左心房全自动、基于 3D 的功能分析与房颤分类方案之间的相关性。
Associations between fully-automated, 3D-based functional analysis of the left atrium and classification schemes in atrial fibrillation.
机构信息
Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland.
Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
出版信息
PLoS One. 2022 Aug 15;17(8):e0272011. doi: 10.1371/journal.pone.0272011. eCollection 2022.
BACKGROUND
Atrial fibrillation (AF) has been linked to left atrial (LA) enlargement. Whereas most studies focused on 2D-based estimation of static LA volume (LAV), we used a fully-automatic convolutional neural network (CNN) for time-resolved (CINE) volumetry of the whole LA on cardiac MRI (cMRI). Aim was to investigate associations between functional parameters from fully-automated, 3D-based analysis of the LA and current classification schemes in AF.
METHODS
We retrospectively analyzed consecutive AF patients who underwent cMRI on 1.5T systems including a stack of oblique-axial CINE series covering the whole LA. The LA was automatically segmented by a validated CNN. In the resulting volume-time curves, maximum, minimum and LAV before atrial contraction were automatically identified. Active, passive and total LA emptying fractions (LAEF) were calculated and compared to clinical classifications (AF Burden score (AFBS), increased stroke risk (CHA2DS2VASc≥2), AF type (paroxysmal/persistent), EHRA score, and AF risk factors). Moreover, multivariable linear regression models (mLRM) were used to identify associations with AF risk factors.
RESULTS
Overall, 102 patients (age 61±9 years, 17% female) were analyzed. Active LAEF (LAEF_active) decreased significantly with an increase of AFBS (minimal: 44.0%, mild: 36.2%, moderate: 31.7%, severe: 20.8%, p<0.003) which was primarily caused by an increase of minimum LAV. Likewise, LAEF_active was lower in patients with increased stroke risk (30.7% vs. 38.9%, p = 0.002). AF type and EHRA score did not show significant differences between groups. In mLRM, a decrease of LAEF_active was associated with higher age (per year: -0.3%, p = 0.02), higher AFBS (per category: -4.2%, p<0.03) and heart failure (-12.1%, p<0.04).
CONCLUSIONS
Fully-automatic morphometry of the whole LA derived from cMRI showed significant relationships between LAEF_active with increased stroke risk and severity of AFBS. Furthermore, higher age, higher AFBS and presence of heart failure were independent predictors of reduced LAEF_active, indicating its potential usefulness as an imaging biomarker.
背景
心房颤动（AF）与左心房（LA）扩大有关。虽然大多数研究都集中在基于二维的静态 LA 容积（LAV）估计上，但我们使用完全自动的卷积神经网络（CNN）对心脏 MRI（cMRI）上的整个 LA 进行时间分辨（CINE）容积测量。目的是研究基于 LA 的全自动 3D 分析的功能参数与 AF 现行分类方案之间的相关性。
方法
我们回顾性分析了连续接受 1.5T 系统 cMRI 的 AF 患者，该系统包括一组覆盖整个 LA 的斜轴 CINE 序列。通过验证的 CNN 自动分割 LA。在得到的容积时间曲线中，自动识别最大、最小和收缩前的 LAV。计算主动、被动和总 LA 排空分数（LAEF），并与临床分类（AF 负担评分（AFBS）、增加的中风风险（CHA2DS2VASc≥2）、AF 类型（阵发性/持续性）、EHRA 评分和 AF 危险因素）进行比较。此外，还使用多变量线性回归模型（mLRM）来识别与 AF 危险因素的关联。
结果
共分析了 102 例患者（年龄 61±9 岁，17%为女性）。随着 AFBS 的增加，主动 LAEF（LAEF_active）显著降低（轻度：44.0%，中度：36.2%，中度：31.7%，重度：20.8%，p<0.003），这主要是由于最小 LAV 的增加所致。同样，具有较高中风风险的患者 LAEF_active 较低（30.7%比 38.9%，p = 0.002）。AF 类型和 EHRA 评分在各组之间无显著差异。在 mLRM 中，LAEF_active 随年龄（每年：-0.3%，p = 0.02）、AFBS （每类：-4.2%，p<0.03）和心力衰竭（-12.1%，p<0.04）的增加而降低。
结论
从 cMRI 获得的整个 LA 的全自动形态测量学显示，LAEF_active 与增加的中风风险和 AFBS 的严重程度之间存在显著关系。此外，较高的年龄、较高的 AFBS 和心力衰竭的存在是 LAEF_active 降低的独立预测因子，表明其作为成像生物标志物的潜在有用性。
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