Echocaridographic assessment of pulmonary hypertension: improving the probability

Abstract Introduction Pulmonary hypertension (PH) is a cardio-pulmonary disorder characterised by raised pulmonary artery pressure (mPAP >20mmHg) leading to right ventricular failure, and death if left untreated. Transthoracic echocardiography (TTE) assessment of PH uses a probability-based approach, combining peak tricuspid regurgitant velocity with other supporting signs. We evaluated the latest ESC TTE PH guidelines in a real-world UK dataset and identified markers that may improve the sensitivity of the existing algorithm. Method Retrospective analysis of 447 patients referred for initial PH assessment. This was a select population of patients who underwent both TTE and right heart catheter (RHC) (mean time to RHC = 40±30 days). In addition to current TTE PH probability markers we analysed right ventricular free wall strain (RVFWS). Measurements were made by a single operator using Tomtec Arena 2020. Crosstabulation, ROC curve analysis and T-tests were used to compare variables between PH and no PH. Results Mean sample age was 64±15 yrs (61% female). 6.9% (n=4) of the no PH group had pulmonary mid-systolic notching, which may be due to pseudo-notching attributable to incomplete Doppler envelope. Sensitivity and specificity of current ESC TTE probability guidelines were 83%, and 65% respectively (95% C.I.). However, within the low probability group (i.e. those felt to be less likely to have PH) 62% (n=63) had confirmed PH at RHC (mPAP = 28±9). The addition of RVFWS as an additional marker to the existing supporting signs of PH (Table 1) resulted in an improvement of the sensitivity of the algorithm to 92% with a mild reduction in specificity (63%). The number of false negatives were reduced by 51%. Of these, the majority were in the low TTE probability group (n = 32). Conclusions Using current PH TTE probability algorithms classifies a significant proportion of those with PH as having a low TTE PH probability. The addition of RVFWS improves the sensitivity of the existing algorithm to 92% and reduces false negatives by 51%.