Identification of Risk Factors Associated With Mortality Among Patients With COVID-19 Using Random Forest Model: A Historical Cohort Study

There is conflicting evidence about factors associated with Clinical course and risk factors for mortality of adult inpatients. We aimed to identify the demographic, clinical, treatment, and laboratory data factors associated with mortality in the Khoy district. We performed a retrospective cohort s...

Full description

Saved in:
Bibliographic Details
Published inActa medica Iranica Vol. 59; no. 8; p. 457
Main Authors Moghaddam-Tabrizi, Fatemeh, Omidi, Tahereh, Mahdi-Akhgar, Masoomeh, Bahadori, Robabeh, Valizadeh, Rohollah, Farrokh-Eslamlou, Hamidreza
Format Journal Article
LanguageEnglish
French
Published Tehran Tehran University of Medical Sciences 26.09.2021
Subjects
Chronic illnesses
Cohort analysis
Coronavirus disease 2019 (COVID-19)
Coronaviruses
COVID-19
Decision tree
Epidemiology
Infectious diseases
Mortality
Random forests
Risk factors
Variable importance
Ventilators
Online AccessGet full text

Cover

More Information
Summary:There is conflicting evidence about factors associated with Clinical course and risk factors for mortality of adult inpatients. We aimed to identify the demographic, clinical, treatment, and laboratory data factors associated with mortality in the Khoy district. We performed a retrospective cohort study including COVID-19 infected patients who were admitted to Qamar-Bani Hashim hospital from 2 November 2020 to 4 December 2020. We used random forest methods to explore the risk factors associated with death. The applied method was evaluated using sensitivity, specificity, accuracy, and the area under the curve. Age, pulmonary symptoms, patients need a ventilator, brain symptoms, nasal airway, job were the most important risk factors for mortality of COVID-19 in the random forest (RF) method. The RF method showed the highest accuracy, 82.9 and 79.3, for training and testing samples, respectively. However, this method resulted in the highest specificity (89.5% for training and 95.7% for testing sample) and the highest sensitivity (91.9% for training and 94.5% for testing sample). The potential risk factors consisting of older age, pulmonary symptoms, the use of a ventilator, brain symptoms, nasal airway, and the job could help clinicians to identify patients with poor prognosis at an early stage.
ISSN:0044-6025
1735-9694
DOI:10.18502/acta.v59i8.7248