Prediction of stroke severity: systematic evaluation of lesion representations.

IF 4.4 2区 医学 Q1 CLINICAL NEUROLOGY Annals of Clinical and Translational Neurology Pub Date : 2024-10-11 DOI:10.1002/acn3.52215
Anna K Bonkhoff, Alexander L Cohen, William Drew, Michael A Ferguson, Aaliya Hussain, Christopher Lin, Frederic L W V J Schaper, Anthony Bourached, Anne-Katrin Giese, Lara C Oliveira, Robert W Regenhardt, Markus D Schirmer, Christina Jern, Arne G Lindgren, Jane Maguire, Ona Wu, Sahar Zafar, John Y Rhee, Eyal Y Kimchi, Maurizio Corbetta, Natalia S Rost, Michael D Fox
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Abstract

Objective: To systematically evaluate which lesion-based imaging features and methods allow for the best statistical prediction of poststroke deficits across independent datasets.

Methods: We utilized imaging and clinical data from three independent datasets of patients experiencing acute stroke (N1 = 109, N2 = 638, N3 = 794) to statistically predict acute stroke severity (NIHSS) based on lesion volume, lesion location, and structural and functional disconnection with the lesion location using normative connectomes.

Results: We found that prediction models trained on small single-center datasets could perform well using within-dataset cross-validation, but results did not generalize to independent datasets (median R2 N1 = 0.2%). Performance across independent datasets improved using large single-center training data (R2 N2 = 15.8%) and improved further using multicenter training data (R2 N3 = 24.4%). These results were consistent across lesion attributes and prediction models. Including either structural or functional disconnection in the models outperformed prediction based on volume or location alone (P < 0.001, FDR-corrected).

Interpretation: We conclude that (1) prediction performance in independent datasets of patients with acute stroke cannot be inferred from cross-validated results within a dataset, as performance results obtained via these two methods differed consistently, (2) prediction performance can be improved by training on large and, importantly, multicenter datasets, and (3) structural and functional disconnection allow for improved prediction of acute stroke severity.

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中风严重程度的预测:病变表征的系统评估。
目的系统评估哪些基于病灶的成像特征和方法可在独立数据集上对卒中后功能障碍进行最佳统计预测:我们利用来自三个独立数据集的急性卒中患者(N1 = 109、N2 = 638、N3 = 794)的成像和临床数据,根据病变体积、病变位置以及与病变位置的结构和功能断开情况,使用常模连接组对急性卒中严重程度(NIHSS)进行统计预测:我们发现,使用数据集内交叉验证,在小型单中心数据集上训练的预测模型表现良好,但结果不能推广到独立数据集(中位数 R2 N1 = 0.2%)。使用大型单中心训练数据时,独立数据集的性能有所提高(R2 N2 = 15.8%),使用多中心训练数据时性能进一步提高(R2 N3 = 24.4%)。这些结果在不同的病变属性和预测模型中都是一致的。将结构性或功能性断开纳入模型的预测结果优于仅基于体积或位置的预测结果(P 解释:我们的结论是:(1) 急性卒中患者独立数据集的预测性能不能从数据集内的交叉验证结果推断,因为通过这两种方法得到的性能结果始终存在差异;(2) 预测性能可以通过在大型数据集(重要的是,多中心数据集)上进行训练得到改善;(3) 结构性和功能性断开可改善急性卒中严重程度的预测。
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来源期刊
Annals of Clinical and Translational Neurology
Annals of Clinical and Translational Neurology Medicine-Neurology (clinical)
CiteScore
9.10
自引率
1.90%
发文量
218
审稿时长
8 weeks
期刊介绍: Annals of Clinical and Translational Neurology is a peer-reviewed journal for rapid dissemination of high-quality research related to all areas of neurology. The journal publishes original research and scholarly reviews focused on the mechanisms and treatments of diseases of the nervous system; high-impact topics in neurologic education; and other topics of interest to the clinical neuroscience community.
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