Pulmonologiya

Nontuberculous mycobacterial pulmonary disease (NTM­PD) is clinically and radiologically heterogeneous and may mimic destructive pulmonary tuberculosis, especially in the cavitary form. The aim of the study was to assess the clinical significance of the medical history, clinical, laboratory, microbiological, and radiological features of the cavitary and nodular bronchiectatic forms of pulmonary NTM­PD. Methods. A retrospective analysis included 151 HIV­negative patients examined in Federal State Budgetary Institution “National Medical Research Center for Phthisiopulmonology and Infectious Diseases” of the Ministry of Health of the Russian Federation (2023 – 2025). The patients had both cavitary form (n = 68) and nodular bronchiectatic form (n = 83). The diagnosis was established according to American Thoracic Society/European Respiratory Society/European Society of Clinical Microbiology and Infectious Diseases/Infectious Diseases Society of America (2020) criteria with NTM species identification. Risk factors, clinical and laboratory parameters, computed tomography findings, fluorescent acid­fast bacilli (AFB) sputum smears, NTM species distribution, and the frequency of an initial diagnosis of tuberculosis before microbiological verification were compared. Associations were assessed using odds ratio (OR) with 95% confidence intervals; statistical significance was calculated using Fisher’s exact test and the Mann – Whitney U test. Results. The cavitary form was more often associated with smoking (35.3% vs 10.8%; OR 4.49; p < 0.001) and emphysema (OR 4.19; p = 0.002). The nodular bronchiectatic form was more often characterized by bilateral changes (OR 0.30; p = 0.028), bronchiectasis (OR 0.51; p = 0.048), and a treein­bud pattern (OR 0.34; p = 0.007). AFB­positive sputum smears were more frequent in the cavitary form (63.2% vs 21.7%; OR 6.21; p < 0.001). Mycobacterium avium complex predominated in both groups (61.8% and 90.4%), whereas M. kansasii was relatively more frequent in the cavitary form (25.0% vs 3.6%). An initial diagnosis of tuberculosis was made more often for the cavitary form (38.2% vs 8.4%; OR 6.72; p < 0.001). Conclusion. The two phenotypes differ in risk factors, CT patterns, and microbiological characteristics. Cavitation together with frequent AFB­positive smears in the cavitary form may lead to an initial diagnosis of tuberculosis and warrants expanded microbiological testing for NTM.

The frequency of detecting nontuberculous mycobacteria (NTM) in respiratory samples has increased in recent years, which is associated both with improvements in laboratory diagnostics and with the growing prevalence of mycobacterial diseases. At the same time, there is no standardized approach in clinical practice to the interpretation of the initial NTM isolation and indications for repeat microbiological testing, which complicates diagnosis and patient management. The aim of the study was to assess the detection rate and analyze the species composition of non­tuberculous mycobacteria (NTM) in respiratory samples from patients with suspected tuberculosis referred to a Regional State Autonomous Healthcare Institution “Tomsk Phthisiopulmonology Medical Center”. Methods. A retrospective study of microbiological assays of 254 patients referred to the Regional State Autonomous Healthcare Institution “Tomsk Phthisiopulmonology Medical Center” in 2023 – 2025 for exclusion of pulmonary tuberculosis was conducted. Patients with at least one positive NTM result from respiratory samples were included. Species identification was performed using Matrix­Assisted Laser Desorption/Ionization Time­of­Flight Mass Spectrometry (MALDI­TOF MS). Depending on the frequency of detection, patients were stratified into three groups: single detection without follow­up, unconfirmed detection upon repeat testing, and confirmed repeated detection. Statistical analysis was performed using the chi­square (χ2) test and Fisher’s exact test with Bonferroni correction. Results . In the majority of patients (78.4%), NTM were detected once or the detection was not confirmed. Repeated detection was confirmed in 21.7% of cases. Slowly growing NTM predominated (69.3%), with the Mycobacterium avium complex (MAC) being the most common (53.1%). The proportion of MAC increased with repeated detection. These findings support the need to standardize repeat microbiological testing after initial NTM detection to improve diagnostic accuracy and optimize patient management. Conclusion. The data obtained justify the need for standardization of repeated microbiological control after the first positive result in order to increase diagnostic accuracy and optimize patient routing.

