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CC BY-NC-ND 4.0 · Ibnosina Journal of Medicine and Biomedical Sciences 2020; 12(02): 123-129
DOI: 10.4103/ijmbs.ijmbs_58_20
Original Article

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

Sudhir Bhandari
1   Department of Medicine, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
,
Ajit Shaktawat
1   Department of Medicine, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
,
Amit Tak
2   Department of Physiology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
,
Bhoopendra Patel
3   Department of Physiology, Government Medical College, Barmer, Rajasthan, India
,
Jyotsna Shukla
2   Department of Physiology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
,
Sanjay Singhal
2   Department of Physiology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
,
Kapil Gupta
2   Department of Physiology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
,
Jitendra Gupta
2   Department of Physiology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
,
Shivankan Kakkar
4   Department of Pharmacology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
,
Amitabh Dube
2   Department of Physiology, SMS Medical College and Attached Hospitals, Jaipur, Rajasthan, India
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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.

Key-words:

Coronavirus disease 2019 - differential neutrophil count - logistic regression model - random blood sugar - receiver operating characteristic curve

Financial support and sponsorship

Nil.




Publication History

Received: 25 May 2020

Accepted: 17 June 2020

Article published online:
07 July 2022

© 2020. The Libyan Authority of Scientific Research and Technologyand the Libyan Biotechnology Research Center. All rights reserved. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License,permitting copying and reproductionso long as the original work is given appropriate credit. Contents may not be used for commercial purposes, oradapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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