Vol 27, No 2 (2022)

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.

BACKGROUND: Despite the important advances in the development of healthcare and the introduction of new and safe diagnosis and treatment technologies of various diseases, the problem of preventing health care-associated infections (HAIs) does not lose its relevance.

AIM: This study aimed to examine the features of the detection and registration of HAIs in the Ural and Siberian Federal Districts in 2021, to conduct a rating assessment of the quality of detection and registration of infections, followed by ranking of subjects to assess the functions of the epidemiological surveillance system of HAIs.

MATERIALS AND METHODS: The article presents a data analysis of the officially registered HAI cases within medical organizations of Ural and Siberian Federal Districts. The analysis was conducted based on the data from Federal Statistical Observation Forms No. 2 “Information on infectious and parasitic diseases” and No. 23–17 “Information on outbreaks of infectious diseases” and additional request materials “Information on medical manipulations and contingents treated” for 2021 and changes within 7 years (2015–2021) in comparison with indicators for the whole Russian Federation.

RESULTS: Statistical data were provided by the Departments of Rospotrebnadzor from Irkutsk, Kemerovo, Kurgan, Novosibirsk, Omsk, Sverdlovsk, Tomsk, Tyumen, Chelyabinsk regions; Altai, Transbaikal, Krasnoyarsk Territory; republics of Altai, Buryatia, Tyva, Khakassia; Khanty–Mansi Autonomous Okrug–Yugra, and Yamalo-Nenets Autonomous Okrug. Data for the Russian Federation were provided by the Federal Center for Hygiene and Epidemiology of Rospotrebnadzor according the duties of Ural–Siberian Scientific and Methodological Center for the Prevention of Health Care-associated Infections.

CONCLUSION: Based on the analysis results, the rating assessment of HAI’s detection and registration quality in Ural and Siberian Federal Districts and the ranking of subjects taking into account the functioning activities of the HAI epidemiological surveillance system were conducted. The information presented in the article is intended for employees of Rospotrebnadzor bodies and organizations, medical organizations, educational organizations, and executive authorities in healthcare.

Human dirofilariasis is a zoonosis caused by thin nematodes of Dirofilaria species (D. repens, D. immitis, D. tenuis and D. ursi) that belong to the Onchocercidae family. Larval stages of Dirofilaria usually occur in their primary hosts, such as cats and dogs, and rarely invade incidental hosts — human. The study described a clinical case of a patient with skull base meningioma who was admitted to the neurosurgical department for elective surgical treatment. The dense nodule was palpated in the subcutaneous tissue of the frontal area of the head. During the removal of the subcutaneous node, a thin white alive worm was evacuated during the surgery. The microscopical examination verified D. repens. Human dirofilariasis is a rare disease in the Republic of Tatarstan, Russia. However, the number of dirofilariasis cases is increasing in the recent years. Thirty-seven patients with dirofilariasis were diagnosed in the Republic of Tatarstan from 2010 to 2022.