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CMR-FT量化的左心室应变对高血压患者脑白质高信号的诊断价值
Diagnostic Value of CMR-FT Quantified Left Ventricular Strain in Patients with Hypertension for Cerebral White Matter Hyperintensities

DOI: 10.12677/acm.2025.1541156, PP. 2076-2086

Keywords: 心脏磁共振特征跟踪技术,左室,心肌应变
Cardiac Magnetic Resonance Feature Tracking Technology
, Left Ventricle, Myocardial Strain

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Abstract:

目的:基于磁共振特征追踪(CMR-FT)技术在高血压患者中探讨左心室应变对血管源性脑白质高信号(WMH)的诊断价值。方法:选取2021年1月~2023年12月于本院就诊的高血压患者共159例。进行头颅磁共振及心脏磁共振(CMR)检查,根据头颅磁共振影像及改良Fazekas分级将患者分为对照组、轻度组、中重度组。收集所有患者的临床基线资料和左心CMR参数。结果:(1) 相对于对照组与轻度组,中重度组年龄、Hcy、SBP水平升高,而GCS、GLS、GCPDSR、GLPDSR降低(P > 0.05);对照组与轻度组相比,各指标间差异无统计学意义(P > 0.05)。(2) 行二元Logistic回归分析得出,年龄(OR = 1.103, 95%CI: 1.104~1.170, P = 0.001)、Hcy (OR = 1.115, 95%CI: 1.015~1.225, P = 0.023)、SBP (OR = 1.025, 95%CI: 1.002~1.049, P = 0.036)、GCS (OR = 1.384, 95%CI: 1.163~1.648, P < 0.001)、GLS (OR = 1.149, 95%CI: 1.031~1.281, P = 0.012)是高血压伴中重度WMH的独立危险因素。(3) ROC分析显示:单因素模型对高血压伴中重度WMH的诊断效能最大的为GLS (AUC = 0.762, 95%CI: 0.688~0.826, P < 0.001);多因素联合模型对高血压伴中重度WMH的诊断效能最好的为模型四:年龄 + Hcy + SBP + GCS + GLS (AUC = 0.895, 95%CI: 0.836~0.938, P < 0.001)。结论:年龄、Hcy、SBP、GCS、GLS是高血压伴中重度WMH的独立危险因素。基于年龄、Hcy、SBP结合心肌应变构建的模型对高血压伴中重度WMH具有较高的特异性和敏感性,有助于优化高血压患者的治疗与管理。
Objective: To explore the diagnostic value of left ventricular strain for vascular leukoaraiosis (WMH) in hypertensive patients based on cardiovascular magnetic resonance feature tracking (CMR-FT) technology. Methods: A total of 159 hypertensive patients who visited our hospital from January 2021 to December 2023 were selected. Cranial magnetic resonance and cardiac magnetic resonance (CMR) examinations were performed, and patients were divided into control group, mild group, and moderate to severe group based on cranial magnetic resonance images and modified Fazekas grading. Clinical baseline data and left ventricular CMR parameters of all patients were collected. Results: (1) Compared with the control group and the mild group, the moderate to severe group had increased age, Hcy, and SBP levels, while GCS, GLS, GCPDSR, and GLPDSR decreased (P > 0.05); there was no statistically significant difference in indicators between the control group and the mild group (P > 0.05). (2) Binary Logistic regression analysis showed that age (OR = 1.103, 95%CI: 1.104~1.170, P = 0.001), Hcy (OR = 1.115, 95%CI: 1.015~1.225, P = 0.023), SBP (OR = 1.025, 95%CI: 1.002~1.049, P = 0.036), GCS (OR = 1.384, 95%CI: 1.163~1.648, P < 0.001), and GLS (OR = 1.149, 95%CI: 1.031~1.281, P = 0.012) were independent risk factors for hypertension with moderate to severe WMH. (3) ROC analysis showed that the diagnostic efficacy of the single-factor model for hypertension with moderate to severe WMH was the highest for GLS (AUC = 0.762, 95%CI: 0.688~0.826, P < 0.001); the diagnostic efficacy of the multifactorial combined

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