Introduction. The traditional surveillance system for influenza and ARVI provides a general description of epidemics, but does not provide information on the age-related characteristics of the etiology and clinical peculiarities of severe acute respiratory diseases (SARI) in hospitalized patients. Aim. To monitor the etiology of SARI in hospitalized children and adults, assessing the impact of the COVID-19 pandemic on this process. Materials and methods. Standardized clinical and laboratory monitoring of SARI among 18,458 hospitalized patients was carried out in hospitals in three cities of Russia with weekly PCR detection of 11 types of pathogens. Results. According to the investigation of hospitalized patients with SARI for the period from 2018 to 2023, the viral etiology of respiratory diseases was deciphered in 58.3% of cases. Weekly monitoring showed a change in the etiological mosaic of SARI pathogens during the SARS-CoV-2 pandemic with a sharp decrease in the frequency of detection of influenza and respiratory syncytial virus (RSV) during the 2020–2021 season against the background of a significant increase of metapneumovirus and rhinovirus infections in children. During the 2022-2023 season an increase in the proportion of RSV infection in children under 6 years of age (up to 36.2%) was noted against the background of a significant decrease in the frequency of SARS-CoV-2. In the intensive care units (ICU), RSV infection was most often in children during the post-pandemic period (up to 30.1–53.6% of positive cases, p 0.001); in adults, SARS-CoV-2 was mostly detected (76,5–100% of cases, p 0.001). Conclusion. Hospital surveillance data significantly complements the epidemiological information obtained in the traditional surveillance system. Monitoring of infections has shown a continuously changing etiological infrastructure of SARI, with the disappearance of influenza and RSV during the COVID-19 pandemic and their return to circulation in the post-pandemic period.

Introduction. In the context of a high load on all links in the structure of providing medical care to patients with COVID-19, solving the issue of effective triage of patients seems to be extremely urgent. The duration of inpatient treatment is one of the most objective and unambiguously interpreted indicators that can be used to indirectly assess the severity of the patient’s condition.Objective. Develop a machine learning model to predict the duration of inpatient care for patients with COVID-19 based on routine clinical indicators assessed at the prehospital stage.Materials and methods. A total of 564 patients were examined with diagnoses: U07.1 COVID-19, virus identified (n = 367) and U07.2 COVID-19, virus not identified (n = 197). The study included 270 patients, of whom in 50.37% of patients the duration of inpatient treatment did not exceed 7 days, in 49.63% of patients the duration of inpatient treatment was more than 10 days. Eleven clinical parameters were chosen as the most important predictors for predicting the duration of inpatient treatment: age, height and weight of the patient, SpO₂ level, body temperature, body mass index, pulse rate, number of days from the onset of illness, respiratory rate, systolic and diastolic arterial pressure.Results. The accuracy of our machine learning model for predicting the duration of inpatient treatment more than 10 days was 83.75% (95% CI: 73.82–91.05%), sensitivity — 82.50%, specificity — 85.00%. AUC = 0.86.Conclusion. The method developed by us based on machine learning is characterized by high accuracy in predicting the duration of inpatient treatment of patients with COVID-19, which makes it possible to consider it as a promising new tool to support medical decisions on further tactics of patient management and to resolve the issue of the need for hospitalization.

Introduction. Acute respiratory infections (ARI) are one of the main causes of morbidity and mortality from infectious diseases in the world. Respiratory infections can be caused by pathogens of various etiologies: viruses, bacteria, mycoplasmas, etc. Rapid and accurate identification of pathogens, such as bacteria, in biological samples is an important task, for which 16S rRNA gene sequencing using new generation platforms is used.The objective was a comparative analysis of the qualitative characteristics of the oropharyngeal microbiome of healthy volunteers and patients with ARI of unknown etiology based on the 16S rRNA gene sequencing.Methods and materials. Using V3–V4 region of 16S rRNA Illumina MiSeq sequences from oropharyngeal swabs, we analyzed the microbiome of hospitalized patients with ARI symptoms and healthy patients.Results. In this study, we conducted V3–V4 region of 16S rRNA sequencing analyses of the oropharyngeal samples from 116 hospitalized patients with ARI symptoms and 81 healthy patients. Patients with ARI exhibited higher abundance of opportunistic pathogens, particularly Staphylococcus, Ralstonia, Aeribacillus, Acinetobacter baumannii, Methylobacterium-Methylorubrum, Rhodococcus equi. In the control samples, normal commensal respiratory tract microbiota, such as Neisseria, Prevotella, Fusobacterium, Veilonella was dominated.Conclusions. The microbiota samples of hospitalized patients with ARI showed a predominance of opportunistic and potentially pathogenic microbiota, while normal representatives of the respiratory tract microbiota predominate in healthy volunteers. For a more detailed analysis, data on the species composition of the microbiota is required, which can be obtained by sequencing the complete sequence of the 16S rRNA gene.