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PFI-RP: Partnership for Innovation - Avoiding Kidney Injuries with Evidence-Based Smart Technology

PFI-RP: Partnership for Innovation - Avoiding Kidney Injuries with Evidence-Based Smart Technology
PFI-RP:创新合作——利用循证智能技术避免肾脏损伤
批准号:
2122901
负责人:
Erica Forzani
金额:
$55.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-09-01 至 2024-08-31

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中文摘要
翻译
这一创新-研究伙伴关系(PFI-RP)项目的更广泛影响/商业潜力是开发一种新技术,提高医生诊断住院患者急性肾损伤的能力。急性肾损伤是一种开始时没有临床症状或体征的内科疾病,据估计,高达20%的住院患者会发生急性肾损伤。急性肾损伤会对患者造成重大伤害,并极大地增加整体医疗成本。急性肾损伤通常在病程的早期并未被发现。目前用于诊断急性肾损伤的方法尚不完善。使用目前可用的方法识别急性肾损伤是延迟的,并且只有在发生严重的器官功能障碍之后才会发生。该项目将开发的技术可能会更早地发现急性肾损伤,以便进行治疗。该项目将开发一种新的技术,用于对急性肾损伤的尿液生物标志物进行连续、实时的测量和实时分析。具体来说,将开发和建设尿液成分连续测量系统。该系统将用于测量和量化尿液成分,并开发早期急性肾损伤模型。将开发一个机器学习预测模型,并用模型系统进行测试。这一奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The broader impact/commercial potential of this Partnerships for Innovation – Research Partnerships (PFI-RP) project is to develop a new technology that improves the ability of doctors to diagnose acute kidney injury in hospitalized patients. Acute kidney injury is a medical condition which begins without clinical symptoms or signs, and has been estimated to occur in up to 20% of hospitalized patients. Acute kidney injury results in significant harm to patients as well as greatly increased overall health care costs. Acute kidney injury is generally not recognized early in its course. Currently available methods used to diagnose acute kidney injury are imperfect. Recognition of acute kidney injury using presently available methods is delayed and only occurs after the development of significant organ dysfunction. The technology that will be developed in this PFI-RP project may detect acute kidney injury earlier so that treatment can be rendered.This project will develop a new technology for continuous, real time measurement and real time analysis of urinary biomarkers of acute kidney injury. Specifically, a system for continuous measurement of urine components will be developed and built. This system will be used to measure and quantify urine components and develop a model of early acute kidney injury. A machine learning prediction model will be developed and tested with the model system.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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