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A machine learning analysis of correlates of mortality among patients hospitalized with COVID-19

التفاصيل البيبلوغرافية
العنوان: A machine learning analysis of correlates of mortality among patients hospitalized with COVID-19
المؤلفون: Timothy B. Baker, Wei-Yin Loh, Thomas M. Piasecki, Daniel M. Bolt, Stevens S. Smith, Wendy S. Slutske, Karen L. Conner, Steven L. Bernstein, Michael C. Fiore
المصدر: Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
بيانات النشر: Nature Portfolio, 2023.
سنة النشر: 2023
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: Abstract It is vital to determine how patient characteristics that precede COVID-19 illness relate to COVID-19 mortality. This is a retrospective cohort study of patients hospitalized with COVID-19 across 21 healthcare systems in the US. All patients (N = 145,944) had COVID-19 diagnoses and/or positive PCR tests and completed their hospital stays from February 1, 2020 through January 31, 2022. Machine learning analyses revealed that age, hypertension, insurance status, and healthcare system (hospital site) were especially predictive of mortality across the full sample. However, multiple variables were especially predictive in subgroups of patients. The nested effects of risk factors such as age, hypertension, vaccination, site, and race accounted for large differences in mortality likelihood with rates ranging from about 2–30%. Subgroups of patients are at heightened risk of COVID-19 mortality due to combinations of preadmission risk factors; a finding of potential relevance to outreach and preventive actions.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-023-31251-1
URL الوصول: https://doaj.org/article/0b44c5e661144b3da4cbab2d736422cc
رقم الانضمام: edsdoj.0b44c5e661144b3da4cbab2d736422cc
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-023-31251-1