Background: Chronic obstructive pulmonary disease (COPD) manifests itself in complex ways, with local and systemic effects; because of this, a multifactorial approach is needed for disease ...evaluation, in order to understand its severity and impact on each individual. Thus, our objective was to study the correlation between easily accessible variables, usually available in clinical practice, and maximum aerobic capacity, and to determine models for peak oxygen uptake (VO.sub.2peak) estimation in COPD patients. Subjects and methods: Individuals with COPD were selected for the study. At the first visit, clinical evaluation was performed. During the second visit, the volunteers were subjected to the cardiopulmonary exercise test. To determine the correlation coefficient of VO.sub.2peak with forced expiratory volume in 1 second (FEV.sub.1) (% pred.) and the COPD Assessment Test score (CATs), Pearson or Spearman tests were performed. VO.sub.2 at the peak of the exercise was estimated from the clinical variables by simple and multiple linear regression analyses. Results: A total of 249 subjects were selected, 27 of whom were included after screening (gender: 21M/5F; age: 65.0+ or -7.3 years; body mass index: 26.6+ or -5.0 kg/m.sup.2; FEV.sub.1 (% pred.): 56.4+ or -15.7, CAT: 12.4+ or -7.4). Mean VO.sub.2peak was 12.8+ or -3.0 mLkg.sup.-1*min.sup.-1 and VO.sub.2peak (% pred.) was 62.1%+14.9%. VO.sub.2peak presented a strong positive correlation with FEV.sub.1 (% pred.), r: 0.70, and a moderate negative correlation with the CATs, r: -0.54. In the VO.sub.2peak estimation model based on the CAT (estimated VO.sub.2peak =15.148- 0.185x CATs), the index explained 20% of the variance, with estimated error of 2.826 mLkg.sub.-1*min.sub.-1. In the VO.sub.2peak estimation model based on FEV.sub.1 (estimated VO.sub.2peak =6.490+ 0.113x FEV.sub.1), the variable explained 50% of the variance, with an estimated error of 2.231 mLkg.sub.-1min.sup.-1. In the VO.sub.2peak estimation model based on CATs and FEV.sub.1 (estimated VO.sub.2peak =8.441- 0.0999x CAT + 0.1000x FEV.sub.1), the variables explained 55% of the variance, with an estimated error of 2.156 mL*kg.sup.-1*min.sup.-1. Conclusion: COPD patients' maximum aerobic capacity has a significant correlation with easily accessible and widely used clinical variables, such as the CATs and FEV.sub.1, which can be used to estimate peak VO.sub.2. Keywords: chronic obstructive pulmonary disease, exercise, oxygen uptake, symptoms
Background
Concurrent aerobic and resistance training (CART) programs have been widely recommended as an important strategy to improve physiologic and functional performance in patients with chronic ...diseases. However, the impact of a personalized CART program in patients with type 2 diabetes (T2D) requires investigation. Therefore, the primary aim of the current study is to investigate the impact of CART programs on metabolic profile, glycemic control, and exercise capacity in patients with diabetes.
Methods
We evaluated 41 subjects with T2D (15 females and 19 males, 50.8 ± 7 years); subjects were randomized into two groups; sedentary (SG) and CART (CART-G). CART was performed over 1.10-h sessions (30-min aerobic and 30-min resistance exercises) three times/week for 12 weeks. Body composition, biochemical analyses, peripheral muscular strength, and cardiopulmonary exercise testing were primary measurements.
Results
The glycated hemoglobin HbA1c (65.4 ± 17.9 to 55.9 ± 12.7 mmol/mol), cholesterol (198.38.1 ± 50.3 to 186.8 ± 35.1 mg/dl), and homeostasis model assessment insulin resistance (HOMA-IR) (6.4 ± 6.8 to 5.0 ± 1.4) decreased in the CART-G compared to the SG. Although body weight did not significantly change after training, skinfold measurement indicated decreased body fat in the CART-G only. CART significantly enhanced muscle strength compared to the SG (
p
< 0.05). CART was also associated with significant increase in peak oxygen uptake and maximal workload compared to the SG (
p
< 0.05).
Conclusions
These data support CART as an important strategy in the treatment of patients with T2D, producing both physiologic and functional improvements.
Trial Registration
Ensaiosclinicos.gov.br,
RBR492q8z
•Development of personalized clinical models for cardiovascular risk assessment.•Flexible framework: information fusion, groups of patients, new clinical knowledge.•Clinical platform integrating: ...patient data, ECG acquisition, developed algorithms.•Validation based on real patient datasets.•Promising results when compared with current CVD risk assessment tools.
