Cardiopulmonary Exercise Test in Patients with Hypertension: Focused on Hypertensive Response to Exercise

Cardiopulmonary exercise responses in patients with non-ischemic heart failure and a mildly reduced ejection fraction

Background: The cardiopulmonary response to exercise in patients with heart failure exhibiting a mildly reduced ejection fraction (41% ≤ EF ≤ 49%) remains unclear. Materials & methods: A total of 193 consecutive patients with heart failure (excluding those with coronary artery disease) who underwent cardiopulmonary exercise (CPX) tests were examined. CPX variables were compared among patients with reduced, mildly reduced, and preserved EF. Results: The CPX test responses of patients with mildly reduced EF were similar to those of patients with reduced or preserved EF; however, increases in systolic blood pressure during exercise differed significantly (32 ± 26, 50 ± 24, and 41 ± 31 mmHg, respectively; p = 0.016). Grip strength and an increase in systolic blood pressure during exercise were independently associated with peak oxygen uptake in patients with mildly reduced EF (β = 0.41, 0.35, respectively; p < 0.05). Conclusion: Measurements of grip strength and blood pressure during exercise are useful predictors of prognoses in patients with non-ischemic and mildly reduced EF.

Utilizing Machine Learning Techniques to Predict the Efficacy of Aerobic Exercise Intervention on Young Hypertensive Patients Based on Cardiopulmonary Exercise Testing

Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians' decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients. As such, this paper aims to explore the implications of various cardiopulmonary metabolic indicators in the field by mining patients' cardiopulmonary exercise testing (CPET) data before making treatment plans. CPET data are collected "breath by breath" by using an oxygenation analyzer attached to a mask and then divided into four phases: resting, warm-up, exercise, and recovery. To mitigate the effects of redundant information and noise in the CPET data, a sparse representation classifier based on analytic dictionary learning was designed to accurately predict the individual responsiveness to AEI. Importantly, the experimental results showed that the model presented herein performed better than the baseline method based on BP change and traditional machine learning models. Furthermore, the data from the exercise phase were found to produce the best predictions compared with the data from other phases. This study paves the way towards the customization of personalized aerobic exercise programs for young hypertensive patients.