| Project/Area Number |
18K16548
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 55060:Emergency medicine-related
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| Research Institution | St. Marianna University School of Medicine |
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Principal Investigator |
Naito Takaki 聖マリアンナ医科大学, 医学部, 助教 (30814628)
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| Project Period (FY) |
2018-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2021: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 早期傾向スコア / 機械学習 / Rapid Response System / 院内心停止 / 院内急変 / 早期警告スコア / Rapid response system / NEWS / Machine learning / rapid response system / cardiac arrest / in-hospital emergency / rapid response team / medical emergency team / 院内救急 / RRS |
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| Outline of Final Research Achievements |
To clarify the in-hospital emergency response system in our country, we integrated the Rapid Response System (RRS) registry and the In-Hospital Cardiac Arrest registry, aligning them with the American Heart Association's registry definitions. We analyzed data from each facility and provided feedback to facility representatives, comparing their data with national data. The registry data analysis revealed that early warning scores are also useful for risk stratification in our country, suggesting that this could be a solution to the current low RRS activation rate. Additionally, we developed a prognostic model for post-RRS activation using machine learning, specific to our country. This new machine learning model demonstrated superior predictive accuracy for mortality or unexpected ICU transfer within 24 hours compared to existing early warning scores.
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| Academic Significance and Societal Importance of the Research Achievements |
院内救急体制の現状を把握するための世界標準のレジストリ整備がされた。またそのデータを元にフィードバックを行っており、各施設の院内救急体制の発展への貢献が期待される。レジストリデータを用いた解析により早期警告スコアが我が国でも有用である可能性を示した。これにより早期警告スコアの導入によるRapid Response System(RRS)起動率増加、院内心停止の予防が期待される。機械学習を用いたRRS起動後の短期予後予測モデルを開発することにより、RRSによる介入の質を改善が期待される。
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