論文 【 表示 ／ 非表示 】

論文 【 表示 ／ 非表示 】

• Development of a Machine Learning-Based Prediction Model to Differentiate Infectious and Non-Infectious Diseases in Patients with Undiagnosed Fever: A Single Hospital-Based Retrospective Study 査読あり

  Nakamura M., Yamashita S., Osako R., Motomura S., Katsuki N.E., Yamashita S.I., Tago M.

  Journal of Clinical Medicine   15 ( 5 )   2026年3月

  　詳細を見る

  担当区分：筆頭著者   掲載種別：研究論文（学術雑誌）   出版者・発行元：Journal of Clinical Medicine  

  Background/Objectives: Fever can develop from several causes, including infectious diseases, noninfectious inflammatory diseases (NIID), malignancies, and other medical conditions. Although serum ferritin (SF) level can help differentiate infectious from non-infectious diseases, its discriminative ability (specificity) is far from satisfactory. The aim of this study was to develop a diagnostic prediction model to distinguish infectious diseases from other febrile illnesses using only common blood tests available on admission, in addition to SF level, in patients with undiagnosed fever. Methods: This single-center retrospective observational study included patients with fever of unidentified origin aged ≥18 years admitted to a Japanese acute care hospital between 1 January 2013, and 31 December 2022. They were divided into infectious and non-infectious disease groups based on their final diagnosis. Machine learning and multivariable logistic regression analysis were used to develop a model to differentiate infectious diseases from non-infectious diseases. Model performance was evaluated using area under the curve (AUC), shrinkage coefficient, and stratified likelihood ratio. Results: Among the 143 patients included, 73 had infectious diseases. A prediction model consisting of five factors—serum white blood cell count, neutrophil percentage, platelet count, lactate dehydrogenase level, and log-transformed SF level—was developed. The AUC of the model was 0.794 (95% confidence interval: 0.721–0.867) with a sensitivity of 77.1%, specificity of 68.5%, shrinkage coefficient of 0.876, and stratified likelihood ratio of 0.13–5.04. Conclusions: We developed a prediction model consisting of only five high-performing indicators, which would help differentiate infectious diseases from other fever causes early after admission.

  DOI： 10.3390/jcm15051905

  Scopus

講演・口頭発表等 【 表示 ／ 非表示 】

講演・口頭発表等 【 表示 ／ 非表示 】

• 地方救命救急センターの診療体制変更に伴う変化について

  島津志帆子, 中村仁彦, 長嶺育弘, 金丸勝弘, 落合秀信

  第48回日本救急医学会総会・学術集会 

  　詳細を見る

  開催年月日： 2020年11月18日 - 2020年11月20日

  会議種別：ポスター発表  

• 院外心室細動患者にてCPR法開始までの時間と心拍再開との関係(The duration of CPR skills for return of spontaneous circulation in out-ofhospital ventricular fibrillation)(英語)

  矢野隆郎，山内弘一郎, 中村仁彦, 長嶺育弘, 落合秀信

  第48回日本救急医学会総会・学術集会 

  　詳細を見る

  開催年月日： 2020年11月18日 - 2020年11月20日

  会議種別：ポスター発表  

• 非ショック適応心肺停止患者に対するアドレナリン投与時間効果

  中村仁彦, 長嶺育弘, 山内弘一郎, 矢野隆郎, 落合秀信

  第48回日本救急医学会総会・学術集会 

  　詳細を見る

  開催年月日： 2020年11月18日 - 2020年11月20日

  会議種別：ポスター発表