Abstract
Background To date, assessment of right ventricular (RV) function in mice has relied extensively on invasive measurements. Echocardiographic advances have allowed adaptation of measures used in humans for serial, noninvasive RV functional assessment in mice. We evaluated the diagnostic performance of tricuspid annular plane systolic excursion (TAPSE), RV peak systolic myocardial velocity (s'), RV myocardial performance index (MPI), and RV fractional area change (FAC) in a mouse model of pulmonary hypertension. Methods and Results Echocardiography was performed on mice at baseline and 3 weeks after induction of pulmonary hypertension using inhaled bleomycin or saline, including adapted measures of TAPSE, s', MPI, and FAC. RV systolic pressure was measured by invasive catheterization, and RV contractility was measured as the peak slope of the RV systolic pressure recording (maximum change pressure/change time). Postmortem morphological assessment of RV hypertrophy was performed. RV systolic pressure was elevated and maximum change pressure/change time was reduced in bleomycin versus control (n=8; P=0.002). Compared with controls, bleomycin mice had reduced TAPSE (0.79±0.05 versus 1.06±0.04 mm; P=0.003), s' (21.3±1.2 versus 29.2±1.3 mm/s; P<0.001), and FAC (20.3±0.7% versus 31.0±1.3%; P<0.001), whereas MPI was increased (0.51±0.03 versus 0.37±0.01; P=0.006). All measures correlated with RV systolic pressure and maximum change pressure/change time. Intraobserver and interobserver variability were minimal. Receiver operating characteristic curves demonstrated that TAPSE (<0.84 mm), s'(<23.3 mm/s), MPI (0.42), and FAC (<23.3%) identified maximum change pressure/change time ≤2100 mm Hg/s with high accuracy. Conclusions TAPSE, s', MPI, and FAC are measurable consistently using high-resolution echocardiography in mice, and are sensitive and specific measures of pulmonary pressure and RV function. This validation opens the opportunity for serial noninvasive measures in mouse models of pulmonary hypertension, enhancing the statistical power of preclinical studies of novel therapeutics.