Relationship between intraventricular mechanical dyssynchrony and left ventricular systolic and diastolic performance: An in vivo experimental study.
Manuel Ignacio Monge GarcíaZhongping JianFeras HatibJos J SettlesMaurizio CecconiMichael R PinskyPublished in: Physiological reports (2023)
Left ventricular mechanical dyssynchrony (LVMD) refers to the nonuniformity in mechanical contraction and relaxation timing in different ventricular segments. We aimed to determine the relationship between LVMD and LV performance, as assessed by ventriculo-arterial coupling (VAC), LV mechanical efficiency (LV eff ), left ventricular ejection fraction (LVEF), and diastolic function during sequential experimental changes in loading and contractile conditions. Thirteen Yorkshire pigs submitted to three consecutive stages with two opposite interventions each: changes in afterload (phenylephrine/nitroprusside), preload (bleeding/reinfusion and fluid bolus), and contractility (esmolol/dobutamine). LV pressure-volume data were obtained with a conductance catheter. Segmental mechanical dyssynchrony was assessed by global, systolic, and diastolic dyssynchrony (DYS) and internal flow fraction (IFF). Late systolic LVMD was related to an impaired VAC, LV eff , and LVEF, whereas diastolic LVMD was associated with delayed LV relaxation (logistic tau), decreased LV peak filling rate, and increased atrial contribution to LV filling. The hemodynamic factors related to LVMD were contractility, afterload, and heart rate. However, the relationship between these factors differed throughout the cardiac cycle. LVMD plays a significant role in LV systolic and diastolic performance and is associated with hemodynamic factors and intraventricular conduction.
Keyphrases
- left ventricular
- aortic stenosis
- cardiac resynchronization therapy
- hypertrophic cardiomyopathy
- left atrial
- heart failure
- acute myocardial infarction
- heart rate
- mitral valve
- ejection fraction
- blood pressure
- smooth muscle
- atrial fibrillation
- machine learning
- physical activity
- big data
- electronic health record
- deep learning
- percutaneous coronary intervention
- coronary artery disease