| Project/Area Number |
18K12126
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 90130:Medical systems-related
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| Research Institution | National Cardiovascular Center Research Institute |
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Principal Investigator |
Uemura Kazunori 国立研究開発法人国立循環器病研究センター, 研究所, 室長 (10344350)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 血圧 / 心拍出量 / ウェアラブル / ストレスフリー / 機械学習 / 血行動態モニター / 循環管理システム / ベータ遮断薬 |
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| Outline of Final Research Achievements |
In this research project, we developed prototypes of potentially wearable, stress-free, non-invasive devices to monitor critical hemodynamic variables including arterial blood pressure (BP), cardiac output (CO), and atrial pressures. We have first developed a minimally occlusive cuff BP monitor, which utilizes a thin plate-type ultrasound probe placed between the cuff and the skin. In 6 anesthetized animals, we validated that the reliability of the BP monitor was acceptable according to the AAMI/ISO standards. In the wearable monitor, we planned to implement a module to estimate CO from the BP pulse contour obtainable with use of the developed BP monitor. As the prototype of CO monitor, we have developed a trans-esophageal Doppler CO monitor, which utilizes a peripheral BP pulse contour and a machine learning approach. In 12 anesthetized animals, we have validated that the developed CO monitor tracks changes in CO with acceptable reliability under diverse hemodynamic conditions.
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| Academic Significance and Societal Importance of the Research Achievements |
開発したストレスフリー血圧モニターにおける微小薄型超音波プローブと従来のカフ式血圧計を統合するという画期的アイデアは、これまで例がなく学術的意義が大きい。この開発により、24時間血圧計(ABPM)の睡眠時ストレスなどの問題が克服でき、高血圧診療が改善する。これは国民死因の多くを占める脳卒中や心筋梗塞の克服にもつながりえ、その社会的意義は計り知れない。開発した経食道心エコードプラー心拍出量モニターにおける機械学習の応用は過去に例がなく、学術的意義が大きい。またこのモニターにより、周術期循環管理の精度が改善することは、国民死因のトップである癌患者等の予後改善にもつながり、社会的意義は大きい。
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