| 孙硕,刘文佳,张浩,王明霄,鱼笑,马林.基于相位对比MRI颅内血流动力学参数预测急性高原反应[J].中国医学影像技术,2025,41(5):706~711 |
| 基于相位对比MRI颅内血流动力学参数预测急性高原反应 |
| Phase contrast MRI intracranial hemodynamic parameters for predicting acute mountain sickness |
| 投稿时间：2024-10-23 修订日期：2025-03-12 |
| DOI：10.13929/j.issn.1003-3289.2025.05.003 |
| 中文关键词: 急性高原反应 血流动力学 磁共振成像 前瞻性研究 |
| 英文关键词:acute high altitude response hemodynamics magnetic resonance imaging prospective studies |
| 基金项目:国家自然科学基金面上项目(82271922)。 |
| 作者 | 单位 | E-mail | | 孙硕 | 中国人民解放军医学院研究生院, 北京 100853
中国人民解放军总医院第一医学中心放射诊断科, 北京 100853 | | | 刘文佳 | 中国人民解放军总医院第一医学中心放射诊断科, 北京 100853 | | | 张浩 | 中国人民解放军医学院研究生院, 北京 100853
中国人民解放军总医院第一医学中心放射诊断科, 北京 100853 | | | 王明霄 | 中国人民解放军医学院研究生院, 北京 100853
中国人民解放军总医院第一医学中心放射诊断科, 北京 100853 | | | 鱼笑 | 北京京煤集团总医院影像科, 北京 102300 | | | 马林 | 中国人民解放军医学院研究生院, 北京 100853
中国人民解放军总医院第一医学中心放射诊断科, 北京 100853 | cjr.malin@vip.163.com |
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| 中文摘要: |
| 目的 观察基于相位对比(PC)MRI颅内血流动力学参数预测急性高原反应(AMS)的价值。方法 前瞻性招募72名健康青年志愿者,于平原地区采集平静呼吸及轻、中及重度瓦尔萨尔瓦动作(VM)下的颈内动脉(ICA)及颈内静脉(IJV)PC MRI并记录ICA及IJV血流动力学参数;根据急进海拔4 411 m的高原地区10 h后路易斯湖评分(LLS)结果划分AMS组(n=9)与无AMS组(n=63);采用单因素及多因素logistic回归分析筛选各状态下AMS的独立预测因素,构建单一及联合VM状态预测模型;绘制受试者工作特征曲线,计算曲线下面积(AUC),评估各模型预测效能。结果 轻度VM下ICA搏动指数(PIICA)、中度VM下IJV面积(SIJV)及重度VM下IJV阻力指数(RIIJV)均为AMS独立预测因素(P均＜0.05)。联合VM状态模型(AUC=0.869)预测AMS的效能高于单一VM状态模型(AUC=0.698~0.738)。结论 基于轻度VM PIICA、中度VM SIJV及重度VM RIIJV构建的模型可有效预测AMS。 |
| 英文摘要: |
| Objective To explore the value of phase contrast (PC) MRI intracranial hemodynamic parameters for predicting acute mountain sickness (AMS). Methods Totally 72 healthy young volunteers were prospectively recruited. Intracranial hemodynamic parameters of internal carotid artery (ICA) and internal jugular vein (IJV) were measured using PC MRI under normal breathing, as well as mild, moderate and severe Valsalva maneuvers (VM) in plain area. The subjects were divided into AMS group (n=9) and non-AMS group (n=63) according to results of Lake Louise score (LLS) 10 h after a rapid ascent to plateau area with altitude of 4 411 m. Univariate and multivariate logistic regression analyses were performed to screen independent predictors of AMS under different states and then construct single and combined VM states prediction models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was calculated to evaluate the predictive efficacy of each model. Results ICA pulsatility index (PIICA) under mild VM, IJV cross-sectional area (SIJV) under moderate VM and IJV resistance index (RIIJV) under severe VM were all independent predictors of AMS (all P＜0.05). The efficacy of combined VM states model (AUC=0.869) for predicting AMS was higher than each single VM state model (AUC=0.698—0.738). Conclusion The model constructed based on PIICA under mild VM, SIJV under moderate VM and RIIJV under severe VM could be used to effectively predict AMS. |
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