Roever: Atherofrailty Score Offers a A Novel Machine Learning Approach to Stroke Risk Stratification
Leonardo Roever, CEO of Brazilian Evidence-Based Health Network, shared on LinkedIn about a recent article by Jingxian Sun et al, published in Lipids in Health and Disease:
”Integration of Frailty and Cumulative Lipid Burden for Stroke Risk Stratification: A Machine Learning Approach
Traditional lipid indices, such as the Cumulative Plasma Atherogenic Index (cumAIP), demonstrate inconsistent predictive performance for stroke in older adults, likely due to the modifying effects of complex medication regimens.
This study developed a novel Atherofrailty Score (AFS) that effectively addresses the predictive limitations of traditional metabolic markers in medicated older adults.
By integrating metabolic burden with systemic vulnerability, this new composite score offers a robust linear predictive approach for stroke, supporting a multidimensional approach to vascular risk stratification in older populations.”
Title: Integrating frailty and cumulative lipid burden for stroke risk stratification: a machine learning–guided Athero-Frailty Score from the CHARLS cohort
Authors: Jingxian Sun, Daikang Xu, Guangyu Du, Tongyu Jia, Xing Han, Yi Yu, Jianpeng Wang, Zhiyong Yan, Shifang Li, Chao Wang, Shusheng Che
Read the Full Article on Lipids in Health and Disease
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