Background and Objectives: Autonomic nervous system (ANS) dysfunction is present in early stages of alcohol abuse and increases the likelihood of cardiovascular events. Given the nonlinear pattern of dynamic interaction between sympathetic nervous system (SNS) and para sympathetic nervous system (PNS) and the complex relationship with lifestyle factors, machine learning (ML) algorithms are best suited for analyzing alcohol impact over heart rate variability (HRV), because they allow the analysis of complex interactions between multiple variables. This study aimed to characterize autonomic nervous system dysfunction by analysis of HRV correlated with cardiovascular risk factors in young individuals by using machine learning. Materials and Methods: Total of 142 young adults (28.4 ± 4.34 years) agreed to participate in the study. Alcohol intake and drinking patterns were assessed by the AUDIT (Alcohol Use Disorders Identification Test) questionnaire and the YAI (Yearly Alcohol Intake) index. A short 5-min HRV evaluation was performed. Post-hoc analysis and machine learning algorithms were used to assess the impact of alcohol intake on HRV. Results: Binge drinkers presented slight modification in the frequency domain. Heavy drinkers had significantly lower time-domain values: standard deviation of RR intervals (SDNN) and root mean square of the successive differences (RMSSD), compared to casual and binge drinkers. High frequency (HF) values were significantly lower in heavy drinkers (p = 0.002). The higher low-to-high frequency ratio (LF/HF) that we found in heavy drinkers was interpreted as parasympathetic inhibition. Gradient boosting machine learner regression showed that age and alcohol consumption had the biggest scaled impact on the analyzed HRV parameters, followed by smoking, anxiety, depression, and body mass index. Gender and physical activity had the lowest impact on HRV. Conclusions: In healthy young adults, high alcohol intake has a negative impact on HRV in both time and frequency-domains. In parameters like HRV, where a multitude of risk factors can influence measurements, artificial intelligence algorithms seem to be a viable alternative for correct assessment.

Background: Adjustment to stress requires the involvement of coping strategies. Using maladaptive coping strategies may precipitate the onset or recurrence of psychiatric disorders. On the other hand, the illness itself may alter the coping mechanisms of an individual. This study aims to identify the coping strategies in patients with bipolar disorder (BD) and major depressive disorder (MDD) and determine the correlation between coping strategies and clusters of psychiatric symptoms. Material and Methods: Socio-demographic and clinical data were analyzed for 30 inpatients with BD and 30 inpatients with MDD. The SCL-90 questionnaire and COPE inventory were filled in by the participants. Results: Compared to the general population, the patients with BD had lower scores for functional coping strategies and higher scores for one dysfunctional coping strategy. The patients with MDD had lower scores for all active functional and two passive functional coping strategies. By contrast, they presented higher scores on one passive functional and one dysfunctional coping strategy. Positive reinterpretation and growth were negatively correlated with somatization, depression, anxiety, interpersonal sensitivity, hostility, and psychoticism. Behavioral disengagement was positively correlated with depression, anxiety, somatization, interpersonal sensitivity, and psychoticism. Substance use was positively correlated with the number of episodes. Distinct coping mechanisms were associated with certain symptom clusters. Conclusions: Although dysfunctional coping strategies may predispose to psychiatric disorders, in our study, they appear to be state-dependent rather than trait-dependent.

Background: Many patients with heart failure with reduced ejection fraction (HFrEF) remain undertreated in routine practice. Delayed treatment intensification, poor adherence, and fragmented follow-up are common barriers. Low-cost digital support may help reduce this implementation gap. Objective: This study evaluated whether a simple digital support pathway was associated with better 6-month treatment adherence and guideline-directed medical therapy (GDMT) optimization in ambulatory patients with stable HFrEF. Methods: This single-center matched cohort study compared a prospective digital-support cohort with a historical usual-care cohort. The intervention combined smartphone-based telemanagement, home blood pressure and heart-rate reporting, daily weight surveillance, and scheduled video consultations. The co-primary endpoints were treatment adherence at 6 months and GDMT optimization, assessed by change in foundational HFrEF drug classes and by a prespecified exploratory GDMT optimization score. Results: After 1:1 propensity-score matching, 200 patients were included, with 100 patients in each cohort. Treatment adherence at 6 months was higher in the digital-support cohort than in usual care (82.0% vs. 64.0%, p = 0.004). The intervention cohort also had more frequent class addition, more dose escalation, a greater increase in foundational drug classes, and a larger improvement in GDMT optimization score (all p < 0.001). Heart failure hospitalization and the composite of heart failure hospitalization or all-cause death were less frequent in the digital-support cohort, but these clinical outcomes were exploratory. Conclusions: A pragmatic low-cost digital support pathway was associated with better adherence and more complete GDMT optimization in ambulatory patients with HFrEF. The findings support further prospective multicenter evaluation.

Introduction: Optimization of guideline-directed medical therapy in chronic heart failure remains challenging in real-world practice, particularly outside settings with routine cardiopulmonary exercise testing. In this context, cardiovascular rehabilitation can improve functional capacity, symptoms, and quality of life, while structured follow-up may also facilitate treatment adjustment. We therefore evaluated whether exposure to a structured multimodal functional assessment pathway, embedded within a more intensive follow-up model, was associated with pharmacological optimization and functional change in chronic heart failure. Methods: We conducted a retrospective, single-center cohort study including adults with chronic heart failure with reduced or mildly reduced ejection fraction managed in a tertiary university clinic. Patients were classified according to documented exposure to an integrated pathway that combined standardized 6 min walk testing, heart rate dynamics, oxygen saturation response, perceived exertion, validated quality-of-life assessment, and prespecified interim reassessment, versus usual care. The integrated pathway involved more frequent clinical contact than usual care. The primary outcome was change in 6 min walk distance over 6 months. Secondary outcomes included changes in heart rate recovery, oxygen saturation nadir, Borg perceived exertion score, quality-of-life score, intensity of guideline-directed medical therapy, treatment intensification rates, and heart failure hospitalization. Results: The study included 250 patients with comparable baseline demographic and clinical characteristics. Patients managed within the structured pathway showed greater improvement in 6 min walk distance at 6 months than those receiving usual care, together with more pronounced improvement in secondary functional parameters and quality-of-life scores. Pharmacological optimization, reflected by higher uptake and intensification of guideline-directed medical therapy, also occurred more frequently in the structured pathway group. The integrated group, however, also had higher follow-up intensity, which limits causal interpretation of the observed between-group differences. Conclusions: In this real-world heart failure cohort, exposure to a structured care pathway combining repeated multimodal functional profiling with closer follow-up was associated with greater functional improvement and more intensive pharmacological optimization. These findings should be interpreted as pathway-level associations rather than proof that functional assessment alone drove benefit, and they require prospective validation.