The quest for load-independent left ventricular chamber properties: exploring the normalized pressure-volume loop.

Left ventricular (LV) pressure-volume ( P - V ) loop analysis is the gold standard for chamber function assessment. To advance beyond traditional P-V and pressure phase plane (d P /dt-P) analysis in the quest for novel load-independent chamber properties, we introduce the normalized P - V loop. High-fidelity LV pressure and volume data (161 P-V loops) from 13 normal control subjects were analyzed. Normalized LV pressure ( P N ) was defined by 0 ≤ P (t) ≤ 1. Normalized LV volume ( V N ) was defined as V N = V ( t )/ V diastasis , since the LV volume at diastasis ( V diastasis ) is the in-vivo equilibrium volume relative to which the LV volume oscillates. Plotting P N versus V N for each cardiac cycle generates normalized P-V loops. LV volume at the peak LV ejection rate and at the peak LV filling rate (peak -d V /dt and peak +d V /d t , respectively) were determined for conventional and normalized loops. V N at peak +d V /dt was inscribed at 64 ± 5% of normalized equilibrium (diastatic) volume with an inter-subject variation of 8%, and had a reduced intra-subject (beat-to-beat) variation compared to conventional P-V loops (9% vs. 13%, respectively; P  < 0.005), thereby demonstrating load-independent attributes. In contrast, V N at peak -d V /dt was inscribed at 81 ± 9% with an inter-subject variation of 11%, and had no significant change in intra-subject (beat-to-beat) variation compared to conventional P-V loops (17% vs. 17%, respectively; P  = 0.56), therefore failing to demonstrate load-independent tendencies. Thus, the normalized P-V loop advances the quest for load-independent LV chamber properties. V N at the peak LV filling rate (≈sarcomere length at the peak sarcomere lengthening rate) manifests load-independent properties. This novel method may help to elucidate and quantify new attributes of cardiac and cellular function. It merits further application in additional human and animal physiologic and pathophysiologic datasets.