The combination of asthma and bronchiectasis is associated with more severe disease, chronic sputum production, and frequent infectious exacerbations. Aim. To evaluate the effect of long­term azithromycin therapy on clinical manifestations and laboratory markers of inflammation in patients with severe asthma and concomitant bronchiectasis experiencing frequent infective exacerbations. Methods. The study included 60 adult patients with severe asthma and bronchiectasis. Patients were divided into two groups: the control group received standard asthma therapy; the main group received standard therapy combined with azithromycin at a dose of 500 mg two times a week for 6 months. All patients also received N­acetylcysteine at a dose of 600 mg/day. Symptoms, exacerbation frequency, pulmonary function tests, and laboratory inflammatory markers in blood and sputum were assessed before treatment and after 6 months. Results. Baseline characteristics were comparable between groups. After 6 months, the azithromycin group, compared to the control group, showed less severe dyspnea on the mMRC (modified Medical Research Council dyspnea scale – mMRC) scale – 2.0 (1.0 – 2.0) vs 3.0 (2.0 – 3.0), p = 0.003; higher asthma control according to ACT (Asthma Control Test) – 21.0 (18.0 – 21.0) vs 16.5 (15.0 – 17.0), p < 0.001; and a lower exacerbation rate over 6 months – 1.0 (0.0 – 2.0) vs 2.0 (1.0 – 2.0), p = 0.001. Reduced laboratory markers of inflammation were also observed: blood leukocytes (p = 0.012), neutrophils (p = 0.004), C reactive protein (p = 0.006), sputum leukocytes (p = 0.010), and the degree of sputum purulence – 1.0 (1.0 – 2.0) vs 3.0 (1.3 – 3.0), p = 0.007. Forced expiratory volume in 1 second (FEV1) was higher in the azithromycin group – 1.5 (1.1 – 1.7) L vs 1.1 (0.9 – 1.5) L, p = 0.021. Conclusion. Long­term azithromycin therapy in patients with severe asthma combined with bronchiectasis was associated with improved asthma control, reduced symptom severity, decreased exacerbation frequency, increased FEV1, and reduced sputum purulence.

The global prevalence of chronic bronchitis (CB) ranges from 3.4% to 22%, while it ranges from 10% to 20% in the Russian Federation. These variations reflect differences in definitions and the association with the chronic obstructive pulmonary disease (COPD) phenotype. Among patients with COPD, the frequency of CB reaches 14 – 74% and is associated with a more severe disease course, including accelerated lung function decline, frequent exacerbations, reduced quality of life and increased mortality. CB also occurs in individuals without airway obstruction and increases the risk of developing COPD. Overdiagnosis represents a significant challenge: up to 88.4% of patients diagnosed with CB do not meet the diagnostic criteria, whereas the true prevalence of COPD remains underestimated. Aim. This study aims to assess, through a retrospective analysis of medical records, the extent to which the diagnosis of chronic bronchitis aligns with standard criteria in real­world clinical practice, and to analyze potential reasons for discrepancies. Methods. We conducted a retrospective cohort study of 185 medical records with a final diagnosis of chronic bronchitis from 2018 to 2025, extracting anamnestic, clinical, laboratory and instrumental data. We divided the patients into four groups: CB, chronic lung diseases, acute respiratory diseases, and other diseases. We performed statistical analysis using StatTech software. Results. The retrospective analysis confirmed the diagnosis of CB in 9 out of 185 patients (4.9%; 95% CI: 2.2 – 9.0). In 177 patients (95.1%), we revised the initial diagnosis, indicating low diagnostic accuracy for CB in clinical practice. Conclusion. The key to improving diagnostic accuracy lies not so much in applying high­technology methods, but rather in meticulously collecting the medical history and evaluating the duration of symptoms. The diagnosis of CB should remain a diagnosis of exclusion, requiring a systematic process of ruling out more common causes of chronic cough.

Chronic obstructive pulmonary disease (COPD) ranks third in the global mortality structure, with up to 80% of cases remaining undiagnosed due to the limitations of spirometry and the subjectivity inherent in interpreting clinical and laboratory­instrumental data. The heterogeneity of the disease necessitates a transition toward personalized medicine, in which artificial intelligence technologies play a key role. The aim. To analyze the current state and prospects of artificial intelligence technologies in the diagnosis, prediction, monitoring, and patient education for COPD. Methods. A search for scientific publications was conducted in the electronic databases PubMed, Cochrane Library, eLibrary.ru, and CyberLeninka for the period 2013 – 2026. Original studies focusing on the application of machine learning, deep learning, natural language processing, and generative models in COPD were selected for analysis. Results. It has been established that AI models based on neural networks achieve a differential diagnostic accuracy for COPD of up to 96.84%. Multimodal systems integrating clinical, imaging, and text data predict exacerbations (AUC – 0.77) and mortality with accuracy exceeding the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric severity classification. NLP algorithms extract information on dyspnea severity from physician notes, while sensor­based platforms (TOLIFE, RE­SAMPLE) predict exacerbations up to 7 days before a probable event. In patient education, chatbots demonstrate high response intelligibility but fall short in accuracy and adherence to readability standards, necessitating the validation of specialized AI systems. Conclusion. Artificial intelligence is forming the foundation for personalized COPD patient management, improving the accuracy of screening and disease course prediction. The main barriers to the implementation of this type of technology are a lack of external validation, algorithmic opacity, and data fragmentation. The priority is the development of explainable and validated clinical decision support systems.