The CardioRisk project addresses the coronary artery disease (CAD), namely, the management of myocardial infarction (MI) patients. The main goal is the development of personalized clinical models for cardiovascular (CV) risk assessment of acute events (e.g., death and new hospitalization), in order to stratify patients according to their care needs. This paper presents an overview of the scientific and technological issues that are under research and development.
Three major scientific challenges can be identified: (i) the development of fusion approaches to merge CV risk assessment tools; (ii) strategies for the grouping (clustering) of patients; (iii) biosignal processing techniques to achieve personalized diagnosis. At the end of the project, a set of algorithms/models must properly address these three challenges.
Additionally, a clinical platform was implemented, integrating the developed models and algorithms. This platform supports a clinical observational study (100 patients) that is being carried out in Leiria Hospital Centre to validate the developed approach. Inputs from the hospital information system (demographics, biomarkers, clinical exams) are considered as well as an ECG signal acquired based on a Holter device.
A real patient dataset provided by Santa Cruz Hospital, Portugal, comprising N=460 ACS-NSTEMI patients is also applied to perform initial validations (individual algorithms).
The CardioRisk team is composed by two research institutions, the University of Coimbra (Portugal), Politecnico di Milano (Italy) and Leiria Hospital Centre (a Portuguese public hospital).
Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to ...investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-linearities, non-linear methods might provide better insight. HRV complexity was hereby analyzed during wakefulness and different sleep stages in healthy and obese subjects. Given the short duration of each sleep stage, complexity measures, normally extracted from long-period signals, needed be calculated on short-term signals. Sample entropy, Lempel-Ziv complexity and detrended fluctuation analysis were evaluated and results showed no significant differences among the values calculated over ten-minute signals and longer durations, confirming the reliability of such analysis when performed on short-term signals. Complexity parameters were extracted from ten-minute signal portions selected during wakefulness and different sleep stages on HRV signals obtained from eighteen obese patients and twenty controls. The obese group presented significantly reduced complexity during light and deep sleep, suggesting a deficiency in the control mechanisms integration during these sleep stages. To our knowledge, this study reports for the first time on how the HRV complexity changes in obesity during wakefulness and sleep. Further investigation is needed to quantify altered HRV impact on cardiovascular mortality in obesity.
Identification of CVD risk parameters during sleep Bianchi, Anna M.; Henriques, Jorge; Italiano Monteiro, Clara ...
2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI),
02/2016
Conference Proceeding, Journal Article
Peer reviewed
Parameters obtained from the heart rate variability (HRV) signal have good prognostic value in the cardiovascular disease (CVD), thus can cover a relevant role in the estimation of the risk ...stratification, especially when they are associated to other clinical and demographic data. In the view of home monitoring of CVD patients, the possibility of using signals recorded only during night may greatly reduce the impact on the patient's daily life. In this paper, we want to discuss if the HRV parameters recorded only during the night are sufficient for estimating the CVD risk. In addition, we will discuss a possible procedure for the automatic calculation of the HRV parameters without the need of specialized personnel.
Reduced ejection fraction (EF), possibly induced/mediated by autonomic abnormal activation, is one of the most powerful predictors of adverse outcome after acute myocardial infarction (MI). A deep ...understanding of the correlation between the autonomous functionality and the left ventricular performance in these patients is therefore of paramount importance. The autonomous function is reflected in the cardiac activity and, specifically, in the heart rate variability (HRV) signal. Given the cardiac activity nonlinearity, growing interest is being manifested towards nonlinear methods of analysis, which might provide more significant information than the traditional linear approaches. The aim of the present study was to investigate if non-linear HRV metrics change between MI patients with preserved EF (pEF) and MI patients with reduced EF (rEF). Data were acquired in the context of the cardioRisk project. Ten MI patients with rEF and six MI patients with pEF, admitted to Intensive Cardiac Care after a first acute MI episode, were studied. The ECG was acquired during a Holter recording and the tachogram was extracted. Sample entropy (SE) and Lempel-Ziv Complexity (LZC 1 and LZC 2) metrics were computed on five hour long tachogram portions. A significant correlation was found between LZC indices and EF in the whole population; SE, LZC 1 and LZC 2 were significantly higher in patients with pEF. Our results indicate that lower complexity characterizes the HRV of MI patients with rEF. Complexity reduction might be due to a simplification of regulatory mechanisms, which might explain why MI patients with rEF are at higher risk for subsequent non-fatal and fatal events.
This work briefly describes a Matlab tool originally developed in the HeartCycle1 European project and updated during the cardioRisk project. It addresses the analysis of the electrocardiogram (ECG) ...signal in the context of the management of heart failure (HF) patients. The toolbox is composed of six modules, focusing on the major clinical aspects relevant to HF diagnosis: signal delineation, detection of auricular and ventricular arrhythmias, ST segment deviation and heart rate variability analysis. The last module was the main focus of the cardioRisk project.
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