Study Design

In this cross-sectional study of RV function, patients with acute PE were compared with control patients with PHT. Patients with acute PE ( n = 45) were retrospectively identified from administrative records and were eligible for study if echocardiography was performed within 48 hours of computed tomographic pulmonary angiography. These patients were anticoagulated, and none underwent thrombolysis. Most were hemodynamically stable, although two had documented systolic blood pressure < 90 mm Hg. Patients with documented chronic thromboembolic pulmonary hypertension were excluded. Control patients were obtained from a chronic PAH registry cohort from the Royal Hobart Hospital and Launceston General Hospital between 2005 and 2013. These latter had PAH diagnosed by right-heart catheterization and were treated with an endothelin receptor antagonist, a phosphodiesterase-5 inhibitor, intravenous or inhaled prostacyclin analogs, or their combination, in accordance with current guidelines. The primary control group comprised patients with PAH matched for age, gender, and PASP (as derived from transthoracic echocardiography) ( n = 45). Because matching is rarely perfect, an unmatched group from the same patient cohort ( n = 116) was included in a sensitivity analysis.

Standard Echocardiography

Studies were performed on commercially available GE (Vivid E9, Vivid 7, and Vivid I; GE Medical Systems, Milwaukee, WI) and Philips (iE33 and EPIQ; Philips Medical Systems, Andover, MA) equipment. The acquisition followed a standard protocol, including RV-focused views and measurement of pulsed-wave Doppler of the RV outflow tract for estimation of pulmonary vascular resistance (PVR).

All measurements were performed by a single reader. Standard RV and left ventricular (LV) measurements were performed per current American Society of Echocardiography guidelines. LV ejection fraction was measured using Simpson’s biplane method from the apical four- and two-chamber views. RV end-diastolic area (RVEDA) (square centimeters) and RV end-systolic area (square centimeters) were calculated from the apical RV-focused view. Fractional area change (FAC) was calculated as the percentage change between the RVEDA and RV end-systolic area. Peak velocities of the early (E) and late (A) diastolic filling were derived from the transmitral inflow pattern. Tissue Doppler imaging was used to determine the peak diastolic early velocity (e′) of the lateral and septal mitral annulus from the apical four-chamber view. PASP was calculated from the tricuspid regurgitation maximal signal, using the modified Bernoulli equation. Right atrial pressure was assessed via inferior vena cava collapsibility. To calculate PVR, tricuspid regurgitation maximum velocity was divided by the RV outflow time-velocity integral. The severity of tricuspid jet regurgitation was assessed using standard guidelines, on the basis of jet density, hepatic vein flow reversal, and tricuspid valve morphology.

Speckle Tracking Strain Analysis

RVFWS was assessed from an apical RV-focused view, using speckle-tracking (Image Arena; Tomtec, Unterschleissheim, Germany). The RV end-diastolic endocardial border was manually traced along the RV septal and RV free wall. These borders were then tracked automatically frame by frame throughout the cardiac cycle. Manual adjustments were performed when necessary, and regions were excluded if excessive noise or inadequate tracking was present. Peak systolic strain was obtained from the average of the three separate basal, mid and septal RVFWS traces, RV septal wall measurements were not used for analysis.

Statistical Analysis

Data were assessed for normality using the Shapiro-Wilk W test and transformed if necessary. For PVR values, the “linear trend at point” method was used for missing data. Patients were placed into equal tertiles for PVR because of an inability to normalize. Comparison of patients and control subjects was performed by matching 1:1 for age, gender, and PASP, as well as the inclusion of all control subjects into statistical models. Acuity of afterload increase was assessed as a binary variable (PAH vs PE). Associations of RV systolic function were determined using univariate and multivariate logistic regression. Anticipated associations were placed into the model (age, gender, and PASP). Incremental benefit was assessed by comparing the global χ ² value for each model. Receiver operating characteristic curves were created to determine the diagnostic ability of RV functional measurements to distinguish those with and without acute increases in RV afterload, with determination of the area under the curve (AUC). Cutoff values were obtained using the Youden index. Interoperator variability was assessed in both the chronic pulmonary hypertension ( n = 30) and PE ( n = 25) studies. Variability studies were randomly selected, with the second reader not aware of reader 1’s results or which cardiac cycle and image window was measured.

Bland-Altman analysis was used to assess the mean difference and limits of agreement, and the intraclass correlation coefficient (ICC) was calculated.

Patient Characteristics

Patient characteristics are presented in Table 1 . Patients in the PAH group were divided into 81 (50%) with idiopathic PAH, 68 (42%) with scleroderma, seven (3%) with congenital heart disease, four (2.5%) with chronic obstructive pulmonary disease and one (0.6%) with portopulmonary hypertension. The main clinical differences between PE and the PHT control subjects were in body mass index, heart rate, systolic blood pressure, and renal function. Atrial fibrillation was present in four members of the PE cohort (9%) and five members of the PAH group (3%). The severity of tricuspid regurgitation in patients with PAH was mild in 119 (74%), moderate in 27 (17%), and severe in 15 (9%).

