Wild type cardiac amyloidosis: is it time to order a nuclear technetium pyrophosphate SPECT imaging study?

Transthyretin (ATTR) amyloidosis is a debilitating systemic disease often associated with symptomatic cardiac involvement. Diagnosis has dramatically changed with the advent of Technetium-99 m pyrophosphate (Tc-PYP) single-photon emission computed tomography (SPECT). With the ability to diagnose ATTR amyloidosis noninvasively and offer newer therapies, it is increasingly important to identify which patients should be referred for this testing. Relative apical sparing of longitudinal strain on echocardiogram can be potentially used to screen such patients. We sought to describe electrocardiogram (ECG) and echocardiogram (TTE) findings, including relative apical sparing of longitudinal strain, in ATTR amyloidosis patients diagnosed non-invasively with 99mTc-PYP imaging. This was a single-center, retrospective study with 64 patients who underwent 99mTc-PYP imaging between June 2016 and February 2019. Relative apical longitudinal strain was calculated from left ventricular longitudinal strain (LV LS) values. No ECG parameters were meaningfully associated with of 99 m Tc-PYP positive patients. LV mass index (p = 0.001), IVSd (p < 0.001), and LVPWd (< 0.001) demonstrated a highly significant difference between positive and negative 99mTc-PYP groups. 99mTc-PYP positive patients had a higher relative apical sparing of LV LS (p < 0.001), and notably, no 99mTc-PYP negative patient had a ratio > 1.0. The finding of relative apical sparing of longitudinal strain can reliably guide clinicians in triaging which patients to consider ordering 99mTc-PYP imaging for the noninvasive diagnosis of wild type cardiac amyloidosis. A patient with clinically suggestive features and an LV LS relative apical sparing ratio > 0.8 can be considered for 99mTc-PYP imaging to evaluate for ATTR cardiac amyloidosis.

Artificial Intelligence and Echocardiography: A Primer for Cardiac Sonographers.

Artificial intelligence (AI) is emerging as a key component in diagnostic medical imaging, including echocardiography. AI with deep learning has already been used with automated view labeling, measurements, and interpretation. As the development and use of AI in echocardiography increase, potential concerns may be raised by cardiac sonographers and the profession. This report, from a sonographer's perspective, focuses on defining AI, the basics of the technology, identifying some current applications of AI, and how the use of AI may improve patient care in the future.

Left ventricular outflow tract velocity time integral outperforms ejection fraction and Doppler-derived cardiac output for predicting outcomes in a select advanced heart failure cohort.

BACKGROUND: Left ventricular outflow tract velocity time integral (LVOT VTI) is a measure of cardiac systolic function and cardiac output. Heart failure patients with low cardiac output are known to have poor cardiovascular outcomes. Thus, extremely low LVOT VTI may predict heart failure patients at highest risk for mortality. METHODS: Patients with heart failure and extremely low LVOT VTI were identified from a single-center database. Baseline characteristics and heart failure related clinical outcomes (death, LVAD) were obtained at 12 months. Correlation between clinical endpoints and the following variables were analyzed: ejection fraction (EF), pulmonary artery systolic pressure (PASP), NYHA class, renal function, Doppler cardiac output (CO), and LVOT VTI. RESULTS: Study cohort consisted of 100 patients. At the 12-month follow up period, 30 events (28 deaths, 2 LVADs) were identified. Occurrence of death and LVAD implantation was statistically associated with a lower LVOT VTI (p = 0.039) but not EF (p = 0.169) or CO (p = 0.217). In multivariate analysis, LVOT VTI (p = 0.003) remained statistically significant, other significant variables were age (p = 0.033) and PASP (p = 0.022). Survival analysis by LVOT VTI tertile demonstrated an unadjusted hazard ratio of 4.755 (CI 1.576-14.348, p = 0.006) for combined LVAD and mortality at one year. CONCLUSIONS: Extremely low LVOT VTI strongly predicts adverse outcomes and identifies patients who may benefit most from advanced heart failure therapies.