| Project/Area Number |
17K16514
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
General surgery
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| Research Institution | Jichi Medical University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | 補助人工心臓 / 創部感染 / 遠隔医療 / アプリ / 在宅医療 / モバイルアプリ / 重症心不全 / 感染症 / 人工臓器 / 人工心臓 |
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| Outline of Final Research Achievements |
First of all, we investigated the problems of in-home medical care for the patients with implantable ventricular assist devices, and conducted an environmental survey surrounding medical apps. We figured out that the importance of consultation not only between doctors to patients, but also between doctors to doctors in this field. The problems in the in-home medical care are driveline infection, anticoagulant therapy and poor compliance patients. In order to solve them, we developed a ventricular assist device home management consult app, which is clinically available. The scoring of the driveline exit site photos potentially contributed to the management of the driveline infection. On the other hand, it was found that the establishment of a consultation system was an unsolved problem, and the development of an application that can be used between patients and medical staff is also required.
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| Academic Significance and Societal Importance of the Research Achievements |
補助人工心臓患者は増加の一途を辿っており、さらにDestination therapyが保険償還されさらなる患者数の増加が予想され、医療者への負担が大きくなることが予想される。本アプリはその中で、医師間コンサルトという、補助人工心臓在宅管理において注目が低かった部分をカバーするアプリとして有用であると考えられる。一方で、臨床使用例からドライブライン貫通部の写真は感染悪化を捉えており、今後、写真からドライブライン感染の悪化を予測する画像解析の可能性を見出している。補助人工心臓患者における遠隔医療、在宅医療、画像解析による医療管理の基礎となったと考えられる。
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