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A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort.
Ulgen, Ayse; Cetin, Sirin; Cetin, Meryem; Sivgin, Hakan; Li, Wentian.
  • Ulgen A; Department of Biostatistics, Faculty of Medicine, Girne American University, Karmi, Cyprus.
  • Cetin S; Department of Biostatistics, Faculty of Medicine, Tokat Gaziosmanpasa University, Turkey.
  • Cetin M; Department of Medical Microbiology, Faculty of Medicine, Amasya University, Amasya, Turkey.
  • Sivgin H; Department of Internal Medicine, Faculty of Medicine, Tokat Gaziosmanpasa University, Turkey.
  • Li W; The Robert S. Boas Center for Genomics and Human Genetics, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA. Electronic address: wtli2012@gmail.com.
Comput Biol Chem ; 98: 107681, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1778061
ABSTRACT
Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recovery as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10 ~ 15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contain calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials / Reviews Limits: Humans Language: English Journal: Comput Biol Chem Journal subject: Biology / Medical Informatics / Chemistry Year: 2022 Document Type: Article Affiliation country: J.compbiolchem.2022.107681

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Observational study / Prognostic study / Randomized controlled trials / Reviews Limits: Humans Language: English Journal: Comput Biol Chem Journal subject: Biology / Medical Informatics / Chemistry Year: 2022 Document Type: Article Affiliation country: J.compbiolchem.2022.107681