Suppr超能文献

用于解释疾病严重程度的脑容量多发性硬化症特异性参考曲线。

Multiple Sclerosis-Specific Reference Curves for Brain Volumes to Explain Disease Severity.

作者信息

van Nederpelt David Rudolf, Bos Lonneke, Mattiesing Rozemarijn M, Strijbis Eva M M, Moraal Bastiaan, Kuijer Joost, Hoogland Jeroen, Mutsaerts Henk J M M, Uitdehaag Bernard, Killestein Joep, Heine Lizette, Jasperse Bas, Barkhof Frederik, Schoonheim Menno M, Vrenken Hugo

机构信息

MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands.

MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands.

出版信息

Neurology. 2025 May 27;104(10):e213618. doi: 10.1212/WNL.0000000000213618. Epub 2025 Apr 23.

Abstract

BACKGROUND AND OBJECTIVES

Brain atrophy is relevant for understanding disease progression and treatment response in people with multiple sclerosis (pwMS). Automatic brain volume-reporting tools often rely on healthy control (HC) reference curves to interpret brain volumes, whereas brain volume loss is different in pwMS. This observational study aimed to develop an MS-specific reference model for brain volumes and evaluate its performance compared with HC-based curves, as a proof-of-concept.

METHODS

Participants, pwMS and HCs, from the Amsterdam MS cohort were included based on the availability of T1-weighted MR scans. Normalized brain volumes (NBVs) were obtained using commercially available software. The software program also provides NBV percentiles, based on age-specific and sex-specific HC curves, grouped into NBV quartiles, describing deviation from expected NBVs. Disease severity was determined with the MS severity score (MSSS), Symbol Digit Modalities Test (SDMT), and 9-Hole Peg Test (9HPT). An MS-specific model was developed by regressing NBVs against age, sex, disease duration, and MS phenotype. The resulting MS model was also used to classify pwMS into quartiles describing deviation from expected NBV, given the modeled patient characteristics, with leave-one-out predictions. Quartile classification from HC-based and MS-based reference curves was compared with MSSS using analysis of variance (ANOVA).

RESULTS

Regressions for NBVs from 713 pwMS and 259 HCs (mean age: 49.1 ± 9.7 and 48.3 ± 10.1, %female: 70.4% and 67.2%, respectively) were significant for age, sex, disease duration, and phenotype, which were included in the MS-specific model. MS-specific model quartile designations significantly improved associations with MSSS values ( = 2.210, = 0.06) compared with HC-based quartiles. MSSS values worsened with lower NBV quartiles in the MS-specific model (difference between quartiles 1-4 = -0.84, = 6.110, 95% CI [-1.5 to -0.18])), which was not observed for HC-based quartiles ( = 0.98). Quartile group differences were observed for 9HPT (MS: = 3.510, = 0.02, HC: = 6.610, = 0.02) and SDMT (MS: = 3.110, = 0.05, HC: = 5.410, = 0.04) values, but MS-specific quartiles again improved quartile associations ( = 0.036, = 0.01 and = 0.02, = 0.01, respectively).

DISCUSSION

NBV values derived from an MS-specific reference model offer improved relevance for assessing disease severity compared with curves derived from age-specific and sex-specific HC reference models. Improving the model toward application in individual people could enhance clinical implementation.

摘要

背景与目的

脑萎缩与理解多发性硬化症患者(pwMS)的疾病进展和治疗反应相关。自动脑容量报告工具通常依靠健康对照(HC)参考曲线来解释脑容量,而pwMS患者的脑容量损失情况有所不同。本观察性研究旨在开发一种针对MS的脑容量参考模型,并与基于HC的曲线相比评估其性能,作为概念验证。

方法

根据T1加权磁共振扫描的可用性,纳入来自阿姆斯特丹MS队列的参与者,即pwMS和HC。使用商业软件获得标准化脑容量(NBV)。该软件程序还基于特定年龄和性别的HC曲线提供NBV百分位数,分为NBV四分位数,描述与预期NBV的偏差。通过MS严重程度评分(MSSS)、符号数字模态测试(SDMT)和9孔插钉测试(9HPT)确定疾病严重程度。通过将NBV与年龄、性别、疾病持续时间和MS表型进行回归分析,开发了一种针对MS的模型。所得的MS模型还用于根据建模的患者特征,通过留一法预测将pwMS分类为描述与预期NBV偏差的四分位数。使用方差分析(ANOVA)将基于HC和基于MS的参考曲线的四分位数分类与MSSS进行比较。

结果

对713例pwMS和259例HC(平均年龄分别为49.1±9.7和48.3±10.1,女性比例分别为70.4%和67.2%)的NBV进行回归分析,结果显示年龄、性别、疾病持续时间和表型具有显著意义,这些因素均纳入了针对MS的模型。与基于HC的四分位数相比,基于MS的模型四分位数指定显著改善了与MSSS值的相关性(F = 2.2×10,P = 0.06)。在基于MS的模型中,随着NBV四分位数降低,MSSS值恶化(四分位数1 - 4之间的差异 = -0.8',P = 6.1×10,95%置信区间[-1.5至 -0.18]),而基于HC的四分位数未观察到这种情况(P = 0.98)。在9HPT(MS:F = 3.5×10,P = 0.02,HC:F = 6.6×10,P = 0.02)和SDMT(MS:F = 3.1×10,P = 0.05,HC:F = 5.4×10,P = 0.04)值方面观察到四分位数组差异,但基于MS的四分位数再次改善了四分位数相关性(分别为P = 0.036,P = 0.01和P = 0.02,P = 0.01)。

讨论

与从特定年龄和性别的HC参考模型得出的曲线相比,从针对MS的参考模型得出的NBV值在评估疾病严重程度方面具有更高的相关性。将模型改进以应用于个体患者可加强临床应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c72b/12012623/63b66c09c5df/WNL-2024-104325f1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验