Alkan M; Golden Jubilee National Hospital, Glasgow, Scotlan
Veldtman G; Deligianni F

Scientific Reports. 16(1), 2026 Feb 19.

Cardiopulmonary exercise testing (CPET) provides a comprehensive
assessment of functional capacity by measuring key physiological variables
including oxygen consumption ([Formula: see text]), carbon dioxide
production ([Formula: see text]), and pulmonary ventilation (VE) during
exercise. Previous research has identified peak [Formula: see text] and
[Formula: see text] 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.