Predicting cognitive decline from routinely collected data

• Project/Area Number: 17H07421
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Single-year Grants
• Research Field: Applied health science
• Research Institution: Fujita Health University (2018); National Cardiovascular Center Research Institute (2017)
• Principal Investigator: Ogata Soshiro 藤田医科大学, 医療科学部, 講師 (00805012)
• Project Period (FY): 2017-08-25 – 2019-03-31
• Project Status: Completed (Fiscal Year 2018)
• Budget Amount: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000); Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000); Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
• Keywords: 認知機能 / 予測モデル / 自治体保有情報 / ビッグデータ / 循環器病リスク要因 / 機械学習 / AI / 地域在住高齢者

Outline of Final Research Achievements

The present study conducted an epidemiological study collaborating with Nobeoka city, and collected data related to cognitive function from 480 community-dwelling people aged 75 years old and over. By utilizing a sparse modeling as a machine learning method, we developed a diagnostic prediction model of cognitive impairment (i.e., the Routine prediction model) only based on information routinely collected by Japanese cities. This had moderate predictability (AUC：0.70~0.71). Additionally, we developed an additional diagnostic prediction model (i.e., the Additional prediction model) by adding education level, category fluency test, and short-term memory test to the Routine prediction model. This had relatively high predictability (AUC：0.74~0.96). Thus, the present study developed the diagnostic prediction models of cognitive impairment with moderate to high predictability, which just required information collected routinely by cities and/or additional information collected easily.

Academic Significance and Societal Importance of the Research Achievements

認知症対策として認知機能低下を早期発見し予防につなげることが重要な戦略である。本研究で開発した地域在住後期高齢者を対象とした認知機能障害予測モデルは、日本の市町村が既に業務の一環として収集している情報を主として使用している。そのため、市町村は本研究で開発した予測モデルを低コストで今すぐ運用することが可能である。以上のことから、本研究は認知症早期発見システム構築に貢献すると考える。

Research Products

• [Presentation] 地域在住後期高齢者における認知機能と過去数年間の循環器病リスク要因の経時変化の関連 (2019)
  • Author(s): 尾形宗士郎, 清重映里, 竹上未紗, 中井陸運, 中尾葉子, 神出計, 西村 邦宏, 宮本恵宏
  • Organizer: 日本循環器病予防学会
• [Presentation] 地域在住後期高齢者における認知機能を予測する要因の検討 (2018)
  • Author(s): 尾形宗士郎、西村邦宏、宮本恵宏
  • Organizer: 日本公衆衛生学会
• [Presentation] Developing a Prediction Model to Estimate Low Cognitive Function in Japanese People Aged 75 Years and Over (2018)
  • Author(s): Soshiro Ogata, Michikazu Nakai, Misa Takegami, Kunihiro Nishimura, Yoshihiro Miyamoto
  • Organizer: Gerontological Society of America
  • Int'l Joint Research
• [Presentation] Temporal order between depressive symptoms and subjective memory complaints using cross lagged panel model (2018)
  • Author(s): Haruka Tanaka, Soshiro Ogata, Chisato Hayashi
  • Organizer: Gerontological Society of America
  • Int'l Joint Research
• [Presentation] Trajectories of Stroke Risk Factors Before Stroke Onset With a 24-year Follow-up of Japanese People Living in an Urban Area: The Suita Study (2018)
  • Author(s): Soshiro Ogata, Fumiaki Nakamura, Kunihiro Nishimura, Makoto Watanabe, Yoshihiro Kokubo, Aya Higashiyama, Misa Takegami, Yoko M Nakao, Michikazu Nakai, Tomonori Okamura, and Yoshihiro Miyamoto
  • Organizer: American Heart Association's Epidemiology and Prevention/Lifestyle and Cardiometabolic Health
  • Int'l Joint Research