Central blood pressure assessment using 24-hour brachial pulse wave analysis

Maria Lorenza Muiesan, Massimo Salvetti, Fabio Bertacchini, Claudia Agabiti-Rosei, Giulia Maruelli, Efrem Colonetti, Anna Paini Clinica Medica, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy Abstract: This review describes the use of central blood pressure (BP) measurements during ambulatory monitoring, using noninvasive devices. The principles of measuring central BP by applanation tonometry and by oscillometry are reported, and information on device validation studies is described. The pathophysiological basis for the differences between brachial and aortic pressure is discussed. The currently available methods for central aortic pressure measurement are relatively accurate, and their use has important clinical implications, such as improving diagnostic and prognostic stratification of hypertension and providing a more accurate assessment of the effect of treatment on BP. Keywords: aortic blood pressure measurements, ambulatory monitoring, pulse wave analysi

Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data.

Many modifiable and non-modifiable risk factors have been associated with hypertension. However, current screening programs are still failing in identifying individuals at higher risk of hypertension. Given the major impact of high blood pressure on cardiovascular events and mortality, there is an urgent need to find new strategies to improve hypertension detection. We aimed to explore whether a machine learning (ML) algorithm can help identifying individuals predictors of hypertension. We analysed the data set generated by the questionnaires administered during the World Hypertension Day from 2015 to 2019. A total of 20206 individuals have been included for analysis. We tested five ML algorithms, exploiting different balancing techniques. Moreover, we computed the performance of the medical protocol currently adopted in the screening programs. Results show that a gain of sensitivity reflects in a loss of specificity, bringing to a scenario where there is not an algorithm and a configuration which properly outperforms against the others. However, Random Forest provides interesting performances (0.818 sensitivity - 0.629 specificity) compared with medical protocols (0.906 sensitivity - 0.230 specificity). Detection of hypertension at a population level still remains challenging and a machine learning approach could help in making screening programs more precise and cost effective, when based on accurate data collection. More studies are needed to identify new features to be acquired and to further improve the performances of ML models

Left Ventricular Diastolic Function in Hypertension: Methodological Considerations and Clinical Implications

The assessment of left ventricular (LV) diastolic function should be an integral part of a routine examination of hypertensive patient; indeed when LV diastolic function is impaired, it is possible to have heart failure even with preserved LV ejection fraction. Left ventricular diastolic dysfunction (LVDD) occurs frequently and is associated to heart disease. Doppler echocardiography is the best tool for early LVDD diagnosis. Hypertension affects LV relaxation and when left ventricular hypertrophy (LVH) occurs, it decreases compliance too, so it is important to calculate Doppler echocardiography parameters, for diastolic function evaluation, in all hypertensive patients. The purpose of our review was to discuss about the strong relationship between LVDD and hypertension, and their relationship with LV systolic function. Furthermore, we aimed to assess the relationship between the arterial stiffness and LV structure and function in hypertensive patients

Preclinical atherosclerosis, metabolic syndrome and risk of cardiovascular events

Atherosclerotic disease is a chronic disorder developing insidiously throughout the life and usually progressing to an advanced stage by the time symptoms occur. In order to realize cardiovascular (CV) prevention, the detection of asymptomatic but diseased patients is crucial for an early intervention, since in these subjects there are opportunities to alter the progression of disease and the outcome (1). However, the simply analysis of risk factors don’t permits to identify always these subjects since it doesn’t informs about the effect that risk factors (RF) had already provoked and may more provoke on the individual vasculature. Besides, the risk factors known predict can explain only the 90 percent of cardiovascular disease (CVD) and traditional algorithms for prediction of CV risk failed to predict a proportion of cardiovascular events (CVE), realizing a “risk factors prediction gap” (2). It may be explained by several reasons: the epidemiology-derived models, based on the prediction of long-term risk, may not accurately predict short-term events, they don’t take into consideration emerging and novel risk factors; risk algorithms don’t identify, among patients with neither a previous history of CVD nor an high risk for atherosclerotic disease, those who will develop acute myocardial infarction and/or sudden coronary death as first CVD manifestation, and this may be due to the fact that the factors responsible of plaque formation and growth are not necessarily the same responsible of its instability and rupture, being the latter related to inflammation, thrombosis and plaque morphology (3).So, a possible approach to evaluate the individual global cardiovascular risk with more accurateness is to identify risk factors combination that more easily produces vascular damage, or alternatively, to evaluate directly the arterial wall and its damage degree. The former approach is performed by the evaluation of metabolic syndrome, the latter by the non-invasive study of pre-ATS markers

Endothelial Function in Pre-diabetes, Diabetes and Diabetic Cardiomyopathy: A Review

