Watanabe T; Department of Cardiovascular Medicine, Kyushu University,J apan.
Tohyama T; Ikeda M; Fujino T; Hashimoto T; Matsushima S;
Kishimoto J; Todaka K; Kinugawa S; Tsutsui H; Ide T

European Journal of Preventive Cardiology. 31(4):448-457, 2024 Mar 04. VI 1

AIMS: Exercise intolerance is a clinical feature of patients with heart
failure (HF). Cardiopulmonary exercise testing (CPET) is the first-line
examination for assessing exercise capacity in patients with HF. However,
the need for extensive experience in assessing anaerobic threshold (AT)
and the potential risk associated with the excessive exercise load when
measuring peak oxygen uptake (peak VO2) limit the utility of CPET. This
study aimed to use deep-learning approaches to identify AT in real time
during testing (defined as real-time AT) and to predict peak VO2 at
real-time AT.
METHODS AND RESULTS: This study included the time-series data of CPET
recorded at the Department of Cardiovascular Medicine, Kyushu University
Hospital. Two deep neural network models were developed to: (i) estimate
the AT probability using breath-by-breath data and (ii) predict peak VO2
using the data at the real-time AT. The eligible CPET contained 1472
records of 1053 participants aged 18-90 years and 20% were used for model
evaluation. The developed model identified real-time AT with 0.82 for
correlation coefficient (Corr) and 1.20 mL/kg/min for mean absolute error
(MAE), and the corresponding AT time with 0.86 for Corr and 0.66 min for
MAE. The peak VO2 prediction model achieved 0.87 for Corr and 2.25
mL/kg/min for MAE.
CONCLUSION: Deep-learning models for real-time CPET analysis can
accurately identify AT and predict peak VO2. The developed models can be a
competent assistant system to assess a patient’s condition in real time,
expanding CPET utility.