Users Online: 1953
Home Print this page Email this page
Home About us Editorial board Search Browse articles Submit article Ahead of Print Instructions Subscribe Contacts Special issues Login 
ORIGINAL ARTICLE
Year : 2022  |  Volume : 11  |  Issue : 1  |  Page : 58

Identification of high death risk coronavirus disease-19 patients using blood tests


1 Department of Biology, Faculty of Basic Sciences, Azarbaijan Shahid Madani University, Tabriz, Iran
2 Department of Internal Medicine, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz; Emam Hossein Hospital, Tabriz University of Medical Sciences, Hashtrood, Iran
3 Emam Hossein Hospital, Tabriz University of Medical Sciences, Hashtrood; Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran

Correspondence Address:
Dr. Ghader Molavi
Immunology Research Center, Tabriz University of Medical Sciences, Tabriz 5166614733
Iran
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/abr.abr_178_21

Rights and Permissions

Background: The coronavirus disease (COVID-19) pandemic has made a great impact on health-care services. The prognosis of the severity of the disease help reduces mortality by prioritizing the allocation of hospital resources. Early mortality prediction of this disease through paramount biomarkers is the main aim of this study. Materials and Methods: In this retrospective study, a total of 205 confirmed COVID-19 patients hospitalized from June 2020 to March 2021 were included. Demographic data, important blood biomarkers levels, and patient outcomes were investigated using the machine learning and statistical tools. Results: Random forests, as the best model of mortality prediction, (Matthews correlation coefficient = 0.514), were employed to find the most relevant dataset feature associated with mortality. Aspartate aminotransferase (AST) and blood urea nitrogen (BUN) were identified as important death-related features. The decision tree method was identified the cutoff value of BUN >47 mg/dL and AST >44 U/L as decision boundaries of mortality (sensitivity = 0.4). Data mining results were compared with those obtained through the statistical tests. Statistical analyses were also determined these two factors as the most significant ones with P values of 4.4 × 10−7 and 1.6 × 10−6, respectively. The demographic trait of age and some hematological (thrombocytopenia, increased white blood cell count, neutrophils [%], RDW-CV and RDW-SD), and blood serum changes (increased creatinine, potassium, and alanine aminotransferase) were also specified as mortality-related features (P < 0.05). Conclusions: These results could be useful to physicians for the timely detection of COVID-19 patients with a higher risk of mortality and better management of hospital resources.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed650    
    Printed40    
    Emailed0    
    PDF Downloaded148    
    Comments [Add]    

Recommend this journal