Hemodynamic response in one session of strength exercise with and without electrostimulation in heart failure patients: A randomized controlled trial

Background: Studies have investigated the influence of neuromuscular electrostimulation on the exercise/muscle capacity of patients with heart failure (HF), but the hemodynamic overload has never been investigated. The aim of our study was to evaluate the heart rate (HR), systolic and diastolic blood pressures in one session of strength exercises with and without neuromuscular electrostimulation (quadriceps) in HF patients and in healthy subjects. Methods: Ten (50% male) HF patients and healthy subjects performed three sets of eight repetitions with and without neuromuscular electrostimulation randomly, with one week between sessions. Throughout, electromyography was performed to guarantee the electrostimulation was effective. The hemodynamic variables were measured at rest, again immediately after the end of each set of exercises, and during the recovery period. Results: Systolic and diastolic blood pressures did not change during each set of exercises among either the HF patients or the controls. Without electrostimulation: among the controls, the HR corresponding to the first (85 &#177; 13 bpm, p = 0.002), second (84 &#177; 10 bpm, p < 0.001), third (89 &#177; 17, p < 0.001) sets and recuperation (83 &#177; 16 bpm, p = 0.012) were different compared to the resting HR (77 bpm). Moreover, the recuperation was different to the third set (0.018). Among HF patients, the HR corresponding to the first (84 &#177; 9 bpm, p = 0.041) and third (84 &#177; 10 bpm, p = 0.036) sets were different compared to the resting HR (80 &#177; 7 bpm), but this increase of 4 bpm is clinically irrelevant to HF. With electrostimulation: among the controls, the HR corresponding to the third set (84 &#177; 9 bpm) was different compared to the resting HR (80 &#177; 7 bmp, p = 0.016). Among HF patients, there were no statistical differences between the sets. The procedure was well tolerated and no subjects reported muscle pain after 24 hours. Conclusions: One session of strength exercises with and without neuromuscular electrostimulation does not promote a hemodynamic overload in HF patients. (Cardiol J 2011; 18, 1: 39-46

Age-Related Maximum Heart Rate Among Ischemic and Nonischemic Heart Failure Patients Receiving beta-Blockade Therapy

Background: Equations to predict maximum heart rate (HRmax) in heart failure (HF) patients receiving beta-adrenergic blocking (BB) agents do not consider the cause of HF. We determined equations to predict HRmax in patients with ischemic and nonischemic HF receiving BB therapy. Methods and Results: Using treadmill cardiopulmonary exercise testing, we studied HF patients receiving BB therapy being considered for transplantation from 1999 to 2010. Exclusions were pacemaker and/or implantable defibrillator, left ventricle ejection fraction (LVEF) &gt;50%, peak respiratory exchange ratio (RER) &lt;1.00, and Chagas disease. We used linear regression equations to predict HRmax based on age in ischemic and nonischemic patients. We analyzed 278 patients, aged 47 +/- 10 years, with ischemic (n = 75) and nonischemic (n = 203) HF. LVEF was 30.8 +/- 9.4% and 28.6 +/- 8.2% (P = .04), peak VO2 16.9 +/- 4.7 and 16.9 +/- 5.2 mL kg(-1) min(-1) (P = NS), and the HRmax 130.8 +/- 23.3 and 125.3 +/- 25.3 beats/min (P = .051) in ischemic and nonischemic patients, respectively. We devised the equation HRmax = 168 - 0.76 x age (R-2 = 0.095; P = .007) for ischemic HF patients, but there was no significant relationship between age and HRmax in nonischemic HF patients (R-2 = 0.006; P = NS). Conclusions: Our study suggests that equations to estimate HRmax should consider the cause of HF. (J Cardiac Fail 2012;18:831-836)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2006/03910-4]Conselho Nacional de Pesquisa (CNPq) [304733/2008-3, 141272/2012-0

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost