Aims. To evaluate the effects of variations of total-cholesterol/HDL-cholesterol ratio and the effects of the atorvastatin on endothelial function in peripheral artery disease (PAD). Material and Methods. A prospective, randomised controlled study was carried out in 150 PAD patients. Patients randomized to the control group (n = 75) were treated with antiplatelet drugs, angiotensin-converting-enzyme inhibitors and cardiovascular-risk-factor control. Experimental group (n = 75) also received treatment with atorvastatin for a month. It was determined baseline nitrite plasma levels and total-cholesterol/HDL-cholesterol ratio and after one month of treatment in both groups. It was also analysed the correlation between the gradient of nitrite levels and the differential of total-cholesterol/HDL ratio in treatment group. Results. After a month, a reduction in nitrite levels was detected in treatment group (11.88 ± 7.8 μM versus 5.7 ± 1.8 μM, P < 0.0001). It was shown a higher decrease in nitrite plasma levels in the atorvastatin group finding lower levels assessments (5.7 ± 1.8 μM versus 13.1 ± 9.1 μM, resp., P < 0.001). A significant reduction in total-cholesterol/HDL-cholesterol ratio was observed in statin group after treatment (P < 0.0001). A strong correlation was found between the gradient of nitrite levels and the differential of total-cholesterol/HDL-cholesterol ratio in atorvastatin group (r = 0.7; P < 0.001). Conclusions. Improvement of nitrite levels are associated with decreased total cholesterol/HDL ratio values in PAD patients treated with atorvastatin.
Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and T time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.
Cardiovascular diseases are one of the most common causes of death; however, the early detection of patients at high risk of sudden cardiac death (SCD) remains an issue. The aim of this study was to analyze the cardio-vascular couplings based on heart rate variability (HRV) and blood pressure variability (BPV) analyses in order to introduce new indices for noninvasive risk stratification in idiopathic dilated cardiomyopathy patients (IDC). High-resolution electrocardiogram (ECG) and continuous noninvasive blood pressure (BP) signals were recorded in 91 IDC patients and 49 healthy subjects (CON). The patients were stratified by their SCD risk as high risk (IDC HR ) when after two years the subject either died or suffered life-threatening complications, and as low risk (IDC LR ) when the subject remained stable during this period. Values were extracted from ECG and BP signals, the beat-to-beat interval, and systolic and diastolic blood pressure, and analyzed using the segmented Poincaré plot analysis (SPPA), the high-resolution joint symbolic dynamics (HRJSD) and the normalized short time partial directed coherence methods. Support vector machine (SVM) models were built to classify these patients according to SCD risk. IDC HR patients presented lowered HRV and increased BPV compared to both IDC LR patients and the control subjects, suggesting a decrease in their vagal activity and a compensation of sympathetic activity. Both, the cardio -systolic and -diastolic coupling strength was stronger in high-risk patients when comparing with low-risk patients. The cardio-systolic coupling analysis revealed that the systolic influence on heart rate gets weaker as the risk increases. The SVM IDC LR vs. IDC HR model achieved 98.9% accuracy with an area under the curve (AUC) of 0.96. The IDC and the CON groups obtained 93.6% and 0.94 accuracy and AUC, respectively. To simulate a circumstance in which the original status of the subject is unknown, a cascade model was built fusing the aforementioned models, and achieved 94.4% accuracy. In conclusion, this study introduced a novel method for SCD risk stratification for IDC patients based on new indices from coupling analysis and non-linear HRV and BPV. We have uncovered some of the complex interactions within the autonomic regulation in this type of patient.
Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyopathy patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyopathies were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopathies, obtaining an accuracy of 85.7%.
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