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Venous Waveform analysis to predict volume changes in pediatric patients during anesthesia

Venous Waveform analysis to predict volume changes in pediatric patients during anesthesia
静脉波形分析可预测儿科患者麻醉期间的容量变化
批准号:
10683968
负责人:
Jenna M Helmer Sobey
金额:
$21.63万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-08-15 至 2024-05-31

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PROJECT SUMMARY In the perioperative environment, precise understanding and monitoring of intravascular volume status is important. In pediatric patients, changes in vital signs (blood pressure and heart rate) often do not occur until significant blood loss and end-organ damage have ensued. Hemorrhage during surgery represents a major cause of pediatric perioperative morbidity, and early and adequate resuscitation improves survival. Unfortunately, clinical signs and current monitoring strategies are inconsistent in providing accurate, real-time assessment of volume status in pediatric patients. This proposal will develop the use of a unique physiologic signal, Non- Invasive Venous waveform Analysis (NIVA), for monitoring intravascular volume status for both hemorrhage and volume overload. Proof of concept data in humans and experimental animal models demonstrate that NIVA provides a reliable indication of intravascular volume status, supporting the premise of this proposal. This proposal will use an experimental piglet model of controlled hemorrhage, resuscitation, and volume overload to validate and refine the algorithm for waveform analysis. Additionally, the proposal will evaluate the application of the NIVA algorithm for pediatric patients undergoing complex cranial vault reconstruction to assess its ability to detect volume changes in the perioperative setting These studies will be used to mitigate risks associated with the use of NIVA and will lead to a better understanding of venous waveform analysis (for precise management of intravascular volume) and of venous hemodynamics (a largely under-studied part of the cardiovascular system) in the pediatric population.
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Venous Waveform analysis to predict volume changes in pediatric patients during anesthesia
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