谷歌Chrome浏览器插件
订阅小程序
在清言上使用

Comprehensive Blood Coagulation Profiling in Patients Using iCoagLab: Comparison Against Thromboelastography.

THROMBOSIS AND HAEMOSTASIS(2020)

引用 7|浏览13
暂无评分
摘要
Delayed identification of coagulopathy and bleeding increases the risk of organ failure and death in hospitalized patients. Timely and accurate identification of impaired coagulation at the point-of-care can proactively identify bleeding risk and guide resuscitation, resulting in improved outcomes for patients. We test the accuracy of a novel optical coagulation sensing approach, termed iCoagLab, for comprehensive whole blood coagulation profiling and investigate its diagnostic accuracy in identifying patients at elevated bleeding risk. Whole blood samples from patients (N = 270) undergoing conventional coagulation testing were measured using the iCoagLab device. Recalcified and kaolin-activated blood samples were loaded in disposable cartridges and time-varying intensity fluctuation of laser speckle patterns were measured to quantify the clot viscoelastic modulus during coagulation. Coagulation parameters including the reaction time (R), clot progression time (K), clot progression rate (alpha), and maximum clot strength (MA) were derived from clot viscoelasticity traces and compared with mechanical thromboelastography (TEG). In all patients, a good correlation between iCoagLab- and TEG-derived parameters was observed (p < 0.001). Multivariate analysis showed that iCoagLab-derived parameters identified bleeding risk with sensitivity (94%) identical to, and diagnostic accuracy (89%) higher than TEG (87%). The diagnostic specificity of iCoagLab (77%) was significantly higher than TEG (69%). By rapidly and comprehensively permitting blood coagulation profiling the iCoagLab innovation is likely to advance the capability to identify patients with elevated risk for bleeding, with the ultimate goal of preventing life-threatening hemorrhage.
更多
查看译文
关键词
optical coagulation sensing,whole blood clotting tests,hemorrhage,bleeding,coagulation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要