Abstract 12180: Comparison of Hemodynamically Measured Systemic Arterial Pulsatility Index With Measured Aortic Pulsatility on Gated Computed Tomography in Pre-TAVR Patients

Introduction:Systemic Arterial Pulsatility Index, (SAPi), [systemic pulse pressure/left ventricular end diastolic pressure (LVEDP)] has emerged as a useful prognostic tool in many cardiac diseases. We aimed to compare this hemodynamically derived index of pulsatility with directly measured pulsatility on gated CT imaging (SAPi-CT) in pre-TAVR patients, as well as with SAPi calculated from pre-TAVR transthoracic echocardiography (SAPi-TTE). We also evaluated the correlation of each of these measurements with the patients’ pre-TAVR 5-meter walk times (5MWT).Methods:The three SAPi were calculated as; SAPi-Cath using invasive hemodynamics, SAPi-TTE using the Nagueh formula (PCWP = 1.24 X (E/e’) +1.9) as surrogate for LVEDP, and SAPi-CT by calculating the difference in aortic cross-sectional area during systole and diastole on gated CT imaging at the level of the ascending aorta (the level of right pulmonary artery). Correlational comparisons of these methods with each other and with pre-TAVR 5MWT were made using R software.Results:Among 110 patients studied (mean age 77.3, 56% female), the mean SAPi-Cath, SAPi-TTE, and cross-sectional area on CT were 3.15, 3.03, and 53.83mm2respectively. There was a significant positive correlation between SAPi-Cath and SAPi-CT [(r=0.275, p=0.0073); shown in Figure 1a], but not between SAPi-Cath and SAPi-TTE (r=0.163, p=0.1852). SAPI-Cath was significantly associated 5MWT (r=0.287, p=0.0064); shown in Figure 1b. Using a linear regression, the relationship between SAPi-Cath and the 5MWT is estimated to be: 5MWT=4+SAPI. SAPi-CT did not correlate with 5MWT (r=0.021, p=0.8478).Conclusions:SAPi-Cath and SAPi-CT showed a significant positive correlation. Only SAPi-Cath showed a significant correlation with 5MWT. Given the known prognostic value of SAPi-Cath, future studies utilizing imaging variables should be performed to enhance prognostication with multi-modal data.