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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
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijmbs.ijmbs_58_20

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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.

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