Cardiopulmonary exercise testing (CPET) provides a comprehensive assessment of functional capacity by measuring key physiological variables including oxygen consumption ( \(VO_2\) ), carbon dioxide production ( \(VCO_2\) ), and pulmonary ventilation (VE) during exercise. Previous research has identified peak \(VO_2\) and \(VE/VCO_2\) ratio as robust predictors of mortality risk in chronic heart failure (CHF) patients as well as in congenital heart disease (CHD). This study utilises CPET variables as surrogate mortality endpoints for patients with CHD. To our knowledge, this represents the first successful implementation of an advanced machine learning approach that predicts CPET outcomes by integrating electrocardiograms (ECGs) with information derived from clinical letters. Our methodology began with extracting unstructured patient information from clinical letters using natural language processing techniques, organising this data into a structured database. We then digitised ECGs to obtain quantifiable waveforms and established comprehensive data linkages. The core innovation of our approach lies in exploiting the Riemannian geometric properties of covariance matrices derived from both 12-lead ECGs and clinical text data to develop robust regression and classification models. Through extensive ablation studies, we demonstrated that the integration of ECG signals with clinical documentation, enhanced by covariance augmentation techniques in Riemannian space, consistently produced superior predictive performance compared to conventional approaches.
