Multimodal model for predicting exercise-induced pulmonary hypertension validated by invasive exercise hemodynamics: a prospective study

Exercise-induced pulmonary hypertension (EiPH) represents an early stage of pulmonary vascular disease that remains challenging to identify noninvasively, particularly in patients with borderline resting haemodynamics. We aimed to develop and validate a multimodal non-invasive model integrating clinical characteristics, exercise echocardiography, and cardiopulmonary exercise testing (CPET) to accurately predict invasively confirmed EiPH. This prospective cohort study consecutively enrolled adult patients who presented with exercise limitation following chronic pulmonary artery thrombosis or who exhibited increased tricuspid regurgitation velocity on transthoracic echocardiography. All patients underwent comprehensive clinical evaluation, stress echocardiography, CPET, and invasive exercise right heart catheterization as the diagnostic gold standard. To minimize the impact of physiological variability on the results, all three tests were conducted at the same time. Feature selection was conducted sequentially using Spearman correlation analysis, statistical testing, and LASSO regression to identify core features associated with EiPH. Subsequently, a logistic regression model with elastic net regularization was used. Five-fold cross-validation and grid search were employed to optimize model parameters and evaluate the diagnostic performance of different models. In addition, the DeLong test was used to assess whether the AUC differed significantly between models. Finally, subgroup analyses were performed to validate the robustness of the model. The study included a total of 78 patients, comprising 34 cases of EiPH and 44 cases without EiPH. Patients in the EiPH group were significantly older (68.5 vs. 51.5 years, P < 0.001) and demonstrated reduced exercise capacity, impaired pulmonary function, abnormal right ventricular function, and increased pulmonary vascular resistance. After integrating multimodal data from clinical features, stress echocardiography, and cardiopulmonary exercise testing, the Clinical+Echo+CPET model achieved the best performance, with an AUC of 0.951 (95% CI: 0.902–0.987), an accuracy of 0.871, and a Brier score of 0.128, indicating strong discriminative ability and good calibration. The model maintained high stability in the internal validation cohort, with an AUC of 0.900, a sensitivity of 0.833, and a specificity of 0.700. The DeLong test showed that the multimodal model (Clinical+Echo+CPET) had superior discriminative performance compared with the unimodal models (Clinical, Echo, or CPET). Subgroup analysis demonstrated that the model maintained good diagnostic performance across different age and sex groups. A non-invasive multimodal model integrating clinical indicators, exercise echocardiography, and cardiopulmonary metabolic parameters can reliably identify EiPH.