Predicting high-risk pre-capillary pulmonary hypertension: An echocardiographic multiparameter scoring index

Abstract

Abstract Background The risk stratification of pulmonary arterial hypertension proposed by the European Society of Cardiology /European Respiratory Society guidelines in 2015 and 2022 included two to three echocardiographic indicators. However, the specific value of echocardiography in risk stratification of pre-capillary pulmonary hypertension (pcPH) has not been efficiently demonstrated. Given the complex geometry of the right ventricular (RV) and influencing factors of echocardiographic parameter, there is no single echocardiographic parameter that reliably informs about PH status. We hypothesize that a multi-parameter comprehensive index can more accurately evaluate the severity of the pcPH. The purpose of this study was to develop and validate an echocardiographic risk score model to better assist clinical identifying high risk of pcPH during initial diagnosis and follow-up.Methods We studied 197 consecutive patients with pcPH. A multivariable echocardiographic model was constructed to predict the high risk of pcPH in the training set. Points were assigned to significant risk factors in the final model based on β-coefficients. We validated the model internally using bootstrapping.Results The echocardiographic score was constructed by multivariable logistic regression, which showed that pericardial effusion, right atrial (RA) area, RV outflow tract proximal diameter (RVOT-Prox), the velocity time integral of the right ventricular outflow tract (TVIRVOT) and S’ were predictors of high risk of pcPH. The area under curve (AUC) of the training set of the scoring model was 0.882 (95%CI: 0.809–0.956, p < 0.0001), and the AUC of the validation set was 0.884. A 10-point score risk score was generated, with scores ranging from 0 to 10 in the training cohort. The estimate risk of high risk of pcPH ranged from 25.1–94.6%.Conclusions The echocardiographic risk score using five echocardiographic parameters could be comprehensive and useful to predict the high risk of pcPH for initial assessment and follow-up.