At present, use of invasively obtained hemodynamic variables is standard in the evaluation of PAH. Previous attempts to noninvasively diagnose this disease have relied on direct measurements of Doppler ultrasound velocity waveforms2–7
or on the existence of valve incompetence8–10
to predict MPAP. The CMO model parameters presented herein more completely quantify the shape of the velocity waveform in the MPA than previous studies of the pulmonary vasculature and, to the best of our knowledge, have never been applied as a noninvasive diagnostic of PAH. This study, which uses only simple groupings of the patient data, is a pilot investigation into the model’s utility.
As the survival rate of childhood PAH increases, so too does the need for routine noninvasive non-ionizing image-based diagnostics. This work complements previous studies by our team26,27
in providing such ultrasound-based diagnostics for PAH. While these previous studies examined specific predictors of PVR and local vascular stiffness, here our preliminary results found more generally that the CMO model parameter k
holds promise as a predictor of overall right heart function (stroke work and pulmonary flow) and global right heart afterload (PVR and PVS), and the parameter x0
may be useful as an additional predictor of flow and PVS; we propose further investigation of these parameters as useful new noninvasive diagnostic measures. Should such additional work demonstrate their utility, we believe these CMO parameters will first and foremost belong as part of an initial noninvasive assessment of a PAH patient, either alone or in concert with our other ultrasound-based diagnostics. In this capacity, they could provide more quantitative measures to this traditionally more qualitative assessment.
Vascular stiffness has been found recently as an important additional predictor of morbidity and mortality24
for PAH, the prevailing hypothesis being that vascular remodeling yields permanent stiffening of the pulmonary tree, which leads to increases in RV afterload; proximal arterial stiffness has also been shown to affect wall shear in recent computational studies.28
Changes in wall shear may also affect a reduction in vasodilator release.29–32
Of significance is the method’s relative strength in prediction of PVS; additionally, its capability to predict global PVS complements our noninvasive measure of local, proximal compliance,26
and is superior in the absence of a TR jet. Because the set includes significant predictors of both PVR and PVS, it could potentially provide a better noninvasive diagnostic for disease outcomes than previous studies in this area. Thus, further work holds promise to establish the diagnostic as a good predictor of total afterload, and subsequently, of outcomes.
This diagnostic causes minimal discomfort to the patient, may be performed routinely, and with ongoing reductions in ultrasound equipment size, may eventually become a “routine bedside” evaluation. The model’s use of Spectral data from the MPA should ease possible implementation in the clinical environment, since no unusual or non-routine imaging views are required. While this method is currently only performed as a postprocessing step, generation of the model parameters is not computationally intensive and could be readily implemented as an automatic calculation on commercial ultrasound equipment.
We hypothesized that significant differences could be found between the CMO parameters representing control and hypertensive velocity waveforms. Prior to any discussion of model parameters, we note that the two groups display significant differences in their hemodynamic variable means for MPAP, PVR, PVS, and AcT/RVET, as expected from previous studies of PAH.1,22,23
Significant differences also exist in the group means of five of the 10 model variables examined. The first three means of interest, x0
, and kx0
, all show significant increases in absolute value in the control group, and are suggestive of an overall increase in flow, velocity, and acceleration in the MPA. Analogous increases are present in hemodynamically measured quantities of cardiac index and the mean peak velocity (control, 117.0 ± 43.25 cm/s; hypertensive, 89.12 ± 23.58 cm/s: P
= .0114). The model damping c
is larger for the control group, contrary to our expectation that this parameter is comparable to flow losses in the pulmonary circuit. Clearly, such flow losses—i.e., overall pressure drops due to resistance—are larger in patients with greater PVR (i.e., that are hypertensive); thus, the connection between the model damping parameter, c
, and viscous loss through downstream arteries is not appropriate. The remaining significant parameter, kx02
, which quantifies initial system energy, increases in the control group. This suggests that the normotensive flow waveforms display more flow energy, despite the smaller mean right heart power output seen from hemodynamic quantities in this group, and points towards substantially smaller flow losses in the pulmonary circuit of such individuals. With additional clinical study and confirmation, these five measures together might be useful as initial determinants of a patient’s hemodynamic state, and, with commercial implementation of the method, could be obtained regularly in order to track hemodynamic status on a day-to-day basis. Also possible is more sensitive estimation of hemodynamic data through the use of all these significant parameters in multivariate prediction; development of such capability is ongoing.
We note that there are small but significant decreases of the hypertensive group’s AcT/RVET in both the experimental data (control, 0.659 ± 0.058; hypertensive 0.616 ± 0.082: P
= .0121) and the model data (control, 0.623 ± 0.048; hypertensive, 0.587 ± 0.073: P
= .0129). Such decreases are not as large as previously reported.2–7
However, our groups display significant differences in flow, and nearly significant differences in peak velocity, which are features noted as being not significantly different in some previous studies (flow;4,6
). These previous studies also only included hemodynamics measured under room conditions, whereas 44% of our hypertensive patients had measurements obtained only under reactivity conditions (i.e., under hypoxia, or after delivery of NO or other therapeutic agents).
There are several limitations to this study that should be recognized. First, we acknowledge that this is a pilot study, and as such, further work is needed to determine if these measures are useful for clinical decision making. Additional patient numbers and statistical work are needed to clarify the relationships shown here, to further investigate the use of the parameter set as multivariate predictors of PAH status, and to produce robust prediction equations suitable for use as clinical diagnostics. Such multivariate analysis will also attempt to improve the model’s sensitivity and specificity in prediction of PVR and PVS. Further, we note that the CMO model represents a simple, single frequency system, whereas the flow through the MPA is coupled to the right heart and distal pulmonary vasculature, which is a complex assembly capable of multi-frequency response. Thus, meaningful physics-based connections between the CMO model parameters and physiologically measured responses are tentative; however, further effort should be made to better understand the predictive relationships between model parameters and hemodynamic variables.
We conclude that the CMO model parameters provide noninvasive prediction of total right heart afterload (PVR and PVS), stroke work, and flow. These predictors and the existence of significant differences between control and hypertensive waveform parameters support the potential future use of the model as a noninvasive diagnostic of PAH.