• Users Online: 965
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Contacts Login 
ORIGINAL ARTICLE
Year : 2020  |  Volume : 12  |  Issue : 2  |  Page : 123-129

Logistic regression analysis to predict mortality risk in COVID-19 patients from routine hematologic parameters


1 Department of Medicine, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
2 Department of Physiology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
3 Department of Physiology, Government Medical College, Barmer, Rajasthan, India
4 Department of Pharmacology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India

Correspondence Address:
Dr. Amit Tak
4, Pushpa Path, Uniara Garden, Moti Dungri Road, Jaipur - 302 004, Rajasthan
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijmbs.ijmbs_58_20

Rights and Permissions

Background: The triage of coronavirus-19 patients into various strata based on some prognostic indicator might prove a utilitarian strategy in the management of epidemic. The goal of health-care facilities is optimization of the use of medical resources. The present study aimed to develop a predictor model of mortality risk from routine hematologic parameters. Patients and Methods: In this retrospective case–control study, seventy survivors (n = 47) and nonsurvivors (n = 23) were enrolled who were laboratory-confirmed coronavirus disease 2019 (COVID-19) cases from SMS Medical College, Jaipur (Rajasthan, India). The clinical and routine blood profile of the survivors and nonsurvivors was recorded. A logistic regression model was fitted with step-wise method to the above dataset with dependent variable such as survivor or nonsurvivor and independent variables such as age, sex, symptoms, random blood glucose, and complete blood count. The best model was selected on the basis of Akaike information criterion. Results: It was observed that differential neutrophil count (%) and random blood sugar (RBS in mg/dL) are the statistically significant regressors (P < 0.05). The performance metrics of the model with 5-fold cross-validation showed area under the receiver operating characteristic curve, sensitivity, specificity, and validation accuracy to be 0.95, 90%, 92%, and 70%, respectively. The cutoff probability comes out at 0.30 for the outcome (nonsurvivor as success). Conclusion: The study concludes that differential neutrophil count and RBS levels can be used as early screening tools of mortality risk in COVID-19 patients and they assist in further patient management.


[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
    Viewed3231    
    Printed79    
    Emailed0    
    PDF Downloaded387    
    Comments [Add]    

Recommend this journal