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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Translational Neuroscience
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1496810

A machine learning-based predictive model for predicting early neurological deterioration in lenticulostriate atheromatous diseaserelated infarction

Provisionally accepted
Zhuangzhuang Jiang Zhuangzhuang Jiang Dongjuan Xu Dongjuan Xu *Hongfei Li Hongfei Li Xiaolan Wu Xiaolan Wu YUAN FANG YUAN FANG Chen Lou Chen Lou
  • Department of Neurology, Dongyang People's Hospital, Affiliated to Wenzhou Medical University, Dongyang, Zhejiang, China

The final, formatted version of the article will be published soon.

    Background and aim: This study aimed to develop a predictive model for early neurological deterioration (END) in branch atheromatous disease (BAD) affecting the lenticulostriate artery (LSA) territory using machine learning. Additionally, it aimed to explore the underlying mechanisms of END occurrence in this context.We conducted a retrospective analysis of consecutive ischemic stroke patients with BAD in the LSA territory admitted to Dongyang People's Hospital from January 1, 2018, to September 30, 2023. Significant predictors were identified using LASSO regression, and nine machine learning algorithms were employed to construct models. The logistic regression model demonstrated superior performance and was selected for further analysis.Results: A total of 380 patients were included, with 268 in the training set and 112 in the validation set. Logistic regression identified stroke history, systolic pressure, conglomerated beads sign, middle cerebral artery (MCA) shape, and parent artery stenosis as significant predictors of END. The developed nomogram exhibited good discriminative ability and calibration. Additionally, the decision curve analysis indicated the practical clinical utility of the nomogram.The novel nomogram incorporating systolic pressure, stroke history, conglomerated beads sign, parent artery stenosis, and MCA shape provides a practical tool for assessing the risk of early neurological deterioration in BAD affecting the LSA territory. This model enhances clinical decision-making and personalized treatment strategies.

    Keywords: Early neurological deterioration, Branch atheromatous disease, Lenticulostriate artery, ischemic stroke, machine learning

    Received: 15 Sep 2024; Accepted: 28 Nov 2024.

    Copyright: © 2024 Jiang, Xu, Li, Wu, FANG and Lou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dongjuan Xu, Department of Neurology, Dongyang People's Hospital, Affiliated to Wenzhou Medical University, Dongyang, Zhejiang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.