Background and objectives: Secondary prevention after acute coronary syndrome (ACS) is essential to reduce cardiovascular mortality and hospital readmission, ensuring patients return to normal with an improved quality of life. Thus, we investigate the benefits of a comprehensive cardiac rehabilitation (CR) program on lifestyle, risk factors and adherence to guideline-directed medical therapy (GDMT) in patients after ACS and myocardial revascularization through coronary artery by-pass grafting (CABG) or percutaneous coronary intervention (PCI). Methods: This is a prospective, longitudinal study in consecutive post-CABG or PCI patients after ACS that participated or not in a comprehensive CR. Cardiovascular risk factors, quality of life and adherence to GDMT were analyzed in terms of assessing the benefit of 12 months of comprehensive CR on reaching guidelines secondary prevention targets. Results: At the inclusion in comprehensive CR of all patients (n = 480), 85% had hypertension; 86% had elevated total cholesterol values; 69% were characterized by metabolic syndrome; 43% were obese; 31% were active smokers and 29% had type 2 diabetes mellitus. Only 26.66% (n = 128) followed the entire program for 12 months. No statistically significant differences in the prescription of GDMT at hospital discharge after myocardial revascularization between the CR (+) group (n = 128) versus CR (−) group (n = 352) (p > 0.05) were observed. After 12 moths, a significant adherence to GDMT in the CR (+) group vs. CR (−) group was recorded, as follows: antiplatelet agents (100% versus 96%, p = 0.001), beta-blockers (99% versus 92%, p = 0.02), ACE inhibitors/ARAB (89% versus 79%, p = 0.04), lipid-lowering drugs (100% versus 89%, p = 0.001). In total, 82% of the CR (+) patients had a significantly higher adherence at GDMT (82% versus 64%, p = 0.001). At 12 moths, the CR (+) group was characterized by significantly lower values than at the inclusion but some values still increased: systolic blood pressure (139.25 + 19.20 mmHg (p < 0.03)), total cholesterol (171.07 + 48.59 mg/dL (p = 0.0001)) and LDL-cholesterol (102.83 + 41.30 mg/dL (p = 0.009)). At the same time, the analysis of psychosocial factors using the HAD questionnaire revealed a statistically significant improvement in anxiety and depression scores: HAD-A score (9.1 ± 3.7 at T0 vs. 7.1 ± 4.2 at T1, p = 0.001) and HAD-D score (7.7 ± 3.19 at T0 vs. 6.4 ± 4.3 at T1, p = 0.003). A multivariable analysis, following GDMT, showed the actual value or information and training of patients regarding optimal cardiovascular risk factor control was independently associated with lower values of systolic blood pressure (R² = 0.48), diastolic (R² = 0.38), serum glucose (R² = 0.48), glycated hemoglobin (R² = 0.50), total cholesterol (R² = 0.31), LDL-cholesterol (R² = 0.30), HDL-cholesterol (R² = 0.19) and serum triglycerides (R² = 0.20). Conclusion: The twelve-month participation of post-ACS patients in comprehensive CR resulted in excellent post-revascularization management, as well as good adherence to guideline-directed medical therapy, provided further confirmation of the benefit of secondary prevention. Despite high adherence to drug treatments, targets for blood pressure, total cholesterol and LDL-cholesterol are inadequately achieved. Therefore, in the era of personalized medicine, patients with ACS should benefit from specific, comprehensive cardiovascular recovery programs that contain physiotherapists, psychologists, nutritionists and an experienced cardiologist in cardiovascular rehabilitation.

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.