ABSTRACT
The infection with the SARS-CoV-2 virus is associated with numerous systemic involvements. Besides the severe respiratory injuries and cardiovascular complications, it became obvious early on that this disease carries an increased risk of thromboembolic events, but a higher propensity for bleedings as well. We researched the medical literature over significant PubMed published articles debating on the prevalence, category of patients, the moment of occurrence, and evolution of venous thromboembolism (VTE), but also of venous and arterial "in situ" thrombosis (AT), and hemorrhagic events as well. Most researchers agree on an increased prevalence of thromboembolic events, ranging between 25 and 31% for VTE, depending on the analyzed population. For AT and hemorrhagic complications lower rates were reported, namely, about 2-3%, respectively, between 4.8 and 8%, occurring mostly in older patients, suffering from moderate/severe forms of COVID-19, with associated comorbidities. It is important to mention that patients suffering from hemorrhages frequently received thromboprophylaxis with anticoagulant drugs. As a consequence of thromboembolic and hemorrhagic complications which are both important negative prognostic factors, the evolution of patients infected with the SARS-CoV-2 virus is aggravated, determining an augmented morbidity and mortality of this population.
ABSTRACT
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.
