Clinical and laboratory predictors of poor outcome in COVID-19 patients

Abstract

BACKGROUND: Many researchers have reported numerous predictors of severe COVID-19 and poor prognosis. However, to make a quick decision, the doctor needs to have a certain set of data that he can use in routine practice to predict the outcome in patients with this disease.

AIMS: This study aimed to develop and describe a predictive model for determining an unfavorable outcome in COVID-19 patients based on age, objective, laboratory and instrumental data, and comorbid pathology.

MATERIALS AND METHODS: The study included 447 patients with a laboratory-confirmed diagnosis of COVID-19 who underwent inpatient treatment in the period from March 2020 to January 2021. Discriminant analysis was used with cross-validation to build a predictive model.

RESULTS: Based on discriminant analysis, a predictive model was developed to predict the outcome in patients with COVID-19. Evaluation of clinical findings, such as respiratory rate, heart rate, SpO2, laboratory data, and computed tomography results on admission to the hospital, showed their significance as predictors of poor outcome. The discrimination constant was 0.4435. The sensitivity of the model is 96.4%, and the specificity is 90.4%.

CONCLUSION: The developed model will help medical institutions predict the outcome of the disease when a patient is admitted to the hospital and, on this basis, optimize and prioritize the provision of necessary medical care.

Keywords

COVID-19 / predictive model / predictors of poor outcome / laboratory data

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RIGHTS & PERMISSIONS

Lizinfeld I.A., Pshenichnaya N.Y., Bunyaeva O.V., Shilkina I.M., Shmailenko O.A., Gopatsa G.V., Siziakin D.V., Chigaeva E.V.