Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality in the elderly worldwide. Over the past two decades, there has been a wealth of evidence of a close relationship between autonomic nervous system activity and cardiovascular mortality, including sudden cardiac death. Heart rate variability (HRV), derived from photoplethysmographic (PPG) signals, is increasingly recognized as a promising non-invasive digital marker for evaluating autonomic nervous system function and stratifying CVD risk. The application of machine learning algorithms to PPG-derived HRV analysis offers a promising approach for improving CVD risk stratification and facilitating the development of personalized medicine strategies. Background/Objectives: To evaluate the potential of heart rate variability indicators in predicting the risk of developing CVD in individuals aged 65 years and older. Methods: The study involved individuals aged 65 years and older, divided into two groups: those with a risk of developing CVD (n = 54) and those without risk (n = 46). The first stage included a questionnaire as well as anthropometric and hemodynamic measurements. At the second stage, a PPG was performed using the Eldar computer photoplethysmograph and Eldar-Vario software, followed by an analysis of time-domain and spectral HRV parameters. Statistical data analysis was conducted using the SPSS Statistics 22.0 software package, focusing on the evaluation of associations between HRV indicators and the presence of CVD. Interpretable machine learning models were developed using logistic regression and a random forest algorithm within a nested cross-validation framework. In addition to the discriminatory characteristics, Brier score, LogLoss, calibration analysis, error matrices, permutation importance, and SHAP interpretation were analyzed in the study. Results: In patients with cardiovascular diseases, a statistically significant decrease in heart rate variability was revealed: SDNN by 2 times (26 [Q1–Q3: 15, 35] ms), pNN50 by 3.5 times (4 [3, 5]%), TINN by 5 times (31 [20, 51] ms), and HRV by 2.5 times (6 [4, 8.7]). In addition, a decrease was seen in the spectral components of VLF by one-fold (2450 [Q1–Q3: 2450, 4500] ms²), LF by four-fold (750 [750, 1500] ms²) and HF by five-fold (450 [450, 750] ms²) (p < 0.05). At the same time, there was a significant increase in the VLF/HF and LF/HF ratios, which indicates a predominance of sympathetic activity. According to the results of the correlation analysis, statistically significant associations of HRV indicators with age, physical activity level, body mass index and systolic blood pressure were revealed. The results of machine learning also revealed the association of HRV with arterial hypertension, physical activity and BMI. The best final results were demonstrated by a random forest model with a combined set of clinical and HRV signs of HF and RMSSD (ROC-AUC was 0.9988). The signs of heart rate variability obtained by photoplethysmography demonstrated additional prognostic value in relation to clinical signs. PPG-derived HRV features demonstrated additional discriminatory value for cardiovascular risk stratification. Conclusions: The obtained data demonstrate a close association between the risk of developing cardiovascular disease and autonomic nervous system dysfunction. The decrease in heart rate variability is most pronounced in elderly individuals with existing cardiovascular disease and can be considered a potential tool for developing diagnostic, prognostic, and risk stratification strategies. The use of machine learning demonstrated that heart rate variability features obtained using photoplethysmography improve diagnostic prognostication and classification of cardiovascular diseases compared to models based solely on clinical data.

Background: Effective empirical therapy and antimicrobial stewardship rely on up-to-date local data on respiratory pathogens and antimicrobial resistance trends. In Kazakhstan, contemporary microbiological surveillance remains limited, particularly for hospitalized patients with chronic pulmonary disease (COPD) and pneumonia. This study aimed to characterize the microbial profiles and antimicrobial susceptibility patterns in respiratory specimens from these patient groups in Almaty, Kazakhstan. Methods: This retrospective observational study included adult patients hospitalized at a tertiary referral center between January 2024 and December 2025. Respiratory specimens (sputum and bronchoalveolar lavage) underwent bacterial culture, species identification using MALDI-TOF MS, and antimicrobial susceptibility testing via EU-CAST disk diffusion. Susceptibility was interpreted according to EUCAST categories (S, I, R). Group comparisons were performed using Pearson’s chi-square or Fisher’s exact test with Benjamini-Hochberg correction. Results: Among 713 patients (255 COPD exacerbation; 458 pneumonia), Streptococcus pyogenes was the most frequently isolated organism in both groups, followed by viridans group streptococci (VGS) and Streptococcus pneumoniae. Gram-negative isolates included most frequently Klebsiella pneumoniae, Klebsiella aerogenes, and Pseudomonas aeruginosa. After correction for multiple testing, bacterial distribution did not differ significantly between COPD and pneumonia. Streptococcal isolates demonstrated consistently high susceptibility to β-lactams, while susceptibility to macrolides, clindamycin, and doxycycline was lower. Gram-negative organisms demonstrated more variable resistance profiles. MDR phe-notypes were common among K. pneumoniae (16.7% COPD; 31.0% pneumonia) and P. aeruginosa (22.2% COPD; 17.9% pneumonia), with no significant between group dif-ferences. Conclusions: COPD exacerbation and pneumonia patients showed broadly similar bacterial profiles, whereas antimicrobial susceptibility varied substantially across species. High β-lactam activity against streptococci contrasted with complex resistance patterns and notable MDR/XDR phenotypes among Gram-negative organisms. These findings highlight the importance of ongoing institution-specific surveillance to support antimicrobial stewardship and guide empirical treatment decisions.