Integrated International Database for Emergency and Critically-ill Patients(Fostering Joint International Research)

• Project/Area Number: 16KK0211
• Research Category: Fund for the Promotion of Joint International Research (Fostering Joint International Research)
• Allocation Type: Multi-year Fund
• Research Field: Emergency medicine
• Research Institution: Nippon Medical School
• Principal Investigator: TAGAMI TAKASHI 日本医科大学, 医学部, 講師 (40626272)
• Project Period (FY): 2017 – 2019
• Project Status: Completed (Fiscal Year 2019)
• Budget Amount: ¥14,950,000 (Direct Cost: ¥11,500,000、Indirect Cost: ¥3,450,000)
• Keywords: 国際データベース / データベース

Outline of Final Research Achievements

The aim of the current project was to create an internationally-linked centralized main database for both emergency and critically ill patients in the near future. We analyzed and submitted the research paper to a journal using the Pan-Asian Resuscitation Outcomes Study (PAROS)database. PAROS was established in collaboration with emergency medical services agencies and academic centers in Japan, Singapore, South Korea, Malaysia, Taiwan, Thailand, and the United Arab Emirates for better understanding of OHCA trends in Asia. Common data definitions and collection methods were used in all countries. We analyzed and submitted the research paper to a journal (under review) using the PAROS database. Manuscript title: Impact of population aging on the presentation of out-of-hospital cardiac arrest in the Pan Asian Resuscitation Outcomes Study

Academic Significance and Societal Importance of the Research Achievements

世界には、様々な救急医療体制・システムが存在する。救急医療体制・システムは、その国の伝統的な社会制度とも関連することが多い。そのため、詳細データなしに、救急医療システムの優劣を比較検討することは極めて難しく、また適切ではない。重症救急疾患に関わる広範囲の詳細且つ精確なデータ収集・分析により、本邦の救急医療体制・システムや治療方法の客観的検証が可能になる。
救急医療分野の学術進歩に貢献するためには、一国のみのデータベースに留まらず、救急医療システムや重症救急疾患に対する治療方法の異なる多国間比較が可能な国際データベースの解析が必要である。引き続き、継続研究が必要である。

Research Products

• [Int'l Joint Research] Duke-NUS Medical School Heaｌth Services and Systems Research(シンガポール) (2017)
  • Year and Date: 2017-10-01
• [Journal Article] Impact of population aging on the presentation of out‐of‐hospital cardiac arrest in the Pan Asian Resuscitation Outcomes Study (2019)
  • Author(s): Tagami Takashi、Tanaka Hideharu、Shin Sang Do、Ma Matthew Huei‐Ming、Ko Patrick Chow‐in、Karim Sarah、Khruekarnchana Pairoj、Naroo Ghulam Yasin、Ong Marcus Eng Hock
  • Journal Title: Acute Medicine & Surgery Volume: 7 Issue: 1
  • DOI: 10.1002/ams2.430
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Combining Heart Rate Variability with Disease Severity Score Variables for Mortality Risk Stratification in Septic Patients Presenting at the Emergency Department (2019)
  • Author(s): Pong Jeremy Zhenwen、Fook-Chong Stephanie、Koh Zhi Xiong、Samsudin Mas’uud Ibnu、Tagami Takashi、Chiew Calvin J.、Wong Ting Hway、Ho Andrew Fu Wah、Ong Marcus Eng Hock、Liu Nan
  • Journal Title: International Journal of Environmental Research and Public Health Volume: 16 Issue: 10 Pages: 1725-1725
  • DOI: 10.3390/ijerph16101725
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Development of a heart rate variability and complexity model in predicting the need for life-saving interventions amongst trauma patients (2019)
  • Author(s): Kumar Aravin、Liu Nan、Koh Zhi Xiong、Chiang Jayne Jie Yi、Soh Yuda、Wong Ting Hway、Ho Andrew Fu Wah、Tagami Takashi、Fook-Chong Stephanie、Ong Marcus Eng Hock
  • Journal Title: Burns & Trauma Volume: 7 Pages: 12-12
  • DOI: 10.1186/s41038-019-0147-2
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Combining quick sequential organ failure assessment score with heart rate variability may improve predictive ability for mortality in septic patients at the emergency department (2019)
  • Author(s): Prabhakar Sumanth Madhusudan、Tagami Takashi、Liu Nan、Samsudin Mas’uud Ibnu、Ng Janson Cheng Ji、Koh Zhi Xiong、Ong Marcus Eng Hock
  • Journal Title: PLOS ONE Volume: 14 Issue: 3 Pages: e0213445-e0213445
  • DOI: 10.1371/journal.pone.0213445
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department (2019)
  • Author(s): Chiew Calvin J.、Liu Nan、Tagami Takashi、Wong Ting Hway、Koh Zhi Xiong、Ong Marcus E. H.
  • Journal Title: Medicine Volume: 98 Issue: 6 Pages: e14197-e14197
  • DOI: 10.1097/md.0000000000014197
  • Peer Reviewed / Open Access / Int'l Joint Research