A method for predicting the effectiveness of glucocorticoid therapy in patients with moderate COVID-19 based on simple clinical and laboratory data

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Abstract

BACKGROUND: In patients hospitalized with coronavirus infection (COVID-19), methods for predicting the effectiveness of anti-inflammatory therapy have important practical implications for optimizing treatment and outcomes. To date, several indicators of COVID-19 patients (age, comorbidities, and laboratory criteria for the intensity of inflammation) have been identified to indicate a high probability of a severe course and a risk of an adverse outcome. However, the problem of predicting the effectiveness of anti-inflammatory therapy in patients with moderate COVID-19 is not well understood.

AIM: This study aimed to develop a predictive model to determine the effectiveness/failure of anti-inflammatory therapy with glucocorticosteroids (GCS) in patients with moderate COVID-19 to improve the treatment outcomes of hospitalized patients.

MATERIALS AND METHODS: This study retrospectively analyzed electronic medical record data of all patients admitted consecutively from October 1, 2020, to January 31, 2021. The study included 71 patients with probable (clinically confirmed) and confirmed (laboratory) COVID-19 of moderate course, with characteristic changes in the lungs according to computed tomography of the chest organs (CT-CCT). Given the severity of the course, all study patients were prescribed GCS in accordance with the current version of the Interim Guidelines of the Ministry of Health of the Russian Federation.

RESULTS: A total of 71 patients were studied, and 53 (74.7%) of them did not require an escalation of anti-inflammatory therapy, which is regarded as an effective use of corticosteroids as an anti-inflammatory therapy (group 1). In the remaining 18 patients, the use of corticosteroids for an average of 5.5 (3–6) days did not have a definite clinical effect and required the additional use of monoclonal antibodies (MCA) to interleukin-6 (IL-6) or to its receptor (group 2). Using logistic regression analysis and receiver operating characteristic analysis, a mathematical model was developed and evaluated to predict the outcome of anti-inflammatory corticosteroid therapy in patients with moderate COVID-19. As risk factors, indicators that had significant differences in the studied groups before GCS initiation were selected: number of lymphocytes, platelets, and body temperature. The quality of the constructed model is assessed as very good, and the optimal cutoff point is 0.697. The sensitivity index of the model is 81.1%, and the specificity index is 72.2%.

CONCLUSIONS: The mathematical model makes it possible to predict the effectiveness of GCS therapy according to the number of lymphocytes, platelets, and body temperature. The mathematical model is adequate and has a high sensitivity and specificity.

About the authors

Dmitry O. Efremov

Branch No. 1 of the National Medical Research Center for High Medical Technologies

Email: Efremov-d24@mail.ru
ORCID iD: 0000-0001-7889-6052
SPIN-code: 7115-2713

начальник инфекционного центра филиала №1 ФГБУ «Национальный медицинский исследовательский центр высоких медицинских технологий имени А. А. Вишневского» Минобороны России

Russian Federation, Krasnogorsk, Moscow Region, 143409

Vladimir B. Beloborodov

Russian Medical Academy of Continuous Professional Education

Author for correspondence.
Email: belvb1070@mail.ru
ORCID iD: 0000-0002-0544-4167
SPIN-code: 4233-2046

MD, Dr. Sci. (Med.), Professor

Russian Federation, Moscow

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Supplementary files

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2. Fig. 1. Timing of the appointment of MCA to IL-6 or its receptor from the start of the use of GCS (indicated by dots) (n=18).

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3. Fig. 2. ROC-curve for assessing the quality of the logistic regression model for predicting the effectiveness of GCS therapy

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