Echocardiography

Comparison between patients with PE and control subjects showed differences in PVR (higher in patients with PE) and all RV functional indices (reduced in patients with PE) ( Table 1 ). RV size (RVEDA) was increased in patients with PE. Figure 1 shows the pattern of RV regional strain.

Strain patterns of the segments and whole RV free wall in patients with PAH compared with those with PE. Strain was consistently impaired in patients with PE compared with those with PAH.

LV ejection fraction showed a significant difference with the matched group (59 ± 10% vs 64 ± 7%, P = .03). Diastolic function between groups was similar.

McConnell Sign

Both the PE and PAH groups were evaluated for the presence of the McConnell sign. This sign was identified in 41 patients, 18 (40%) with PE and 23 (14%) with PAH ( Table 2 ). Generally, those presenting with the McConnell sign had higher heart rates, higher PVR, lower RVFWS, higher RVEDA, and lower FAC. Figure 2 shows the regional variation in strain between patients with and those without the McConnell sign. We created an RV free wall basal-mid/apical free wall ratio (RV apex/[average basal + mid] × 100). Patients who demonstrated the McConnell sign showed similar RV apical strain compared with those without this sign (59 ± 54% vs 52 ± 36%, P = .27), with the same finding when restricted to patients with PE (64 ± 78% vs 47 ± 25%, P = .33). RV strain showed the same pattern of reduction from base to apex, irrespective of afterload.

Strain patterns of the segments and whole RV free wall in patients with and without the McConnell sign. Patients with this sign demonstrated lower strain in all segments.

RV Longitudinal Function

RVFWS was significantly decreased in the PE group compared with the unmatched ( P < .001) and matched ( P < .001) groups. This pattern was noted in all segments of the RV free wall, with highly significant differences among the basal, mid, and apical segments ( P < .001) ( Figure 1 ).

The associations of RV dysfunction are shown in Table 3 . Univariate analysis showed that age, sex, PASP, acuity of afterload, RVEDA, and PVR were significantly associated with RVFWS. In multivariate models, even when taking into account PASP, acuity of afterload was still significantly associated with RV strain (odds ratio [OR], −5.7; 95% CI, −7.4 to 4.0; P < .001). When PASP was replaced by PVR, this association still held true (OR, −4.4; 95% CI, −6.0 to 2.1; P < .001). Acuity retained a significant association with global longitudinal strain when RVEDA was taken into account (OR, −3.9; 95% CI, −5.7 to 2.2; P < .001).

The incremental value of each factor for discrimination of acuity using sequential logistic regression is shown in a series of models in Table 4 . Using standard clinical predictors, heart rate, PASP, and RVEDA were significantly associated with acuity (model χ ² = 60.5, P < .001). The addition of PVR did not significantly increase the association (χ ² = 63.0, P = .12), and PVR itself was not significant in the model (OR, 0.80; 95% CI, 0.60–1.06; P = .12). However, the addition of RVFWS was significant ( P < .001) and significantly increased the strength of the model (χ ² = 70, P = .008).

Validity of RV Functional Markers over a Spectrum of Measurements

The diagnostic strength of different RV systolic functional parameters may depend on the magnitude of RV failure. Receiver operating characteristic curve analysis was performed, with groups divided on the basis of quantification of RV systolic function; group 1 contained those with mildly reduced or normal FAC values (>35%), whereas group 2 contained those with moderately or severely reduced FAC. If FAC was normal, discriminative power between variables was poor ( Table 5 ). When RV systolic function was at least moderately reduced, RVFWS retained its predictive ability (AUC = 0.77). A second analysis was performed, with groups created on the basis of RVFWS values (normal or mildly reduced, <−20%, or moderate or severe RVFWS). Similar results were seen, with RVFWS the strongest predictor (AUC = 0.69) ( Table 5 ), although some diagnostic strength was lost.

Table 5

Relationship between the diagnostic strength of different RV systolic functional parameters and the magnitude of RV failure

Variable	AUC	95% CI	P	AUC	95% CI	P
	FAC normal/mild			FAC abnormal
FAC	0.54	0.37–0.71	.65	0.43	0.28–0.57	.29
RVFWS	0.49	0.31–0.67	.89	0.76	0.62–0.90	<.001
RVEDA	0.40	0.23–0.57	.25	0.77	0.69–0.85	<.001
	RVFWS normal (<−20%)			RVFWS abnormal (>−20%)
FAC	0.70	0.26–1.0	.25	0.28	0.18–0.37	<.001
RVFWS	0.20	0.03–0.36	.076	0.69	0.59–0.78	.001
RVEDA	0.81	0.68–0.94	.07	0.60	0.50–0.70	.06

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