将输液成像衍生的静脉外流和组织水平的附带参数集成到一个全面的临床模型中,可以提高大血管阻塞中风的预后
Janet Mei1, Hamza Adel Salim2, Dhairya A Lakhani3
1Department of Radiology, Division of Neuroradiology, Johns Hopkins Medical Center, Baltimore, MD, USA.
概括
延长静脉通道 (PVT) 和脑血量 (CBV) 指数改善了在接受机械血栓切除术的中风患者的预测结果. 将PVT与CBV指数相结合,为预测功能结果提供了最高的准确性.
科学领域:
- 神经学 神经学
- 放射学 放射学是一门学科.
- 医疗成像医学成像
背景情况:
- 动脉输入和附带状况显著影响急性缺血性中风与大血管封闭 (AIS-LVO) 患者的功能结果,这些患者接受了机械血栓切除术 (MT).
- 像延长静脉通道 (PVT),脑血体积指数 (CBV) 和低 perfusion 强度比 (HIR) 这样的CT输液 (CTP) 成像生物标志物对于评估血液流量是有价值的.
- 这些CTP生物标志物在提高预后预测以及临床和动脉流入参数的比较有效性需要进一步澄清.
研究的目的:
- 评估PVT,CBV指数和HIR作为预治疗成像生物标志物的有效性,用于预测接受MT的AIS-LVO患者的功能结果.
- 将单个和组合的CTP生物标志物的预测性能与已确定的临床和动脉流入参数进行比较.
- 确定成像生物标志物的最佳组合,以提高该患者队列的预后准确性.
主要方法:
- 对149名前部循环AIS-LVO患者进行回顾性分析,这些患者接受了MT.
- 根据特定静脉鼻的Tmax时间来定义PVT;CBV指数和HIR来自自动CTP软件.
- 将临床因素,动脉流入和CTP生物标志物 (单独和组合) 纳入的后勤回归模型使用接收器操作特征分析和Delong测试进行了评估.
主要成果:
- PVT与90天修改后的Rankin尺度不良结果的可能性更高.
- 将PVT纳入基线模型显著改善了结果预测 (AUC:0.821),优于单独使用CBV指数或HIR的模型.
- 结合PVT和二分化CBV指数,实现了最高的预测准确性 (AUC:0.831),显著超过了基线模型.
结论:
- 结合PVT和CBV指数,以及临床和干预参数,大大提高了AIS-LVO患者功能结果的预测准确性.
- 与CBV指数或HIR相比,PVT在这个人群中表现出更强大的功能结果预测能力.
- 这种全面的成像和临床模型具有结果分层和指导中风管理中的临床决策的潜力,这证明了未来的验证.
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