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Finding of the factors affecting the severity of COVID-19 based on mathematical models.
Qu, Jiahao; Sumali, Brian; Lee, Ho; Terai, Hideki; Ishii, Makoto; Fukunaga, Koichi; Mitsukura, Yasue; Nishimura, Toshihiko.
  • Qu J; School of Integrated Design Engineering, Keio University, Yokohama, Kanagawa, Japan.
  • Sumali B; School of Integrated Design Engineering, Keio University, Yokohama, Kanagawa, Japan.
  • Lee H; Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan.
  • Terai H; Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan.
  • Ishii M; Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan.
  • Fukunaga K; Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan.
  • Mitsukura Y; School of Integrated Design Engineering, Keio University, Yokohama, Kanagawa, Japan. mitsukura@keio.jp.
  • Nishimura T; Department of Anesthesia, Stanford University School of Medicine, Stanford, CA, USA.
Sci Rep ; 11(1): 24224, 2021 12 20.
Article in English | MEDLINE | ID: covidwho-1585790
ABSTRACT
Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Models, Theoretical Type of study: Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-03632-x

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Models, Theoretical Type of study: Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged / Young adult Language: English Journal: Sci Rep Year: 2021 Document Type: Article Affiliation country: S41598-021-03632-x