Anti-inflammatory therapy for COVID-19: effectiveness and predictors of response

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Abstract

BACKGROUND: According to the international guidelines and expert opinions, medicines for the treatment of COVID-19 are prescribed off-label; however, the final decision is made by a physician based on an assessment of the risk/benefit ratio for each patient. Global studies of the efficacy and safety of the use of baricitinib, tocilizumab, olokizumab, dexamethasone in the treatment of COVID-19 are ongoing. There is no information about comparative efficacy of these drugs and on the prognosis of their use in COVID-19.

AIM: To compare the effects of pre-emptive anti-inflammatory therapy (PAT) with tocilizumab, olokizumab, baricitinib, dexamethasone in patients with COVID-19 to identify response predictors and the choice of the most effective treatment.

MATERIALS AND METHODS: A retrospective analysis of 229 cases of severe and moderate course of COVID-19 requiring various types of UPT at the Hospital for War Veterans has been carried out.

RESULTS: In the study of 229 clinical cases of severe COVID-19, it was found that the most significant predictors of the effects of anticytokine therapy include C-reactive protein (CRP), body mass index (BMI), body temperature, saturation level and the need for a certain type of oxygen support at the start of a therapy, platelet count, hematocrit, neutrophil count, and duration of the disease from its onset to development of signs of a cytokine storm. The probability of recovery in the patients with early appointment of UPT increases by 13%; the need for additional oxygen support increases the risk of mortality by 5.3 times as it increases with the transition to each subsequent level; an increase in the level of CRP by 1% increases the unfavorable prognosis; an increase in D-dimer worsens the prognosis by 16%.

CONCLUSION: Based on the statistical data obtained by the method of stepwise regression analysis, was proposed method for predicting the effectiveness of proactive anti-inflammatory therapy in novel coronavirus infections.

About the authors

Irina M. Sukhomlinova

North-Western State Medical University named after I.I. Mechnikov; War veterans Hospital

Author for correspondence.
Email: sukhomlinova2021@list.ru
ORCID iD: 0000-0003-2325-8971
SPIN-code: 6953-1120

MD, applicant

Russian Federation, 47, Piskarevsky Ave., Saint Petersburg, 195067; 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, 47, Piskarevsky Ave., Saint Petersburg, 195067

Maxim Yu. Kabanov

North-Western State Medical University named after I.I. Mechnikov; War veterans Hospital

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

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

Russian Federation, 47, Piskarevsky Ave., Saint Petersburg, 195067; Saint Petersburg

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Supplementary files

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2. Fig. 1. Distribution of the patients in groups by age (conditional outcome: 0 — discharge, 1 — death). SD — standard deviation

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3. Fig. 2. Distribution of the patients in groups depending on the volume of lung damage according to CT data: a — group with recovery; b — group with fatal outcome

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4. Fig. 3. Distribution of the patients depending on the period of developing clinical symptoms before developing symptoms requiring anti-inflammatory therapy (conditional outcomes: 0 — discharge, 1 — death). Median — average value

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5. Fig. 4. ROC curves for analysis of the therapy effect in the patients with COVID-19. Diagonal segments are formed by coincidences

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6. Fig. 5. Distribution of the patients according to the level of C-reactive protein considering the outcome (conditional outcome: 0 — discharge, 1 — death)

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7. Fig. 6. ROC-curve of the model for assessing the probability of a fatal outcome

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

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