Scientific Reports (Feb 2021)

Non-linearity of end-systolic pressure–volume relation in afterload increases is caused by an overlay of shortening deactivation and the Frank–Starling mechanism

  • Moriz A. Habigt,
  • Michelle Krieger,
  • Jonas Gesenhues,
  • Maike Ketelhut,
  • Mare Mechelinck,
  • Marc Hein

DOI
https://doi.org/10.1038/s41598-021-82791-3
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 11

Abstract

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Abstract The linearity and load insensitivity of the end-systolic pressure–volume-relationship (ESPVR), a parameter that describes the ventricular contractile state, are controversial. We hypothesize that linearity is influenced by a variable overlay of the intrinsic mechanism of autoregulation to afterload (shortening deactivation) and preload (Frank-Starling mechanism). To study the effect of different short-term loading alterations on the shape of the ESPVR, experiments on twenty-four healthy pigs were executed. Preload reductions, afterload increases and preload reductions while the afterload level was increased were performed. The ESPVR was described either by a linear or a bilinear regression through the end-systolic pressure volume (ES-PV) points. Increases in afterload caused a biphasic course of the ES-PV points, which led to a better fit of the bilinear ESPVRs (r2 0.929 linear ESPVR vs. r2 0.96 and 0.943 bilinear ESPVR). ES-PV points of a preload reduction on a normal and augmented afterload level could be well described by a linear regression (r2 0.974 linear ESPVR vs. r2 0.976 and 0.975 bilinear ESPVR). The intercept of the second ESPVR (V0) but not the slope demonstrated a significant linear correlation with the reached afterload level (effective arterial elastance Ea). Thus, the early response to load could be described by the fixed slope of the ESPVR and variable V0, which was determined by the actual afterload. The ESPVR is only apparently nonlinear, as its course over several heartbeats was affected by an overlay of SDA and FSM. These findings could be easily transferred to cardiovascular simulation models to improve their accuracy.