Observational studyAnnals of cardiac anaesthesia2025
Intraoperative Course of Left Ventricular Diastolic Strain in Surgical Valve Replacement for Severe Aortic Valve Stenosis.
Observational study in Annals of cardiac anaesthesia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
purposeAssessment of LV diastolic dysfunction (LVDD) poses challenges due to the dynamic changes of loading conditions in the perioperative course. Diastolic strain-based measures showed to be less load dependent, but data in aortic valve replacement (AVR) surgery remains sparse to date. Therefore, we aimed to explore the feasibility to assess these measurements and to describe the intraoperative course in this patient population.
methodsProspective observational study including 30 adult patients. Intraoperative transesophageal echocardiography (TEE) was performed after induction of anesthesia [T1], after termination of cardiopulmonary bypass [T2], and after sternal closure [T3]. TEE assessment included the evaluation of peak longitudinal strain rate during isovolumetric relaxation (SR-IVR), early (SR-E) and late (SR-A) LV filling, as well as of conventional echocardiographic measurements and LVDD grading algorithms.
resultsDiastolic strain analysis was feasible in 27 (90%) AVR patients at all time points of assessment. LV diastolic strain improved significantly after AVR [T1 vs T3] as measured by SR-IVR (0.31 s-1 (IQR 0.22; 0.38) vs. 0.4 s-1 (IQR 0.33; 0.43); P = 0.01), SR-E (1.13 s-1 (IQR 0.89;1.28) vs. 1.35 s-1 (IQR 1.10;1.52); P = 0.035), and E/SR-IVR (2.2 m (IQR 1.7;2.8) vs. 1.6 m (IQR 1.3;2.2); P = 0.013). In contrast, conventional echocardiographic measurements and grading algorithms were not able to detect these changes in the same period.
conclusionAssessment of LV diastolic strain was feasible in our group of selected AVR patients. LV relaxation and filling improved during the intraoperative course of AVR as measured by diastolic strain, while most conventional grading algorithms were not able to detect these alterations.
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