Rationale and methods of the multicenter randomised trial of a heart failure management programme among geriatric patients (HF-Geriatrics)

<p>Abstract</p> <p>Background</p> <p>Disease management programmes (DMPs) have been shown to reduce hospital readmissions and mortality in adults with heart failure (HF), but their effectiveness in elderly patients or in those with major comorbidity is unknown. The Multicenter Randomised Trial of a Heart Failure Management Programme among Geriatric Patients (HF-Geriatrics) assesses the effectiveness of a DMP in elderly patients with HF and major comorbidity.</p> <p>Methods/Design</p> <p>Clinical trial in 700 patients aged ≥ 75 years admitted with a primary diagnosis of HF in the acute care unit of eight geriatric services in Spain. Each patient should meet at least one of the following comorbidty criteria: Charlson index ≥ 3, dependence in ≥ 2 activities of daily living, treatment with ≥ 5 drugs, active treatment for ≥ 3 diseases, recent emergency hospitalization, severe visual or hearing loss, cognitive impairment, Parkinson's disease, diabetes mellitus, chronic obstructive pulmonary disease (COPD), anaemia, or constitutional syndrome. Half of the patients will be randomly assigned to a 1-year DMP led by a case manager and the other half to usual care. The DMP consists of an educational programme for patients and caregivers on the management of HF, COPD (knowledge of the disease, smoking cessation, immunizations, use of inhaled medication, recognition of exacerbations), diabetes (knowledge of the disease, symptoms of hyperglycaemia and hypoglycaemia, self-adjustment of insulin, foot care) and depression (knowledge of the disease, diagnosis and treatment). It also includes close monitoring of the symptoms of decompensation and optimisation of treatment compliance. The main outcome variables are quality of life, hospital readmissions, and overall mortality during a 12-month follow-up.</p> <p>Discussion</p> <p>The physiological changes, lower life expectancy, comorbidity and low health literacy associated with aging may influence the effectiveness of DMPs in HF. The HF-Geriatrics study will provide direct evidence on the effect of a DMP in elderly patients with HF and high comorbidty, and will reduce the need to extrapolate the results of clinical trials in adults to elderly patients.</p> <p>Trial registration</p> <p>(ClinicalTrials.gov number, <a href="http://www.clinicaltrials.gov/ct2/show/NCT01076465">NCT01076465</a>).</p

Plant trait and vegetation data along a 1314 m elevation gradient with fire history in Puna grasslands, Per\ufa

\ua9 2024. The Author(s). Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families

Modeling Pathologies of Diastolic and Systolic Heart Failure

International audienceChronic heart failure is a medical condition that involves structural and functional changes of the heart and a progressive reduction in cardiac output. Heart failure is classified into two categories: diastolic heart failure, a thickening of the ventricular wall associated with impaired filling; and systolic heart failure, a dilation of the ventricles associated with reduced pump function. In theory, the pathophysiology of heart failure is well understood. In practice, however, heart failure is highly sensitive to cardiac microstructure, geometry, and loading. This makes it virtually impossible to predict the time line of heart failure for a diseased individual. Here we show that computational modeling allows us to integrate knowledge from different scales to create an individualized model for cardiac growth and remodeling during chronic heart failure. Our model naturally connects molecular events of parallel and serial sarcomere deposition with cellular phenomena of myofibrillogenesis and sarcomerogenesis to whole organ function. Our simulations predict chronic alterations in wall thickness, chamber size, and cardiac geometry, which agree favorably with the clinical observations in patients with diastolic and systolic heart failure. In contrast to existing single- or bi-ventricular models, our new four-chamber model can also predict characteristic secondary effects including papillary muscle dislocation, annular dilation, regurgitant flow, and outflow obstruction. Our prototype study suggests that computational modeling provides a patient-specific window into the progression of heart failure with a view towards personalized treatment planning