Dataset Information

Impact of Pulse Wave Velocity and Parameters Reflecting Android Type Fat Distribution on Left Ventricular Diastolic Dysfunction in Patients with Chronic Coronary Syndromes.

ABSTRACT:

Background

Left ventricular diastolic dysfunction (LVDD) is caused by a decreased left ventricle relaxation and is associated with an increased risk of symptomatic heart failure (HF) and excessive mortality.

Aim

To evaluate the frequency and factors related to LVDD in the population with chronic coronary syndromes (CCS).

Methods

200 patients (mean age 63.18 ± 8.12 years, 75.5% male) with CCS were included. LVDD was diagnosed based on the recent echocardiography guidelines.

Results

LVDD was diagnosed in 38.5% of CCS population. From the studied factors, after adjustment for age, sex, and N-terminal pro-brain natriuretic peptide (NT-proBNP), LVDD associated positively with android/gynoid (A/G) fat mass ratio, left ventricular mass index (LVMI), and negatively with Z-score and left ventricular ejection fraction (LVEF). In stepwise backward logistic regression analysis, the strongest factors associated with LVDD were pulse wave velocity value, handgrip strength and waist to hip ratio (WHR).

Conclusions

LVDD is common among CCS patients and it is associated with parameters reflecting android type fat distribution regardless of NT-proBNP and high-sensitivity troponin T concentrations. Deterioration in diastolic dysfunction is linked with increased aortic stiffness independently of age and sex. Further studies evaluating the effects of increasing physical fitness and lowering abdominal fat accumulations on LVDD in CCS patients should be considered.

SUBMITTER: Paniczko M

PROVIDER: S-EPMC7761650 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Impact of Pulse Wave Velocity and Parameters Reflecting Android Type Fat Distribution on Left Ventricular Diastolic Dysfunction in Patients with Chronic Coronary Syndromes.

Paniczko Marlena M Chlabicz Małgorzata M Jamiołkowski Jacek J Sowa Paweł P Szpakowicz Małgorzata M Łapińska Magda M Kondraciuk Marcin M Ptaszyńska-Kopczyńska Katarzyna K Raczkowski Andrzej A Szpakowicz Anna A Kamiński Karol Adam KA

Journal of clinical medicine 20201203 12

<h4>Background</h4>Left ventricular diastolic dysfunction (LVDD) is caused by a decreased left ventricle relaxation and is associated with an increased risk of symptomatic heart failure (HF) and excessive mortality.<h4>Aim</h4>To evaluate the frequency and factors related to LVDD in the population with chronic coronary syndromes (CCS).<h4>Methods</h4>200 patients (mean age 63.18 ± 8.12 years, 75.5% male) with CCS were included. LVDD was diagnosed based on the recent echocardiography guidelines.< ...[more]

PMID: 33287316

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Project description:BackgroundPhase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown.ObjectiveThis study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP).MethodsConsecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches.ResultsThe study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82.ConclusionThe phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.

Dataset Information

Impact of Pulse Wave Velocity and Parameters Reflecting Android Type Fat Distribution on Left Ventricular Diastolic Dysfunction in Patients with Chronic Coronary Syndromes.

Background

Aim

Methods

Results

Conclusions

Publications

Impact of Pulse Wave Velocity and Parameters Reflecting Android Type Fat Distribution on Left Ventricular Diastolic Dysfunction in Patients with Chronic Coronary Syndromes.

Similar Datasets

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