Predicting White Matter Hyperintensity: Leveraging Portable MRI for Accessible Brain Health Screening.

Ian P Johnson, Hailey Brigger, Joel Smith, Emma Peasley, Alison Champagne, Lauren Littig, Dheeraj Lalwani, Gordon Sze, Seyedmehdi Payabvash, Basmah Safdar, Gail D'Onofrio, Charles Wira, Juan Eugenio Iglesias, Matthew S Rosen, Annabel Sorby-Adams, W Taylor Kimberly, Kevin N Sheth, Adam de Havenon
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Abstract

Background and purpose: Portable MRI (pMRI) has emerged as a cost-effective and accessible tool for the identification of white matter hyperintensities (WMH), an independent risk factor for stroke and dementia. Our objective was to confirm that pMRI can produce accurate WMH measurements and to develop and validate a risk model to predict WMH on pMRI for the purpose of identifying patients who may benefit from pMRI screening.

Materials and methods: The development (n = 143) and validation (n = 127) cohorts included patients without acute neurologic pathology who received a pMRI at a tertiary care hospital between May 2020 and July 2024. The development cohort included pMRIs collected as part of a prospective WMH screening pilot program in the emergency department. The validation cohort was a retrospective collection of pMRIs obtained for separate research purposes. Conventional MRIs (cMRIs) in the validation cohort obtained within 3 months of pMRIs were used for additional validation and device agreement. The primary outcome was WMH burden greater than 10 mL, assessed via an axial T2-FLAIR sequence acquired on a 0.064T pMRI and quantified by using a WMH segmentation software developed to process sequences of any resolution. We used backwards selection to screen candidate variables and report the area under the curve of the resulting model.

Results: The final model, which included age, systolic blood pressure >140, atrial fibrillation, and tobacco use, achieved an area under the curve (AUC) of 0.83 (95% CI, 0.75-0.90) in the development cohort (n = 143, 62.4 ± 12.6 years, 44% female, 36% nonwhite race) and 0.85 (95% CI, 0.77-0.92) in the validation cohort (n = 127, 65.2 ± 16.8 years, 51% female, 34% nonwhite race), with similar results by using WMH measurements derived from cMRI (n = 120, P = .98, AUC = 0.86, 95% CI, 0.77-0.93). Additionally, we confirmed agreement in WMH volumes between pMRI and cMRI (n = 120, r = 0.93, 95% CI, 0.90-0.95, P < .001).

Conclusions: The WMH risk score demonstrated accurate performance and reproducibility across cohorts, supporting its potential as a screening tool for identifying patients at risk of moderate WMH burden. Appropriately targeted pMRI screening in high-risk individuals could allow providers and patients to proactively manage vascular risk factors and improve neurologic outcomes.

预测白质高强度:利用便携式磁共振成像进行无障碍脑健康筛查。
背景和目的:便携式核磁共振成像(pMRI)已成为一种具有成本效益和可获得的工具,用于识别白质高强度(WMH),这是中风和痴呆的独立危险因素。我们的目的是确认pMRI可以产生准确的WMH测量,并开发和验证一个风险模型来预测pMRI上的WMH,目的是确定可能从pMRI筛查中受益的患者。材料和方法:开发队列(N=143)和验证队列(N=127)包括2020年5月至2024年7月在三级医院接受pMRI检查的无急性神经病理学患者。发展队列包括作为急诊科前瞻性WMH筛查试点项目的一部分收集的pmri。验证队列是为不同的研究目的而获得的pmri回顾性收集。在pmri后3个月内获得的验证队列中的常规mri (cmri)用于额外的验证和设备协议。主要结果是WMH负荷大于10 mL,通过在0.064 T pMRI上获得的轴向T2-FLAIR序列进行评估,并使用开发的用于处理任何分辨率序列的WMH分割软件进行量化。我们使用反向选择来筛选候选变量,并报告结果模型曲线下的面积。结果:最终模型包括年龄、收缩压bbb140、心房颤动和吸烟,在开发队列(N=143, 62.4±12.6岁,44%女性,36%非白种人)和验证队列(N=127, 65.2±16.8岁,51%女性,34%非白种人)的AUC为0.83 (95% CI 0.75-0.90),使用cMRI获得的WMH测量结果相似(N=120, p=0.98, AUC=0.86, 95% CI 0.77-0.93)。此外,我们证实了pMRI和cMRI在WMH体积上的一致性(N=120, r=0.93, 95% CI 0.90-0.95)。结论:WMH风险评分在各队列中表现出准确的性能和可重复性,支持其作为识别有重大WMH负担风险患者的筛查工具的潜力。在高危人群中进行适当针对性的pMRI筛查可以使提供者和患者主动管理血管危险因素并改善神经系统预后。缩写:pMRI =便携式磁共振成像;常规磁共振成像;白质高强度;高血压= HTN;糖尿病= DM;心房颤动;收缩压= SBP;高脂血症;曲线下面积= AUC;受试者工作特征= ROC。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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