Clinical, laboratory and instrumental predictors of the effectiveness of anti-inflammatory therapy in COVID-19

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Abstract

BACKGROUND: Anti-inflammatory therapy is a leading method of pathogenetic treatment of moderate and severe forms of COVID-19. The drugs used during anti-inflammatory therapy, in particular, dexamethasone, olokizumab, tocilizumab, and baricitinib, are still actually prescribed in off-label mode. Of course, their use is justified by the Russian and international clinical guidelines, practical experience, expert opinions. However, the opinion of an attending physician, based on the assessment of the risk/benefit ratio for each patient, is fundamental in determining a drug for anti-inflammatory therapy. Determination of clinical and laboratory predictors of anti-inflammatory therapy effectiveness in moderate and severe forms of COVID-19 will facilitate a decision-making process when identifying risk groups for developing an adverse outcome during anti-inflammatory therapy, as well as determining an optimal drug for an anti-inflammatory therapy, taking into account the identified criteria.

AIM: To compare the effectiveness of preemptive anti-inflammatory therapy with anticytokine drugs (tocilizumab, olokizumab, baricitinib, dexamethasone) in the patients with moderate and severe COVID-19 to identify clinical, laboratory and instrumental predictors of anti-inflammatory therapy outcome.

MATERIALS AND METHODS: A retrospective analysis of 229 cases of severe and moderate COVID-19 disease requiring various types of anti-inflammatory therapy at the Hospital of War Veterans, including the Lenexpo site.

RESULTS: The study has identified the main (significantly affecting the outcome) and additional (significant) predictors of the effectiveness of anti-inflammatory therapy in moderate and severe forms of COVID-19. The main ones include: the level of oxygen support, the period from the onset of clinical manifestations, the level of C-reactive protein, D-dimer. The additional, but significant factors include: the amount of damage to the lung tissue according to the computed tomography data, the presence and degree of compensation of concomitant pathology, the presence of therapy for concomitant pathology, as well as the level of leukocytes and neutrophils in the clinical blood test.

CONCLUSIONS: The presence of additional oxygen support is a leading predictor of the effectiveness of an anti-inflammatory therapy, and its administration as early as possible, if indicated, can significantly increase the chances of a favorable outcome for a patient with moderate to severe COVID-19. Important prognostic markers also include C-reactive protein and D-dimer. The presence of concomitant diseases in anamnesis, as well as the degree of lung damage according to computer tomography data, are significant factors; however, they should be compared with other clinical and laboratory data and the objective status of the patient in order to predict the outcome of an anti-inflammatory therapy.

About the authors

Irina M. Sukhomlinova

North-Western State Medical University named after I.I. Mechnikov; Hospital for Veterans of Wars

Author for correspondence.
Email: sukhomlinova2021@list.ru
ORCID iD: 0000-0003-2325-8971
SPIN-code: 6953-1120
Russian Federation, Saint Petersburg; Saint Petersburg

Igor G. Bakulin

North-Western State Medical University named after I.I. Mechnikov

Email: igbakulin@yandex.ru
ORCID iD: 0000-0002-6151-2021
SPIN-code: 5283-2032
Scopus Author ID: 6603812937
ResearcherId: P-4453-2014

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

Russian Federation, Saint Petersburg

Maxim Yu. Kabanov

North-Western State Medical University named after I.I. Mechnikov; Hospital for Veterans of Wars

Email: makskabanov@gmail.ru
ORCID iD: 0000-0001-9763-8497

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

Russian Federation, Saint Petersburg; Saint Petersburg

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Supplementary files

Supplementary Files
Action
1. JATS XML
2. Fig. 1. Distribution by hemoglobin level in each of the drug groups. 0 — dexamethasone; 1 — tocilizumab; 2 — baricitinib; 3 — olokizumab; Median — average value

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3. Fig. 2. Distribution according to the relative level of lymphocytes in each of the drug groups. 0 — dexamethasone, 1 — tocilizumab, 2 — baricitinib, 3 — olokizumab, Median — average value

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4. Fig. 3. Distribution of the patients in the groups depending on the occurrence of comorbidity. CAD — coronary artery disease; HT — hypertension; DM 2 — type 2 diabetes mellitus; COPD — chronic obstructive pulmonary disease; BA — bronchial asthma

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Copyright (c) 2022 Sukhomlinova I., Bakulin I., Kabanov M.

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