Diabetes mellitus worsens cardiovascular risk profile of affected individuals. Its worldwide increasing prevalence and its negative influences on vascular walls morphology and function are able to induce the expression of several morbidities which worsen the clinical conditions of the patients getting them running towards a reduced survival curve. Although overt diabetes increases the mortality rate of individuals due to its pathogenesis, poor information are in literature about the role of pre-diabetes and family history of diabetes mellitus in the outcome of general population. This emphasizes the importance of early detection of vascular impairment in subjects at risk of developing diabetes. The identification of early stages of atherosclerotic diseases in diabetic persons is a fundamental step in the risk stratification protocols followed-up by physicians in order to have a complete overview about the clinical status of such individuals. Common carotid intima-media thickness, flow-mediated vasodilatation, pulse wave velocity are instrumental tools able to detect the early impairment in cardiovascular system and stratify cardiovascular risk of individuals. The aim of this review is to get a general perspective on the complex relationship between cardiovascular diseases onset, pre-diabetes and family history of diabetes. Furthermore, it points out the influence of diabetes on heart function till the expression of the so-called diabetic cardiomyopathy

Muscle fiber-type distribution predicts weight gain and unfavorable left ventricular geometry: a 19 year follow-up study

BACKGROUND: Skeletal muscle consists of type-I (slow-twitch) and type-II (fast-twitch) fibers, with proportions highly variable between individuals and mostly determined by genetic factors. Cross-sectional studies have associated low percentage of type-I fibers (type-I%) with many cardiovascular risk factors. METHODS: We investigated whether baseline type-I% predicts left ventricular (LV) structure and function at 19-year follow-up, and if so, which are the strongest mediating factors. At baseline in 1984 muscle fiber-type distribution (by actomyosin ATPase staining) was studied in 63 healthy men (aged 32–58 years). The follow-up in 2003 included echocardiography, measurement of obesity related variables, physical activity and blood pressure. RESULTS: In the 40 men not using cardiovascular drugs at follow-up, low type-I% predicted higher heart rate, blood pressure, and LV fractional shortening suggesting increased sympathetic tone. Low type-I% predicted smaller LV chamber diameters (P ≤ 0.009) and greater relative wall thickness (P = 0.034) without increase in LV mass (concentric remodeling). This was explained by the association of type-I% with obesity related variables. Type-I% was an independent predictor of follow-up body fat percentage, waist/hip ratio, weight gain in adulthood, and physical activity (in all P ≤ 0.001). After including these risk factors in the regression models, weight gain was the strongest predictor of LV geometry explaining 64% of the variation in LV end-diastolic diameter, 72% in end-systolic diameter, and 53% in relative wall thickness. CONCLUSION: Low type-I% predicts obesity and weight gain especially in the mid-abdomen, and consequently unfavourable LV geometry indicating increased cardiovascular risk

Serum Uric Acid Predicts All-Cause and Cardiovascular Mortality Independently of Hypertriglyceridemia in Cardiometabolic Patients without Established CV Disease: A Sub-Analysis of the URic acid Right for heArt Health (URRAH) Study

High serum uric acid (SUA) and triglyceride (TG) levels might promote high-cardiovascular risk phenotypes across the cardiometabolic spectrum. However, SUA predictive power in the presence of normal and high TG levels has never been investigated. We included 8124 patients from the URic acid Right for heArt Health (URRAH) study cohort who were followed for over 20 years and had no established cardiovascular disease or uncontrolled metabolic disease. All-cause mortality (ACM) and cardiovascular mortality (CVM) were explored by the Kaplan-Meier estimator and Cox multivariable regression, adopting recently defined SUA cut-offs for ACM (≥4.7 mg/dL) and CVM (≥5.6 mg/dL). Exploratory analysis across cardiometabolic subgroups and a sensitivity analysis using SUA/serum creatinine were performed as validation. SUA predicted ACM (HR 1.25 [1.12-1.40], p < 0.001) and CVM (1.31 [1.11-1.74], p < 0.001) in the whole study population, and according to TG strata: ACM in normotriglyceridemia (HR 1.26 [1.12-1.43], p < 0.001) and hypertriglyceridemia (1.31 [1.02-1.68], p = 0.033), and CVM in normotriglyceridemia (HR 1.46 [1.23-1.73], p < 0.001) and hypertriglyceridemia (HR 1.31 [0.99-1.64], p = 0.060). Exploratory and sensitivity analyses confirmed our findings, suggesting a substantial role of SUA in normotriglyceridemia and hypertriglyceridemia. In conclusion, we report that SUA can predict ACM and CVM in cardiometabolic patients without established cardiovascular disease, independent of TG levels