Examinando por Autor "Pedemonte, Oneglio"

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    Ítem
    Average and Interindividual Effects to a Comprehensive Cardiovascular Rehabilitation Program
    (MDPI, 2023-01) Tuesta, Marcelo; Alvarez, Cristian; Pedemonte, Oneglio; Araneda, Oscar F.; Manríquez-Villarroel, Pablo; Berthelon, Paulina; Reyes, Alvaro
    Background: To describe the average effects and the interindividual variability after a comprehensive outpatient cardiovascular rehabilitation (CCR) program using concurrent exercise training prescribed according to cardiovascular risk stratification on cardiorespiratory fitness (CRF), anthropometric/body composition, quality of life and emotional health in patients of four cardiovascular disease profiles. Methods: CRF, anthropometric/body composition, quality of life, and emotional health were measured before and after a CCR and analyzed in heart valve surgery (HVS), heart failure with reduced ejection fraction (HFrEF), post-acute myocardial infarction (post-AMI), and in coronary artery disease (CAD) patients. Twenty, twenty-four, and thirty-two exercise sessions were prescribed according to mild, moderate, and severe baseline cardiovascular risk, respectively. In addition to concurrent exercise training, nutritional counseling, psychological support, and lifestyle education programs were performed. Results: The main outcomes by delta changes comparisons (Δ) revealed no significant differences at anthropometric/body composition as ΔBody fat decreases (HVS Δ−1.1, HFrEF Δ−1.0, post-AMI Δ−1.4, CAD Δ−1.2 kg) and ΔSkeletal muscle mass increases (HVS Δ+1.4, HFrEF Δ+0.8, post-AMI Δ+0.9, CAD Δ+0.9 kg), and CRF performance as ΔVO2peak increases (HVS Δ+4.3, HFrEF Δ+4.8, post-AMI Δ+4.1, CAD Δ+5.1 mL/kg/min) outcomes among HVS, HFrEF, post-AMI, and CAD (p > 0.05). Secondary outcomes showed significant pre-post delta changes in METs (HVS Δ+1.8, HFrEF Δ+0.7, post-AMI Δ+1.4, CAD Δ+1.4), and maximal O2pulse (HVS Δ+3.1, post-AMI Δ+2.1, CAD Δ+1.9). In addition, quality of life had a significant improvement in physical functioning (HVS Δ+17.0, HFrEF Δ+12.1, post-AMI Δ+9.8, CAD Δ+11.2), physical role (HVS Δ+28.4, HFrEF Δ+26.8, post-AMI Δ+25.6, CAD Δ+25.3), vitality (HVS Δ+18.4, HFrEF Δ+14.3, post-AMI Δ+14.2, CAD Δ+10.6) and social functioning (HVS Δ+20.4, HFrEF Δ+25.3, post-AMI Δ+20.4, CAD Δ+14.8) in all cardiovascular disease. For anxiety (HVS Δ−3.6, HFrEF Δ−2.3, post-AMI Δ−3.0, CAD Δ−3.1) and depression (HVS Δ−2.8, HFrEF Δ−3.4, post-AMI Δ−3.2, CAD Δ−2.3) significant changes were also observed. Conclusions: A CCR program that prescribes the number of exercise sessions using a cardiovascular risk stratification improves CRF, QoL, and emotional health, and the average results show a wide interindividual variability (~25% of non-responders) in this sample of four CVD profile of patients. © 2022 by the authors.
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    Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-02) Torres, Romina; Zurita, Christopher; Mellado, Diego; Nicolis, Orietta; Saavedra, Carolina; Tuesta, Marcelo; Salinas, Matías; Bertini, Ayleen; Pedemonte, Oneglio; Querales, Marvin; Salas, Rodrigo
    Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of (Formula presented.) and an (Formula presented.) of (Formula presented.), where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an (Formula presented.) up to (Formula presented.). The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase. © 2023 by the authors.